#google maps api key

[mostlysignssomeportents]

Too big to care

I'm on tour with my new, nationally bestselling novel The Bezzle! Catch me in BOSTON with Randall "XKCD" Munroe (Apr 11), then PROVIDENCE (Apr 12), and beyond!

Remember the first time you used Google search? It was like magic. After years of progressively worsening search quality from Altavista and Yahoo, Google was literally stunning, a gateway to the very best things on the internet.

Today, Google has a 90% search market-share. They got it the hard way: they cheated. Google spends tens of billions of dollars on payola in order to ensure that they are the default search engine behind every search box you encounter on every device, every service and every website:

https://pluralistic.net/2023/10/03/not-feeling-lucky/#fundamental-laws-of-economics

Not coincidentally, Google's search is getting progressively, monotonically worse. It is a cesspool of botshit, spam, scams, and nonsense. Important resources that I never bothered to bookmark because I could find them with a quick Google search no longer show up in the first ten screens of results:

https://pluralistic.net/2024/02/21/im-feeling-unlucky/#not-up-to-the-task

Even after all that payola, Google is still absurdly profitable. They have so much money, they were able to do a $80 billion stock buyback. Just a few months later, Google fired 12,000 skilled technical workers. Essentially, Google is saying that they don't need to spend money on quality, because we're all locked into using Google search. It's cheaper to buy the default search box everywhere in the world than it is to make a product that is so good that even if we tried another search engine, we'd still prefer Google.

This is enshittification. Google is shifting value away from end users (searchers) and business customers (advertisers, publishers and merchants) to itself:

https://pluralistic.net/2024/03/05/the-map-is-not-the-territory/#apor-locksmith

And here's the thing: there are search engines out there that are so good that if you just try them, you'll get that same feeling you got the first time you tried Google.

When I was in Tucson last month on my book-tour for my new novel The Bezzle, I crashed with my pals Patrick and Teresa Nielsen Hayden. I've know them since I was a teenager (Patrick is my editor).

We were sitting in his living room on our laptops – just like old times! – and Patrick asked me if I'd tried Kagi, a new search-engine.

Teresa chimed in, extolling the advanced search features, the "lenses" that surfaced specific kinds of resources on the web.

I hadn't even heard of Kagi, but the Nielsen Haydens are among the most effective researchers I know – both in their professional editorial lives and in their many obsessive hobbies. If it was good enough for them…

I tried it. It was magic.

No, seriously. All those things Google couldn't find anymore? Top of the search pile. Queries that generated pages of spam in Google results? Fucking pristine on Kagi – the right answers, over and over again.

That was before I started playing with Kagi's lenses and other bells and whistles, which elevated the search experience from "magic" to sorcerous.

The catch is that Kagi costs money – after 100 queries, they want you to cough up $10/month ($14 for a couple or $20 for a family with up to six accounts, and some kid-specific features):

https://kagi.com/settings?p=billing_plan&plan=family

I immediately bought a family plan. I've been using it for a month. I've basically stopped using Google search altogether.

Kagi just let me get a lot more done, and I assumed that they were some kind of wildly capitalized startup that was running their own crawl and and their own data-centers. But this morning, I read Jason Koebler's 404 Media report on his own experiences using it:

https://www.404media.co/friendship-ended-with-google-now-kagi-is-my-best-friend/

Koebler's piece contained a key detail that I'd somehow missed:

When you search on Kagi, the service makes a series of “anonymized API calls to traditional search indexes like Google, Yandex, Mojeek, and Brave,” as well as a handful of other specialized search engines, Wikimedia Commons, Flickr, etc. Kagi then combines this with its own web index and news index (for news searches) to build the results pages that you see. So, essentially, you are getting some mix of Google search results combined with results from other indexes.

In other words: Kagi is a heavily customized, anonymized front-end to Google.

The implications of this are stunning. It means that Google's enshittified search-results are a choice. Those ad-strewn, sub-Altavista, spam-drowned search pages are a feature, not a bug. Google prefers those results to Kagi, because Google makes more money out of shit than they would out of delivering a good product:

https://www.theverge.com/2024/4/2/24117976/best-printer-2024-home-use-office-use-labels-school-homework

No wonder Google spends a whole-ass Twitter every year to make sure you never try a rival search engine. Bottom line: they ran the numbers and figured out their most profitable course of action is to enshittify their flagship product and bribe their "competitors" like Apple and Samsung so that you never try another search engine and have another one of those magic moments that sent all those Jeeves-askin' Yahooers to Google a quarter-century ago.

One of my favorite TV comedy bits is Lily Tomlin as Ernestine the AT&T operator; Tomlin would do these pitches for the Bell System and end every ad with "We don't care. We don't have to. We're the phone company":

https://snltranscripts.jt.org/76/76aphonecompany.phtml

Speaking of TV comedy: this week saw FTC chair Lina Khan appear on The Daily Show with Jon Stewart. It was amazing:

https://www.youtube.com/watch?v=oaDTiWaYfcM

The coverage of Khan's appearance has focused on Stewart's revelation that when he was doing a show on Apple TV, the company prohibited him from interviewing her (presumably because of her hostility to tech monopolies):

https://www.thebignewsletter.com/p/apple-got-caught-censoring-its-own

But for me, the big moment came when Khan described tech monopolists as "too big to care."

What a phrase!

Since the subprime crisis, we're all familiar with businesses being "too big to fail" and "too big to jail." But "too big to care?" Oof, that got me right in the feels.

Because that's what it feels like to use enshittified Google. That's what it feels like to discover that Kagi – the good search engine – is mostly Google with the weights adjusted to serve users, not shareholders.

Google used to care. They cared because they were worried about competitors and regulators. They cared because their workers made them care:

https://www.vox.com/future-perfect/2019/4/4/18295933/google-cancels-ai-ethics-board

Google doesn't care anymore. They don't have to. They're the search company.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/04/04/teach-me-how-to-shruggie/#kagi

[thesomebodywho]

Working in IT can be pretty wild. Some days you feel like the dumbest dumbass that has ever dumbassed because you had to Google the syntax for a switch-case for the fifth time in the same day.

Then one day your university starts using a service and you find out that the coders making the front-end forgot to turn off source-mapping and you can just snoop around their source code.

It was honestly super validating that after actually sending them an email warning about their source code, a couple of API keys and their staging URLs being exposed, the next day the source mapping was turned off. It kind of felt like telling someone their ass crack was showing and politely asking them to pull their pants up. The other option would have been to start poking around in their staging environment, which I would equate to seeing the exposed ass crack and *unzips benis*. Of course, if you are a white hat hacker you could uncover some big security flaws with this. The unzipping allegory kind of crumbles from this point-of-view...

Anyway, the moral of the story is remember to turn off source mapping. (Also don't use API keys in front-end React code and prefer going through the back-end to use external APIs.)

[govindhtech]

How To Use Llama 3.1 405B FP16 LLM On Google Kubernetes

How to set up and use large open models for multi-host generation AI over GKE

Access to open models is more important than ever for developers as generative AI grows rapidly due to developments in LLMs (Large Language Models). Open models are pre-trained foundational LLMs that are accessible to the general population. Data scientists, machine learning engineers, and application developers already have easy access to open models through platforms like Hugging Face, Kaggle, and Google Cloud’s Vertex AI.

How to use Llama 3.1 405B

Google is announcing today the ability to install and run open models like Llama 3.1 405B FP16 LLM over GKE (Google Kubernetes Engine), as some of these models demand robust infrastructure and deployment capabilities. With 405 billion parameters, Llama 3.1, published by Meta, shows notable gains in general knowledge, reasoning skills, and coding ability. To store and compute 405 billion parameters at FP (floating point) 16 precision, the model needs more than 750GB of GPU RAM for inference. The difficulty of deploying and serving such big models is lessened by the GKE method discussed in this article.

Customer Experience

You may locate the Llama 3.1 LLM as a Google Cloud customer by selecting the Llama 3.1 model tile in Vertex AI Model Garden.

Once the deploy button has been clicked, you can choose the Llama 3.1 405B FP16 model and select GKE.Image credit to Google Cloud

The automatically generated Kubernetes yaml and comprehensive deployment and serving instructions for Llama 3.1 405B FP16 are available on this page.

Deployment and servicing multiple hosts

Llama 3.1 405B FP16 LLM has significant deployment and service problems and demands over 750 GB of GPU memory. The total memory needs are influenced by a number of parameters, including the memory used by model weights, longer sequence length support, and KV (Key-Value) cache storage. Eight H100 Nvidia GPUs with 80 GB of HBM (High-Bandwidth Memory) apiece make up the A3 virtual machines, which are currently the most potent GPU option available on the Google Cloud platform. The only practical way to provide LLMs such as the FP16 Llama 3.1 405B model is to install and serve them across several hosts. To deploy over GKE, Google employs LeaderWorkerSet with Ray and vLLM.

LeaderWorkerSet

A deployment API called LeaderWorkerSet (LWS) was created especially to meet the workload demands of multi-host inference. It makes it easier to shard and run the model across numerous devices on numerous nodes. Built as a Kubernetes deployment API, LWS is compatible with both GPUs and TPUs and is independent of accelerators and the cloud. As shown here, LWS uses the upstream StatefulSet API as its core building piece.

A collection of pods is controlled as a single unit under the LWS architecture. Every pod in this group is given a distinct index between 0 and n-1, with the pod with number 0 being identified as the group leader. Every pod that is part of the group is created simultaneously and has the same lifecycle. At the group level, LWS makes rollout and rolling upgrades easier. For rolling updates, scaling, and mapping to a certain topology for placement, each group is treated as a single unit.

Each group’s upgrade procedure is carried out as a single, cohesive entity, guaranteeing that every pod in the group receives an update at the same time. While topology-aware placement is optional, it is acceptable for all pods in the same group to co-locate in the same topology. With optional all-or-nothing restart support, the group is also handled as a single entity when addressing failures. When enabled, if one pod in the group fails or if one container within any of the pods is restarted, all of the pods in the group will be recreated.

In the LWS framework, a group including a single leader and a group of workers is referred to as a replica. Two templates are supported by LWS: one for the workers and one for the leader. By offering a scale endpoint for HPA, LWS makes it possible to dynamically scale the number of replicas.

Deploying multiple hosts using vLLM and LWS

vLLM is a well-known open source model server that uses pipeline and tensor parallelism to provide multi-node multi-GPU inference. Using Megatron-LM’s tensor parallel technique, vLLM facilitates distributed tensor parallelism. With Ray for multi-node inferencing, vLLM controls the distributed runtime for pipeline parallelism.

By dividing the model horizontally across several GPUs, tensor parallelism makes the tensor parallel size equal to the number of GPUs at each node. It is crucial to remember that this method requires quick network connectivity between the GPUs.

However, pipeline parallelism does not require continuous connection between GPUs and divides the model vertically per layer. This usually equates to the quantity of nodes used for multi-host serving.

In order to support the complete Llama 3.1 405B FP16 paradigm, several parallelism techniques must be combined. To meet the model’s 750 GB memory requirement, two A3 nodes with eight H100 GPUs each will have a combined memory capacity of 1280 GB. Along with supporting lengthy context lengths, this setup will supply the buffer memory required for the key-value (KV) cache. The pipeline parallel size is set to two for this LWS deployment, while the tensor parallel size is set to eight.

In brief

We discussed in this blog how LWS provides you with the necessary features for multi-host serving. This method maximizes price-to-performance ratios and can also be used with smaller models, such as the Llama 3.1 405B FP8, on more affordable devices. Check out its Github to learn more and make direct contributions to LWS, which is open-sourced and has a vibrant community.

You can visit Vertex AI Model Garden to deploy and serve open models via managed Vertex AI backends or GKE DIY (Do It Yourself) clusters, as the Google Cloud Platform assists clients in embracing a gen AI workload. Multi-host deployment and serving is one example of how it aims to provide a flawless customer experience.

Read more on Govindhtech.com

[techfinna]

Top 5 Selling Odoo Modules.

In the dynamic world of business, having the right tools can make all the difference. For Odoo users, certain modules stand out for their ability to enhance data management and operations. To optimize your Odoo implementation and leverage its full potential. 

That's where Odoo ERP can be a life savior for your business. This comprehensive solution integrates various functions into one centralized platform, tailor-made for the digital economy. 

Let’s drive into 5 top selling module that can revolutionize your Odoo experience:

Dashboard Ninja with AI, Odoo Power BI connector, Looker studio connector, Google sheets connector, and Odoo data model.

1. Dashboard Ninja with AI: 

Using this module, Create amazing reports with the powerful and smart Odoo Dashboard ninja app for Odoo. See your business from a 360-degree angle with an interactive, and beautiful dashboard.

Some Key Features:

Real-time streaming Dashboard

Advanced data filter

Create charts from Excel and CSV file

Fluid and flexible layout

Download Dashboards items

This module gives you AI suggestions for improving your operational efficiencies.

2. Odoo Power BI Connector:

This module provides a direct connection between Odoo and Power BI Desktop, a Powerful data visualization tool.

Some Key features:

Secure token-based connection.

Proper schema and data type handling.

Fetch custom tables from Odoo.

Real-time data updates.

With Power BI, you can make informed decisions based on real-time data analysis and visualization.

3. Odoo Data Model: 

The Odoo Data Model is the backbone of the entire system. It defines how your data is stored, structured, and related within the application.

Key Features:

Relations & fields: Developers can easily find relations ( one-to-many, many-to-many and many-to-one) and defining fields (columns) between data tables. 

Object Relational mapping: Odoo ORM allows developers to define models (classes) that map to database tables.

The module allows you to use SQL query extensions and download data in Excel  Sheets.

4. Google Sheet Connector:

This connector bridges the gap between Odoo and Google Sheets.

Some Key features:

Real-time data synchronization and transfer between Odoo and Spreadsheet.

One-time setup, No need to wrestle with API’s.

Transfer multiple tables swiftly.

Helped your team’s workflow by making Odoo data accessible in a sheet format.

5.  Odoo Looker Studio Connector:

Looker studio connector by Techfinna easily integrates Odoo data with Looker, a powerful data analytics and visualization platform.

Some Key Features:

Directly integrate Odoo data to Looker Studio with just a few clicks.

The connector automatically retrieves and maps Odoo table schemas in their native data types.

Manual and scheduled data refresh.

Execute custom SQL queries for selective data fetching.

The Module helped you build detailed reports, and provide deeper business intelligence.

 These  Modules will improve analytics, customization, and reporting. Module setup can significantly enhance your operational efficiency. Let’s embrace these modules and take your Odoo experience to the next level. 

Need Help?

I hope you find the blog helpful. Please share your feedback and suggestions.

For flawless Odoo Connectors, implementation, and services contact us at 

info@techfinna.com Or www.techneith.com  

[mariacallous]

The open internet once seemed inevitable. Now, as global economic woes mount and interest rates climb, the dream of the 2000s feels like it’s on its last legs. After abruptly blocking access to unregistered users at the end of last month, Elon Musk announced unprecedented caps on the number of tweets—600 for those of us who aren’t paying $8 a month—that users can read per day on Twitter. The move follows the platform’s controversial choice to restrict third-party clients back in January.

This wasn’t a standalone event. Reddit announced in April that it would begin charging third-party developers for API calls this month. The Reddit client Apollo would have to pay more than $20 million a year under new pricing, so it closed down, triggering thousands of subreddits to go dark in protest against Reddit’s new policy. The company went ahead with its plan anyway.

Leaders at both companies have blamed this new restrictiveness on AI companies unfairly benefitting from open access to data. Musk has said that Twitter needs rate limits because AI companies are scraping its data to train large language models. Reddit CEO Steve Huffman has cited similar reasons for the company’s decision to lock down its API ahead of a potential IPO this year.

These statements mark a major shift in the rhetoric and business calculus of Silicon Valley. AI serves as a convenient boogeyman, but it is a distraction from a more fundamental pivot in thinking. Whereas open data and protocols were once seen as the critical cornerstone of successful internet business, technology leaders now see these features as a threat to the continued profitability of their platforms.

It wasn’t always this way. The heady days of Web 2.0 were characterized by a celebration of the web as a channel through which data was abundant and widely available. Making data open through an API or some other means was considered a key way to increase a company’s value. Doing so could also help platforms flourish as developers integrated the data into their own apps, users enriched datasets with their own contributions, and fans shared products widely across the web. The rapid success of sites like Google Maps—which made expensive geospatial data widely available to the public for the first time—heralded an era where companies could profit through free, mass dissemination of information.

“Information Wants To Be Free” became a rallying cry. Publisher Tim O’Reilly would champion the idea that business success in Web 2.0 depended on companies “disagreeing with the consensus” and making data widely accessible rather than keeping it private. Kevin Kelly marveled in WIRED in 2005 that “when a company opens its databases to users … [t]he corporation’s data becomes part of the commons and an invitation to participate. People who take advantage of these capabilities are no longer customers; they’re the company’s developers, vendors, skunk works, and fan base.” Investors also perceived the opportunity to generate vast wealth. Google was “most certainly the standard bearer for Web 2.0,” and its wildly profitable model of monetizing free, open data was deeply influential to a whole generation of entrepreneurs and venture capitalists.

Of course, the ideology of Web 2.0 would not have evolved the way it did were it not for the highly unusual macroeconomic conditions of the 2000s and early 2010s. Thanks to historically low interest rates, spending money on speculative ventures was uniquely possible. Financial institutions had the flexibility on their balance sheets to embrace the idea that the internet reversed the normal laws of commercial gravity: It was possible for a company to give away its most valuable data and still get rich quick. In short, a zero interest-rate policy, or ZIRP, subsidized investor risk-taking on the promise that open data would become the fundamental paradigm of many Google-scale companies, not just a handful.

Web 2.0 ideologies normalized much of what we think of as foundational to the web today. User tagging and sharing features, freely syndicated and embeddable links to content, and an ecosystem of third-party apps all have their roots in the commitments made to build an open web. Indeed, one of the reasons that the recent maneuvers of Musk and Huffman seem so shocking is that we have come to expect data will be widely and freely available, and that platforms will be willing to support people that build on it.

But the marriage between the commercial interests of technology companies and the participatory web has always been one of convenience. The global campaign by central banks to curtail inflation through aggressive interest rate hikes changes the fundamental economics of technology. Rather than facing a landscape of investors willing to buy into a hazy dream of the open web, leaders like Musk and Huffman now confront a world where clear returns need to be seen today if not yesterday.

This presages major changes ahead for the design of the internet and the rights of users. Twitter and Reddit are pioneering an approach to platform management (or mismanagement) that will likely spread elsewhere across the web. It will become increasingly difficult to access content without logging in, verifying an identity, or paying a toll. User data will become less exportable and less shareable, and there will be increasingly fewer expectations that it will be preserved. Third-parties that have relied on the free flow of data online—from app-makers to journalists—will find APIs ever more expensive to access and scraping harder than ever before.

We should not let the open web die a quiet death. No doubt much of the foundational rhetoric of Web 2.0 is cringeworthy in the harsh light of 2023. But it is important to remember that the core project of building a participatory web where data can be shared, improved, critiqued, remixed, and widely disseminated by anyone is still genuinely worthwhile.

The way the global economic landscape is shifting right now creates short-sighted incentives toward closure. In response, the open web ought to be enshrined as a matter of law. New regulations that secure rights around the portability of user data, protect the continued accessibility of crucial APIs to third parties, and clarify the long-ambiguous rules surrounding scraping would all help ensure that the promise of a free, dynamic, competitive internet can be preserved in the coming decade.

For too long, advocates for the open web have implicitly relied on naive beliefs that the network is inherently open, or that web companies would serve as unshakable defenders of their stated values. The opening innings of the post-ZIRP world show how broader economic conditions have actually played the larger role in architecting how the internet looks and feels to this point. Believers in a participatory internet need to reach for stronger tools to mitigate the effects of these deep economic shifts, ensuring that openness can continue to be embedded into the spaces that we inhabit online.

WIRED Opinion publishes articles by outside contributors representing a wide range of viewpoints. Read more opinions here. Submit an op-ed at ideas@wired.com.

[wealthview]

Ever planned a trip abroad, excitedly browsing Google Maps for your next adventure, only to discover the prices in unfamiliar currencies? It can be a real headache trying to decipher those numbers, especially when you’re already juggling flights, accommodation, and visa applications. Knowing how to change the currency on Google Maps for your trip, let’s say to Europe for a European adventure or even a domestic trip requires careful budget planning, can simplify things immensely. We’ll solve that problem. Learning how to change the currency on Google Maps in is super useful when you’re on the move and planning in India rupees (INR) or needing US dollars (USD). Get ready — I’ll walk you through this so that planning your trip and viewing travel costs becomes a lot easier. This blog post reveals several methods — it’s easier than you may think!

Understanding Currency Settings in Google Maps

First things first: let’s clarify that Google Maps doesn’t actually convert money on the spot so that it never involves real-time stock, for instance. It doesn’t provide precise exchange rates the moment you change currencies with that function because changes happen daily within the markets and also according to the banks involved. Instead, what it does do is help streamline displaying costs in the currency which best serves you in using cost-related apps, such as showing prices displayed by services that show business locations on the map. Because pricing isn’t actually changing according to rates set at any period of time because exchange rates differ frequently, the system won’t show you the most current prices when converting to a foreign currency using conversion software in-application. Google Maps is designed for maps — and currency management is primarily done elsewhere, such as in exchange calculator, converter sites, currency exchange offices where your information is used legitimately. Think of it as a presentation feature to help ease things by showing the business/product data that already exists which involves currency.

Changing the Currency for All Places (Not Always Consistent!):

Several Google-related services integrate, particularly when traveling internationally, although the information exchanged won’t perfectly coincide across all these services, even during the same operating hours between your search engines, banking apps, stock exchange apps and so on. You may need to take an averaged reading when calculating trips abroad. This does get more difficult when using budgeting apps. It mainly facilitates things for viewing pricing for establishments where location business data is involved by integrating the conversion process according to several places within which services may already have been involved with currency conversions. The accuracy of data when using maps can be inconsistent, although if data does exist from business, Google integrates it to show conversions in various applications where appropriate. The conversion feature operates on an indirect information path, not on a system or banking app for currencies. The key factor is that you control location access/data for the application through privacy, location, apps and also from business data which is available online through several maps and similar search functionalities; it may not operate fully within one single service, but instead has some exchange information which flows using access methods — not strictly inside of Google products/APIs although maps mostly shows these integrated exchanges directly.

Changing Currency for Individual Results:

Even once you’ve set default values elsewhere according to your budget, you may want to display different values on your various location searches, although there should be similar exchanges for individual items showing when you have specific searches showing. This can work sometimes in addition to when you change overall currency features that are mentioned (previously stated sections); these may still differ or overlap, as all services don’t operate fully in tandem in data conversion and there remains some discrepancies among data access services used in converting the value.

Using Other Tools for Precise Conversions:

The results given by map applications do not give conversions according to exchange pricing at any timeframe because stock, values given have other sources of data. Any tool available should use disclaimers on exchange and currency, as the accuracy may be questionable if it doesn’t also state it’s not exactly the up-to-the-minute amount and should also be matched according to banks’ data. To enhance your money calculations accuracy when viewing trip estimates, particularly abroad.

* Online Converter Websites: Tons of reputable sites allow instant currency and some that calculate according to exchange periods of activity according to financial marketplaces available according to financial reporting. (For example: xe.com, a well-know currency converter website). When using any software such as banking, stock or other exchanges it’s crucial also to understand whether these data sources integrate; they could work completely separately, yet be indirectly correlated depending on financial markets when used alongside similar-type applications using a common source. Such currency value results may still vary depending on what system of exchange rates and/or the data service used at this given moment in financial market operations — especially across geographical positions that also experience delayed trading. To get as accurate a reading as possible it also makes sense use some averaging where applicable from many sources if a single application isn’t clear to what time period of value integration it’s involved in according to exchange rates it displays using.

* Banking Apps: For any serious planning your financial institutions banking software provides up-to-the-minute currency tools directly provided by exchanges’ systems. Many are fully real-time for the period on which financial markets are open. Because financial exchanges always incorporate fluctuations and can include unpredictable differences, currency software that helps people prepare needs to maintain legal, clear indications that these values do change often according to currency sources.

Remember, Google Maps focuses on geography, traffic, or even locating your dinner — hence that its role concerning currency is secondary. Always confirm financial details directly also check for currency changes if these are made, otherwise also consider double-checking pricing values outside maps when planning, budgeting trips, where accurate, live calculations may more relevant and are fully available sometimes across various bank systems (in specific ways for some online tools that give you exchanges).

Troubleshooting Currency Displays on Google Maps Issues

Sometimes the settings you expect maps to give may differ in relation between what you already set default using conversion currency applications/apps — since several service APIs don’t completely interact simultaneously but use separate (external) systems and sources (which will contain inherent discrepancies over how live pricing conversions work that may appear due to data flow from separate services for exchange involved or timing). These problems usually involve information displayed by maps that are dependent on other locations, services, businesses within the map or your own, individual-user-linked data according to location-access information you’d set accordingly so you yourself will have differing values, depending, for map related conversions of cost information or according to currency systems set by you and also according to settings in associated or related maps used as references — especially ones already integrating, or also indirectly interacting with certain parts of online, publicly available services (i.e involving businesses having information about which also uses similar mapping services having certain exchange applications as information-based display, showing data conversion results related but not the central API involved).

Check your Phone or System settings.: Double-check what conversion software, default settings which involve currencies are set within different platforms of access because if map display differences among exchanges are arising if these conversion rates differ according to applications. So the actual rate according to services (even banking, conversion apps etc, involved directly by exchanging money may always differ) due to live pricing values according to many exchanges or sources that happen differently in live exchange. Google Maps mainly serves to pull over and display converted data which is available from various business location points, depending on what services used if several ones include exchange tools and maps; usually such business services are the origin of the exchange calculation not Google map’s API itself that show them, sometimes separately or overlapping conversions with the actual monetary amounts involving banks and stock transactions.

Clear Your Map Data/Cache: Sometimes you may have various cached, or outdated map information that cause an issue when used with Google conversion tools available where they may interfere due to a delay and thus may generate differences among currency conversion displays due to this outdated amount interfering with that information, so try restarting devices clearing relevant cache, which can help solve differences.

Try a Separate Maps/Exchange Tool: For an enhanced comparison or for additional reassurance with those results view those prices and converted amounts using a totally distinct application such similar location apps where you can compare the display. Usually you want your comparison using actual financial banking institutions whenever making plans for trips abroad

FAQs: Addressing Your Currency Conversion Questions

Q : Why aren’t displayed Google Map amounts exactly the amounts from a bank app conversions displayed? A: Prices are set according to business places providing locations, that integrate conversions through mapping services — that have an external location data to work. Bank, currency conversions work fully independently also to display exchange and they vary because the live currency pricing updates according to banking processes which happen completely distinctly.

Q : If I’m going to Europe, can set Euro (€) amount? When can I set maps default display currency and can that setting involve all Google apps I use?

A: Your apps in Google don’t always function consistently according to default setting exchanges which happen separately on multiple data exchange and access programs. Try using various tools in order to make better comparisons when estimating your budget because maps give displays primarily using businesses already integrated separately — they may differ depending if Google service updates it with a direct link because they sometimes use other linked application programs externally within this function. This only facilitates displays so it might only be accurate for showing prices as already shown through particular service provided information/services or otherwise using financial markets or stock exchange in calculating according prices — these values may only partly coincide with maps having currency display due that their function exists indirectly only for the latter instance (mainly using display as it is already integrated by related/other services that contain exchanges). Default settings for maps involve displaying information from several services depending how all work that operate mostly as indirect exchanges; you might want extra comparisons, and this function is not for direct conversion or making transaction payments in some service although this may provide useful for comparison, depending what integrated/services work within your exchange if there are many different ones functioning concurrently for making a calculated exchange involving financial values if live ones — which always differ over time anyway regardless. You want accurate and appropriate, real up-to the-second exchange value conversions according currency prices in live market which will only be provided from services explicitly given for live transactions according exchange data for this reason; maps exist purely for facilitating that process within some locations according services giving live data which doesn’t only apply to map alone specifically without extra access for direct integration being available specifically and without any direct live updates according stock operations.

Q : Should I directly use maps’ monetary calculations to budget my budget trip abroad? A: It’s often useful, particularly abroad, check several services and applications. As Google Maps displays information not necessarily according to a fully direct service or real market calculation, and sometimes uses indirect pricing in doing conversions or indirectly from those financial stock exchange values because values change rapidly according markets, it’s useful to make a final exchange estimate via applications specifically for currency using bank services so this makes your exchange and its related calculations even more reliably up to date. Maps mainly integrate conversions but it doesn’t maintain live access for these transactions entirely on its own independently so when comparing always match your various app results as they may partly coincide depending when information was integrated rather exact amount as seen across multiple values changing in market according transactions that are made — thus maps may still have inaccuracies within those amounts (that arise differently due this kind overlap among tools for exchanges that show up this time). Always compare any conversions given by maps with exchange software used in live banking applications that include live market update of costs so calculations will have far accurate currency exchanges compared according maps information which mostly display according prices, rates for businesses involved separately involved with other similar ones; if there multiple ones integrated these all differ that may overlap partially in calculations according maps services thus requiring other exchange applications such as your bank or other financial institution software for matching exchange prices during budget calculations if required.

Do you agree on some key aspects regarding use of Google Map pricing or when planning abroad, is it useful or sufficient — we’d love to encourage some insight because your expertise is helpful here in planning for your trips based on what works in comparing maps with those in the related exchange rates calculations made using other tools available from many source options besides what the primary applications give as it concerns displays and also according to currency.

Share your thoughts and questions from own experiences — and do add some new experiences that helps others as well! Lets all develop even better tips within currency, mapping use or when planning trip abroad.

[cwmorton]

How to Build an AI-Automated Print-on-Demand Shop

How to Build an AI-Automated Print-on-Demand Shop

Welcome to our comprehensive guide on setting up an AI-automated print-on-demand shop! This process utilizes tools like Google Sheets, OpenAI, and Printify to streamline the creation of products from AI-generated art to fully described and tagged items ready for sale. Let's dive into the step-by-step process. Step 1: Prepare Your Google Sheet First, you'll need a Google account. If you don't have one, sign up for a Google business account. Once logged in, go to Google Sheets. You can find this by typing 'Google Sheets' into your browser or accessing it directly if it's bookmarked. Create a new blank sheet and name it something relevant like "AI Automated Print Shop". Here, you will input your AI prompts which are essentially the instructions you give to the AI to generate specific images. For example, in cell A2, you might enter 'abstract art broad brush stroke in vibrant colors'. This prompt will guide the AI in creating artwork. Step 2: Setting Up Your First Module in Make.com Navigate to Make.com, where we'll set up our first module. Click the plus sign to add a new module, search for 'Google Sheets', and select it. You'll need to connect to Google by signing in, which will access your Google account and allow you to choose the spreadsheet you wish to use. Select your spreadsheet by its ID and specify the sheet number (usually 'Sheet1'). Define the cell you want to reference, in this case, A2 for our abstract art prompt, and click 'OK'. This module retrieves data from your Google Sheet. Step 3: Generate Images with OpenAI Next, we create a module for OpenAI to generate images. Search for 'OpenAI' in Make.com, and select the module for generating an image. If it's your first time, you'll need to set up your connection by grabbing your API key from your OpenAI account. After creating a new secret key, copy and paste it into the Make.com module. Also, retrieve your organization ID from your OpenAI settings and paste it. Now map the prompt from your Google Sheets module to the OpenAI module, set the response format to URL, and decide the number of images (one is practical for print-on-demand). Click 'OK' and test the process to ensure it generates an image from your prompt. Step 4: Enhance Image Quality with Image Scaler The generated image might not be high-resolution enough for print products. Use the HTTP module in Make.com, labeled as the 'Swiss Army knife' of modules due to its versatility. Set up a POST request to the DeepAI API for image scaling. Paste the API URL provided, add your API key in the headers, and in the body, map the URL of the image from the OpenAI module. Ensure you're using the upscaled image for better quality. Step 5: Upload Image to Printify Now, we need to upload this image to Printify, our print-on-demand service provider. First, ensure you have a Printify account and generate an API token. In Make.com, add a Printify module for uploading an image. Set up your connection with the API token, name the image using the product title from OpenAI, and map the URL of the upscaled image to this module. This step ensures your high-resolution image is ready for product creation. Step 6: Create Product in Printify With the image uploaded, the next module in Make.com will be to create the product. Choose 'Create Product' under Printify, select your shop, map the title and description from the OpenAI modules, and add any hashtags. Set the blueprint ID for the product type (e.g., canvas gallery wrap), choose the print provider, and set your variants (e.g., 36x36 and 24x24 for different sizes). Price your products appropriately, remembering to format the price correctly without decimal points. Add print areas, specifying 'front' for placement, and ensure the image ID maps to the uploaded image in Printify. Set print details like edge options for canvas art. Step 7: Automate and Schedule the Process After setting up all modules, you can automate the entire process. In Make.com, you can schedule how often this process runs, whether it's every few minutes for initially populating your store or weekly for regular updates. This automation ensures your shop is constantly updated with new products without manual intervention. Step 8: Review and Publish Before going live, review the products generated by the AI. Since AI isn't perfect, checking descriptions, titles, and hashtags is crucial. You can choose to publish them manually after this review or automate the publishing part if you trust the AI's curation. Remember, in the beginning, frequent checks might be necessary to refine the system. By following these steps, you've now built an AI-automated print-on-demand shop. This setup can be expanded to include different products like t-shirts in future processes. Keep refining your prompts and parameters to get the best results, and stay tuned for more advanced tutorials on expanding your product line. That's it for this guide on creating an automated print-on-demand shop with AI. Experiment with different prompts, refine your processes, and watch your business grow with minimal effort. Happy automating!

[informaticacloudtraining1]

Best Informatica Cloud Training in India | Informatica IICS

Cloud Data Integration (CDI) in Informatica IICS

Introduction

Cloud Data Integration (CDI) in Informatica Intelligent Cloud Services (IICS) is a powerful solution that helps organizations efficiently manage, process, and transform data across hybrid and multi-cloud environments. CDI plays a crucial role in modern ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) operations, enabling businesses to achieve high-performance data processing with minimal complexity. In today’s data-driven world, businesses need seamless integration between various data sources, applications, and cloud platforms.  Informatica Training Online

What is Cloud Data Integration (CDI)?

Cloud Data Integration (CDI) is a Software-as-a-Service (SaaS) solution within Informatica IICS that allows users to integrate, transform, and move data across cloud and on-premises systems. CDI provides a low-code/no-code interface, making it accessible for both technical and non-technical users to build complex data pipelines without extensive programming knowledge.

Key Features of CDI in Informatica IICS

Cloud-Native Architecture

CDI is designed to run natively on the cloud, offering scalability, flexibility, and reliability across various cloud platforms like AWS, Azure, and Google Cloud.

Prebuilt Connectors

It provides out-of-the-box connectors for SaaS applications, databases, data warehouses, and enterprise applications such as Salesforce, SAP, Snowflake, and Microsoft Azure.

ETL and ELT Capabilities

Supports ETL for structured data transformation before loading and ELT for transforming data after loading into cloud storage or data warehouses.

Data Quality and Governance

Ensures high data accuracy and compliance with built-in data cleansing, validation, and profiling features. Informatica IICS Training

High Performance and Scalability

CDI optimizes data processing with parallel execution, pushdown optimization, and serverless computing to enhance performance.

AI-Powered Automation

Integrated Informatica CLAIRE, an AI-driven metadata intelligence engine, automates data mapping, lineage tracking, and error detection.

Benefits of Using CDI in Informatica IICS

1. Faster Time to Insights

CDI enables businesses to integrate and analyze data quickly, helping data analysts and business teams make informed decisions in real-time.

2. Cost-Effective Data Integration

With its serverless architecture, businesses can eliminate on-premise infrastructure costs, reducing Total Cost of Ownership (TCO) while ensuring high availability and security.

3. Seamless Hybrid and Multi-Cloud Integration

CDI supports hybrid and multi-cloud environments, ensuring smooth data flow between on-premises systems and various cloud providers without performance issues. Informatica Cloud Training

4. No-Code/Low-Code Development

Organizations can build and deploy data pipelines using a drag-and-drop interface, reducing dependency on specialized developers and improving productivity.

5. Enhanced Security and Compliance

Informatica ensures data encryption, role-based access control (RBAC), and compliance with GDPR, CCPA, and HIPAA standards, ensuring data integrity and security.

Use Cases of CDI in Informatica IICS

1. Cloud Data Warehousing

Companies migrating to cloud-based data warehouses like Snowflake, Amazon Redshift, or Google BigQuery can use CDI for seamless data movement and transformation.

2. Real-Time Data Integration

CDI supports real-time data streaming, enabling enterprises to process data from IoT devices, social media, and APIs in real-time.

3. SaaS Application Integration

Businesses using applications like Salesforce, Workday, and SAP can integrate and synchronize data across platforms to maintain data consistency. IICS Online Training

4. Big Data and AI/ML Workloads

CDI helps enterprises prepare clean and structured datasets for AI/ML model training by automating data ingestion and transformation.

Conclusion

Cloud Data Integration (CDI) in Informatica IICS is a game-changer for enterprises looking to modernize their data integration strategies. CDI empowers businesses to achieve seamless data connectivity across multiple platforms with its cloud-native architecture, advanced automation, AI-powered data transformation, and high scalability. Whether you’re migrating data to the cloud, integrating SaaS applications, or building real-time analytics pipelines, Informatica CDI offers a robust and efficient solution to streamline your data workflows.

For organizations seeking to accelerate digital transformation, adopting Informatics’ Cloud Data Integration (CDI) solution is a strategic step toward achieving agility, cost efficiency, and data-driven innovation.

 For More Information about Informatica Cloud Online Training

Contact Call/WhatsApp:  +91 7032290546

Visit: https://www.visualpath.in/informatica-cloud-training-in-hyderabad.html

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How Much Does It Cost to Develop a Taxi App in India?

The on-demand taxi industry in India has seen tremendous growth, with apps like Uber and Ola transforming urban transportation. Many entrepreneurs and businesses are now looking to launch their own taxi booking apps, but one of the most common concerns is the cost of development. The price varies depending on factors such as app complexity, features, technology stack, and the expertise of a taxi app development company in India. To better understand the costs involved, let’s break down the key elements that influence taxi app development expenses.

1. Factors Affecting the Cost of Taxi App Development

Several aspects determine how much you will need to invest in a taxi app. The more advanced the app, the higher the cost.

App Type and Complexity

The cost of development depends on whether you are building a basic, mid-range, or advanced taxi app. A simple taxi app with standard ride-booking and payment features costs less, while a feature-rich app with AI-driven route optimization, real-time analytics, and multiple payment gateways will require a bigger investment.

Features and Functionalities

The number and type of features included in your app play a major role in determining the overall cost. Essential features include user registration, real-time ride booking and tracking, fare calculation, secure payment integration, driver profiles, ride history, push notifications, and an admin dashboard. If you want to integrate premium features such as AI-based ride matching, surge pricing, and blockchain-based payments, development costs will increase.

Technology Stack

A well-built taxi app relies on a strong technology stack. A taxi app development company in India typically uses programming languages like React Native or Flutter for the front end, and Node.js, Python, or Java for the back end. Cloud services such as AWS or Google Cloud are used for data storage, and Google Maps API ensures real-time location tracking. The complexity of the tech stack impacts the cost significantly.

App Development Team and Location

Hiring a professional development team in India is more cost-effective compared to countries like the US or UK. A standard team consists of a project manager, UI/UX designer, front-end and back-end developers, and a quality assurance engineer. The more experienced the team, the higher the charges, but this ensures a more stable and scalable app.

2. Estimated Cost of Developing a Taxi App in India

The cost of taxi app development varies based on complexity. A basic app with standard booking features may cost between ₹5-10 lakhs ($6,000 - $12,000) and take around three to four months to develop. A mid-level app with additional features like real-time fare estimation, in-app chat, and an admin dashboard may cost between ₹10-20 lakhs ($12,000 - $25,000), with a development time of four to six months. For a high-end taxi app with AI-driven ride suggestions, surge pricing, multi-currency support, and advanced analytics, the cost can range between ₹20-50 lakhs ($25,000 - $60,000), requiring six to twelve months for completion.

3. How to Choose the Right Taxi App Development Company in India?

Selecting a professional taxi app development company in India is critical to the success of your app. It is important to evaluate their experience, portfolio, and past projects to ensure they can deliver high-quality work. The company should have expertise in the latest technologies, be able to customize solutions based on your business requirements, and provide post-launch support for app maintenance and updates. A good development partner will also ensure that the app is scalable and future-ready.

4. Additional Costs to Consider

Beyond development, there are other expenses involved in launching a taxi app. App Store and Play Store fees are mandatory, with Apple charging $99 per year and Google requiring a one-time fee of $25. Hosting and server maintenance costs may range from ₹10,000 to ₹50,000 per month, depending on app traffic and data storage needs. Marketing and promotional activities, including social media ads, SEO, and referral programs, may require an initial investment of ₹2-5 lakhs to acquire users and build brand awareness.

Conclusion

Developing a taxi app in India requires careful planning and investment in the right technology and features. The total cost depends on app complexity, features, and the development team’s expertise. By partnering with a reputable taxi app development company in India, businesses can ensure a seamless, high-performing, and scalable app that meets industry standards. With the right approach and strategy, launching a taxi app can be a highly profitable venture in India’s growing ride-hailing market.

For more information, visit us: -

Fantasy Cricket App Development Company in India

Ecommerce Website Development Company

Real Estate App Development Company

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API Integration in Web Development: Connecting Your Site to External Services

If you’ve ever used a weather widget on a travel site or paid through PayPal on an online store, you’ve seen APIs in action. APIs (Application Programming Interfaces) let your website “talk” to other services, adding features without building everything from scratch. For businesses working with the best web development agencies in Odisha, mastering API integration can take your site’s functionality to the next level. Let’s explore how it works and why it matters.

What’s an API, Anyway?

Think of an API like a restaurant menu. You don’t need to know how the kitchen prepares your meal—you just order what you want, and the server brings it to you. Similarly, APIs let your website request specific data or actions from external platforms (like Google Maps or payment gateways) and receive a ready-to-use response.

Why Integrate APIs?

APIs save time, reduce costs, and add features that would otherwise take months to create. For example:

Payment Processing: Integrate Stripe or Razorpay to handle secure transactions.

Social Media Sharing: Let users share content on Facebook or Twitter with one click.

Real-Time Data: Show live weather updates, currency rates, or shipping tracking.

Authentication: Allow sign-ins via Google or Facebook.

Even the best web development agencies Odisha rely on APIs to deliver efficient, feature-rich sites.

How to Integrate APIs: A Step-by-Step Approach

1. Choose the Right API

Not all APIs are created equal. Look for:

Clear Documentation: Instructions should be easy to follow.

Reliability: Check uptime stats and user reviews.

Cost: Some APIs charge fees based on usage.

Popular options include Google Maps API (for location services), Twilio (for SMS), and OpenAI (for AI tools).

2. Get API Credentials

Most APIs require keys or tokens to authenticate requests. These act like passwords, ensuring only authorized users access the service. Store these keys securely—never expose them in public code.

3. Make API Requests

APIs work through HTTP requests (like GET or POST). For example, to fetch weather data, your site might send a GET request to a weather service’s API endpoint with parameters like location and date.

4. Handle Responses

APIs return data in formats like JSON or XML. Your site needs to process this data and display it user-friendly. For instance, converting raw latitude/longitude coordinates into an interactive map.

5. Test Thoroughly

Check how your site handles API errors, slow responses, or downtime. Plan fallbacks—like showing cached data if an API fails—to keep the user experience smooth.

Common Challenges (and How to Solve Them)

Rate Limits: Many APIs restrict how many requests you can make per minute. Avoid hitting limits by caching frequent responses or optimizing request frequency.

Data Security: Always use HTTPS for API calls to encrypt data. Avoid sending sensitive info (like API keys) in URLs.

Version Changes: APIs update over time. Regularly check for deprecated features and update your code to avoid breaking your site.

Best Practices for Smooth Integration

Use Libraries or SDKs Many APIs provide pre-built code libraries (SDKs) to simplify integration. These handle authentication and data formatting, saving you time.

Monitor Performance Track how APIs affect your site’s speed. Slow responses can frustrate users, so optimize code or switch providers if needed.

Document Your Work Keep notes on how APIs are used, where keys are stored, and error-handling processes. This helps future developers (or your team) troubleshoot quickly.

Stay Legal Respect API terms of service. For example, don’t scrape data if the API prohibits it, and credit sources where required.

Real-World Examples

E-Commerce Sites: Use shipping APIs like FedEx to calculate delivery costs in real time.

Travel Portals: Pull flight and hotel availability from services like Amadeus.

Healthcare Apps: Integrate telemedicine APIs for video consultations.

When to Ask for Help

API integration can get tricky, especially with complex systems or strict security needs. Partnering with experienced developers, like the best web development agencies Odisha, ensures your integrations are secure, efficient, and scalable. They’ll handle the technical heavy lifting so you can focus on your business.

[learning-code-ficusoft]

Using Azure Data Factory with Azure Synapse Analytics

Using Azure Data Factory with Azure Synapse Analytics 

Introduction

 Azure Data Factory (ADF) and Azure Synapse Analytics are two powerful cloud-based services from Microsoft that enable seamless data integration, transformation, and analytics at scale. 

ADF serves as an ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) orchestration tool, while Azure Synapse provides a robust data warehousing and analytics platform. 

By integrating ADF with Azure Synapse Analytics, businesses can build automated, scalable, and secure data pipelines that support real-time analytics, business intelligence, and machine learning workloads. 

Why Use Azure Data Factory with Azure Synapse Analytics?

 1. Unified Data Integration & Analytics

 ADF provides a no-code/low-code environment to move and transform data before storing it in Synapse, which then enables powerful analytics and reporting.

2. Support for a Variety of Data Sources

 ADF can ingest data from over 90+ native connectors, including: On-premises databases (SQL Server, Oracle, MySQL, etc.) Cloud storage (Azure Blob Storage, Amazon S3, Google Cloud Storage) APIs, Web Services, and third-party applications (SAP, Salesforce, etc.) 

3. Serverless and Scalable Processing With Azure Synapse, users can choose between: 

Dedicated SQL Pools (Provisioned resources for high-performance querying) Serverless SQL Pools (On-demand processing with pay-as-you-go pricing)

 4. Automated Data Workflows ADF allows users to design workflows that automatically fetch, transform, and load data into Synapse without manual intervention. 

5. Security & Compliance Both services provide enterprise-grade security, including: Managed Identities for authentication Role-based access control (RBAC) for data governance Data encryption using Azure Key Vault 

Key Use Cases 

Ingesting Data into Azure Synapse ADF serves as a powerful ingestion engine for structured, semi-structured, and unstructured data sources. 

Examples include: Batch Data Loading: Move large datasets from on-prem or cloud storage into Synapse. 

Incremental Data Load: Sync only new or changed data to improve efficiency. 

Streaming Data Processing: Ingest real-time data from services like Azure Event Hubs or IoT Hub. 

2. Data Transformation & Cleansing ADF provides two primary ways to transform data: Mapping Data Flows: A visual, code-free way to clean and transform data. 

Stored Procedures & SQL Scripts in Synapse: Perform complex transformations using SQL. 

3. Building ETL/ELT Pipelines ADF allows businesses to design automated workflows that: Extract data from various sources Transform data using Data Flows or SQL queries Load structured data into Synapse tables for analytics 

4. Real-Time Analytics & Business Intelligence ADF can integrate with Power BI, enabling real-time dashboarding and reporting. 

Synapse supports Machine Learning models for predictive analytics. How to Integrate Azure Data Factory with Azure Synapse Analytics Step 1: Create an Azure Data Factory Instance Sign in to the Azure portal and create a new Data Factory instance. 

Choose the region and resource group for deployment. 

Step 2: Connect ADF to Data Sources Use Linked Services to establish connections to storage accounts, databases, APIs, and SaaS applications. 

Example: Connect ADF to an Azure Blob Storage account to fetch raw data. 

Step 3: Create Data Pipelines in ADF Use Copy Activity to move data into Synapse tables. Configure Triggers to automate pipeline execution. 

Step 4: Transform Data Before Loading Use Mapping Data Flows for complex transformations like joins, aggregations, and filtering. Alternatively, perform ELT by loading raw data into Synapse and running SQL scripts. 

Step 5: Load Transformed Data into Synapse Analytics Store data in Dedicated SQL Pools or Serverless SQL Pools depending on your use case. 

Step 6: Monitor & Optimize Pipelines Use ADF Monitoring to track pipeline execution and troubleshoot failures. Enable Performance Tuning in Synapse by optimizing indexes and partitions. 

Best Practices for Using ADF with Azure Synapse Analytics 

Use Incremental Loads for Efficiency Instead of copying entire datasets, use delta processing to transfer only new or modified records. 

Leverage Watermark Columns or Change Data Capture (CDC) for incremental loads. 

2. Optimize Performance in Data Flows Use Partitioning Strategies to parallelize data processing. Minimize Data Movement by filtering records at the source.

3. Secure Data Pipelines Use Managed Identity Authentication instead of hardcoded credentials. Enable Private Link to restrict data movement to the internal Azure network.

 4. Automate Error Handling Implement Retry Policies in ADF pipelines for transient failures. Set up Alerts & Logging for real-time error tracking. 

5. Leverage Cost Optimization Strategies Choose Serverless SQL Pools for ad-hoc querying to avoid unnecessary provisioning. 

Use Data Lifecycle Policies to move old data to cheaper storage tiers. Conclusion Azure Data Factory and Azure Synapse Analytics together create a powerful, scalable, and cost-effective solution for enterprise data integration, transformation, and analytics. 

ADF simplifies data movement, while Synapse offers advanced querying and analytics capabilities. 

By following best practices and leveraging automation, businesses can build efficient ETL pipelines that power real-time insights and decision-making.

WEBSITE: https://www.ficusoft.in/azure-data-factory-training-in-chennai/

[sophiasmithg]

9 Top Python Frameworks for App Development (+Use Cases)

Explore here a list of Top 10 Python App Frameworks to Use in 2025:

1-Django

Django is a leading Python framework designed for building dynamic mobile and web applications with ease. It leverages a robust Object-Relational Mapping (ORM) system and follows the Model-View-Controller (MVC) pattern, ensuring clean, reusable, and easily maintainable code.

Whether you’re creating simple apps or scaling complex projects, Django’s powerful features make development faster and more efficient.

It has built-in tools like URL routing/parsing, authentication system, form validation, template engine, and caching to ensure a swift development process.

Django follows the DRY (Don’t Repeat Yourself) concept and focuses on rapid app development with a neat design.

This framework is the first choice of developers for any Python project due to its versatility, customization, scalability, deployment speed, simplicity, and compatibility with the latest Python versions.

According to a Stack Overflow survey, Django and Flask are the most popular Python software development frameworks.

Some examples popular examples of apps built with the Django framework are Instagram and Spotify.

Key Features of Django Framework:

Enables execution of automated migrations

Robust security

Enhanced web server support

Comprehensive documentation

Vast add-ins with SEO optimization

2-Flask

Flask stands out as a top-rated, open-source Python microframework known for its simplicity and efficiency. The Flask framework comes packed with features like a built-in development server, an intuitive debugger, seamless HTTP request handling, file storage capabilities, and robust client-side session support.

It has a modular and adaptable design and added compatibility with Google App Engine.

Besides Django, Flask is another popular Python framework with the Werkzeug WSGI toolkit and Jinja2 template.

Flask operates under the BSD license, ensuring simplicity and freedom for developers.

Inspired by the popular Sinatra Ruby framework, Flask combines minimalism with powerful capabilities, making it a go-to choice for building scalable and efficient web applications.

Key Features of Flask Framework:

Jinja2 templating and WSGI compliance

Unicode-based with secure cookie support

HTTP request handling capability

RESTful request dispatch handling

Built-in server development and integrated unit-testing support

Plugs into any ORM framework

3-Web2Py

Web2Py is an open-source, full-stack, and scalable Python application framework compatible with most operating systems, both mobile-based and web-based.

It is a platform-independent framework that simplifies development through an IDE that has a code editor, debugger, and single-click deployment.

Web2Py deals with data efficiently and enables swift development with MVC design but lacks configuration files on the project level.

It has a critical feature, a ticketing system that auto-generates tickets in the event of issues and enables tracking of issues and status.

Key Features of Web2py Framework:

No configuration and installation needed

Enables use of NoSQL and relational databases

Follows MVC design with consistent API for streamlining web development

Supports internationalization and role-based access control

Enable backward compatibility

Addresses security vulnerabilities and critical dangers

4-TurboGears

TurboGears is an open-source, full-stack, data-driven popular Python web app framework based on the ObjectDispatch paradigm.

It is meant to make it possible to write both small and concise applications in Minimal mode or complex applications in Full Stack mode.

TurboGears is useful for building both simple and complex apps with its features implemented as function decorators with multi-database support.

It offers high scalability and modularity with MochiKit JavaScript library integration and ToscaWidgets for seamless coordination of server deployment and front end.

Key aspects of TurboGears Framework:

MVC-style architecture

Provides command-line tools 

Extensive documentation

Validation support with Form Encode

It uses pylons as a web server

Provides PasteScript templates

5-Falcon

Falcon is a reliable and secure back-end micro Python application framework used for developing highly-performing microservices, APIs, and large-scale application backends.

It is extensible and optimized with an effective code base that promotes building cleaner designs with HTTP and REST architecture.

Falcon provides effective and accurate responses for HTTP threats, vulnerabilities, and errors, unlike other Python back-end frameworks. Large firms like RackSpace, OpenStack, and LinkedIn use Falcon.

Falcon can handle most requests with similar hardware to its contemporaries and has total code coverage.

Key Features of Falcon Framework:

Intuitive routing with URL templates

Unit testing with WSGI mocks and helpers

Native HTTP error responses

Optimized and extensible code base

Upfront exception handling support

DRY request processing

Cython support for enhanced speed

6-CherryPy

CherryPy is an object-oriented, open-source, Python micro framework for rapid development with a robust configuration system. It doesn’t require an Apache server and enables the use of technologies for Cetera templating and accessing data.

CherryPy is one of the oldest Python app development frameworks mainly for web development. Applications designed with CherryPy are self-contained and operate on multi-threaded web servers. It has built-in tools for sessions, coding, and caching.

Popular examples of CherryPy apps include Hulu and Juju.

Key features of CherryPy Framework:

Runs on Android

Flexible built-in plugin system

Support for testing, profiling, and coverage

WSGI compliant

Runs on multiple HTTP servers simultaneously

Powerful configuration system

7-Tornado

It is an open-source asynchronous networking Python framework that provides URL handling, HTML support, python database application framework support, and other crucial features of every application.

Tornado is as popular as Django and Flask because of its high-performing tools and features except that it is a threaded framework instead of being WSGI-based.

It simplifies web server coding, handles thousands of open connections with concurrent users, and strongly emphasizes non-blocking I/O activities for solving C10k difficulties. 

Key features of Tornado Framework:

Web templating techniques

Extensive localization and translation support

Real-time, in-the-moment services

Allows third-party authorization, authorization methods, and user authentication

Template engine built-in

HTTP client that is not blocking

8-AIOHTTP

AIOHTTP is a popular asynchronous client-side Python web development framework based on the Asyncio library. It depends on Python 3.5+ features like Async and Awaits. 

AIOHTTP offers support for client and server WebSockets without the need for Callback Hell and includes request objects and routers for redirecting queries to functions.

Key Highlights of AIOHTTP Python Framework:

Provides pluggable routing

Supports HTTP servers

Supports both client and WebSockets without the callback hell.

Middleware support for web servers

Effective view building

Also, there are two main cross-platform Python mobile app frameworks

9- Kivy

Kivy is a popular open-source Python framework for mobile app development that offers rapid application development of cross-platform GUI apps.

With a graphics engine designed over OpenGL, Kivy can manage GPU-bound workloads when needed.

Kivy comes with a project toolkit that allows developers to port apps to Android and has a similar one for iOS. However, porting Python apps to iOS currently is possible with Python 2.7.

Features of Kivy Framework:

Enables custom style in rendering widgets to give a native-like feel

Enhanced consistency across different platforms with a swift and straightforward approach

Well-documented, comprehensive APIs and offers multi-touch functionalities

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[meowbilli]

How Much Does a Doordash Clone App Cost? | Build with Enatega

The food delivery market has seen explosive growth over the past few years, with platforms like DoorDash, UberEats, and Grubhub leading the charge. For entrepreneurs, food delivery businesses, and tech startups, creating a Doordash clone app could be the perfect opportunity to tap into this thriving market. But how much does it actually cost to develop one?

This blog will take you through the features and functionality of a Doordash clone, the various factors influencing development costs, and why a ready-made solution like Enatega might be your ideal choice. Let's break it all down.

What is a Doordash Clone App?

Think of a Doordash clone app as a prebuilt framework designed to replicate the core functionality of a popular app like DoorDash, but with opportunities for customization to align with your brand and business model.

Key Features of a Doordash Clone App Include:

Customer Panel:

Advanced search options for restaurants and cuisines

Real-time order tracking

Multiple payment gateways

Restaurant Partner Panel:

Order management and live status updates

Menu customization

Earnings reports and analytics

Delivery Partner Panel:

GPS-enabled navigation

Order history and earnings insights

Availability toggles and communication tools

Admin Panel:

Customer, restaurant, and delivery management

Promotion and discount management

Insights through data analytics for decision-making

These features ensure a seamless user experience while providing a full suite of tools for restaurant owners and delivery drivers alike. The beauty of a clone app lies in its flexibility—features can easily be added, removed, or tailored to meet your business's unique needs.

Factors That Influence the Cost of Development

Creating a Doordash clone app isn't as straightforward as putting together a team of developers. Various factors come into play that significantly affects the final cost.

1. Technical Complexity

Are you working on offering unique features like AI-driven delivery time estimates or gamification to enhance user engagement? The more complex your desired app functionality, the higher the development cost.

2. Design

Sleek, user-friendly UI/UX design ensures your users stick around, but achieving that level of finesse isn't cheap. Custom designs will cost more than using basic templates, especially if your app needs to reflect strong branding.

3. Platform

Are you planning to launch on iOS, Android, or both? Dual-platform development will naturally require more time, effort, and investment compared to focusing on a single platform.

4. Third-Party Integrations

Third-party integrations like payment gateways (Stripe, PayPal, etc.), mapping APIs (Google Maps), and notification services contribute to smoother operations but can add to the overall cost.

5. Ongoing Maintenance

App development doesn’t end once it's live. Regular updates, bug fixes, server costs, and customer support add to the long-term expenditure.

These aspects can cause the cost of development to range from $30,000 to $150,000, depending on your choices. But is there a more affordable path?

Cost Analysis: From Scratch vs. Using Enatega

For entrepreneurs working with limited budgets and tight deadlines, developing an app from scratch might not always be the best option. This is where ready-made solutions like Enatega come in.

Building From Scratch

Development Time: 4–12 months

Cost: $50,000–$150,000 depending on the features and complexity

Key Considerations:

Offers complete creative freedom.

High upfront costs.

Longer time to market.

Enatega Readymade Solution

Development Time: 2–4 weeks

Cost: $10,000–$30,000 depending on customizations

Key Benefits:

Prebuilt and customizable to fit your brand.

Substantially faster time to market.

Affordable and scalable solution.

With Enatega, you benefit from tried-and-tested models while still personalizing your app to fit your unique needs. It’s a solution tailored for startups and entrepreneurs who want to get their food delivery service up and running quickly without compromising on quality.

Why Enatega is a Game-Changer for Startups

Still on the fence? Here’s why Enatega stands out among its competitors:

Affordability

Enatega offers competitive pricing compared to starting from scratch, giving startups a professional-grade app at a fraction of the cost.

Quick Deployment

With Enatega, your app can be up and running in under a month, allowing you to start generating revenue sooner.

Scalability

Enatega’s architecture is designed for growth. Whether you're adding more restaurants, expanding delivery zones, or offering new features, scaling up is seamless.

Ongoing Support

With technical support and regular updates, Enatega ensures your app runs smoothly, so you can focus on growing your business.

Community-Driven

Whether through collaborative forums or real-life partnerships, Enatega fosters a sense of camaraderie among food delivery entrepreneurs, helping you learn and grow as you build your business.

Case Studies of Enatega Success Stories

1. BiteEasy – A Niche Vegan Delivery App

When BiteEasy decided to cater to vegan food lovers, they turned to Enatega to create a user-friendly app that delivered curated vegan meals. Within six months of launch, they had onboarded 150 restaurants and saw revenue growth of 40%.

2. NightBites – Late-Night Delivery Startup

NightBites used Enatega to fill the gap in late-night food delivery. With Enatega's quick deployment, they launched in just three weeks and captured a loyal customer base by offering 24/7 service.

These examples are just two of the many ways Enatega has empowered food delivery businesses to thrive.

Build Your Food Delivery Empire

The food delivery industry is booming, and leveraging a Doordash clone app might just be your ticket to carving out your piece of the pie. While building from scratch offers creative freedom, tools like Enatega make food delivery app development accessible for startups and entrepreneurs by saving time, money, and effort.

[filemakerexperts]

Mitarbeiterzuordnung: Ein PHP-basiertes Tool zur Visualisierung und Priorisierung

Dieses Projekt kombiniert PHP, die Haversine-Formel und die Google Maps API, um die besten Mitarbeiter für einen Unterstützungsantrag basierend auf Standort und Verfügbarkeit auszuwählen. Das Ergebnis wird auf einer interaktiven Karte dargestellt, und eine Mitarbeiterliste zeigt alle relevanten Details wie Entfernung und Priorität. Der Grund, viele Mitarbeiter sind bei unterschiedlichen Kunden im Einsatz. Es kann aber notwendig sein, das ein zweiter oder dritter Mitarbeiter für diesen Einsatz benötigt werden. Schwierig die Mitarbeiter aus dem Kopf heraus zuordnen zu wollen. Überblick Unser Ziel ist es, ein System zu erstellen, das: • Mitarbeiter basierend auf Entfernung und zeitlicher Verfügbarkeit priorisiert. • Standorte auf einer Karte visualisiert. • Überlappende Standorte automatisch verschiebt, um sie sichtbar zu machen. • Eine übersichtliche Mitarbeiterliste generiert. PHP: Entfernung und Datenverarbeitung Im folgenden PHP-Skript berechnen wir die Entfernung zwischen Standorten mit der Haversine-Formel, filtern die Mitarbeiter und senden die Daten an eine KI-Analyse.

<?php // Dein API-Schlüssel (ersetzen, wenn nötig) $apiKey = 'GOOGLE_MAPS_KEY'; $serviceKey = 'DEIN_KEY_FÜR_DIE_KI'; // Mein API-Key // Daten von FileMaker empfangen $rawData = $_GET['data'] ?? ''; // Haversine-Formel zur Berechnung der Entfernung function haversine($lat1, $lng1, $lat2, $lng2) { $earthRadius = 6371; // Radius der Erde in Kilometern $dLat = deg2rad($lat2 - $lat1); $dLng = deg2rad($lng2 - $lng1); $a = sin($dLat / 2) * sin($dLat / 2) + cos(deg2rad($lat1)) * cos(deg2rad($lat2)) * sin($dLng / 2) * sin($dLng / 2); $c = 2 * atan2(sqrt($a), sqrt(1 - $a)); return $earthRadius * $c; } // Daten parsen $employees = explode('|', $rawData); // Mitarbeiter sind durch "|" getrennt $parsedData = []; foreach ($employees as $employee) { $fields = explode(',', $employee); // Felder durch "," getrennt if (count($fields) === 5) { $parsedData[] = [ 'name' => $fields[0], 'lat' => (float)$fields[1], 'lng' => (float)$fields[2], 'startTime' => $fields[3], 'endTime' => $fields[4], ]; } } // Wenn keine gültigen Daten vorhanden sind if (empty($parsedData)) { die("Keine gültigen Mitarbeiterdaten empfangen."); } // Der Mitarbeiter, der Unterstützung benötigt $supportRequest = $parsedData[0]; $remainingEmployees = array_slice($parsedData, 1); // Entfernung berechnen und in das Array hinzufügen foreach ($remainingEmployees as &$employee) { $employee['distance'] = round(haversine( $supportRequest['lat'], $supportRequest['lng'], $employee['lat'], $employee['lng'] ), 2); } // Anfrage an KI zur Analyse senden $payload = [ 'supportRequest' => $supportRequest, 'employees' => $remainingEmployees, ]; // Die Anfrage an die OpenAI-API bleibt unverändert und liefert die Ergebnisse // JSON-Analyse bleibt wie im Skript ?>

Visualisierung mit Google Maps API Hier zeigen wir, wie die Ergebnisse visualisiert werden, einschließlich der dynamischen Verschiebung überlappender Marker und einer dynamischen Mitarbeiterliste.

<script> function initMap() { const map = new google.maps.Map(document.getElementById('map'), { zoom: 10, center: { lat: <?php echo $supportRequest['lat']; ?>, lng: <?php echo $supportRequest['lng']; ?> }, }); const employees = <?php echo json_encode($analysisResult); ?>; // Unterstützungsmarker hinzufügen new google.maps.Marker({ position: { lat: <?php echo $supportRequest['lat']; ?>, lng: <?php echo $supportRequest['lng']; ?> }, map: map, title: "Unterstützungsantrag", }); // Mitarbeiter hinzufügen const OFFSET_DISTANCE = 0.001; const seenLocations = new Map(); employees.forEach((employee) => { let key = `${employee.lat},${employee.lng}`; if (seenLocations.has(key)) { const offset = seenLocations.get(key) + 1; employee.lat += OFFSET_DISTANCE * offset; employee.lng += OFFSET_DISTANCE * offset; seenLocations.set(key, offset); } else { seenLocations.set(key, 0); } new google.maps.Marker({ position: { lat: employee.lat, lng: employee.lng }, map: map, title: `${employee.name} - ${employee.distance} km`, }); }); } window.onload = initMap; </script>

Was übergeben wir aus FileMaker https://deine_url.de/ki_calendar.php?data=André,52.5035525,13.4176949,7:30:00,8:22:30|Mario,52.5814763,13.3057333,9:30:00,11:00:00|Mario,52.5346344,13.4224666,12:00:00,13:00:00|,52.5346344,13.4224666,7:00:00,8:00:00|Philipp,52.4702886,13.2930662,13:00:00,14:00:00|David,52.4702886,13.2930662,10:00:00,11:00:00|Jennifer,52.5035525,13.4176949,9:30:00,10:22:30|Philipp,52.5035525,13.4176949,14:00:00,14:52:30|André,52.5035525,13.4176949,7:30:00,8:22:30|Jennifer,52.6053036,13.3540889,11:00:00,12:30:00|Martin,52.5727963,13.4187507,15:00:00,16:00:00 Fazit Die Rolle der KI in unserem Projekt Bei diesem Projekt haben wir die GPT-4 API von OpenAI genutzt, eine hochmoderne KI, die auf natürliche Sprachverarbeitung spezialisiert ist. Die KI übernimmt dabei eine entscheidende Rolle: Aufgaben der KI 1. Analyse der Daten: Die KI wertet die übermittelten Daten aus, einschließlich der Standorte und Verfügbarkeiten der Mitarbeiter, sowie der geforderten Zeitspanne. Sie trifft Entscheidungen, welche Mitarbeiter am besten geeignet sind, basierend auf: • Geografischer Nähe (Entfernung). • Zeitlicher Verfügbarkeit (Überlappung mit der Anforderungszeit). • Priorisierungskriterien. 2. Priorisierung der Mitarbeiter: Die KI sortiert die Mitarbeiter in Prioritätsklassen (hoch, mittel, niedrig), um Entscheidungsprozesse zu erleichtern. Dies hilft besonders bei komplexen Szenarien mit vielen Teilnehmern und unterschiedlichen Anforderungen. 3. Flexible Verarbeitung: Mit der eingebauten Sprachverarbeitungsfähigkeit kann die KI auf benutzerdefinierte Regeln und neue Anforderungen reagieren. Im Falle unseres Projekts wird sichergestellt, dass: • Der Supportanforderer (Mario) nicht als Unterstützer in die Liste aufgenommen wird. • Die Ergebnisse stets im JSON-Format zurückgegeben werden, damit sie direkt weiterverarbeitet werden können. Warum GPT-4? • Komplexe Entscheidungen: GPT-4 kann nicht nur einfache Regeln anwenden, sondern auch inhaltlich komplexe Daten wie geografische Koordinaten, Zeitfenster und Prioritäten verknüpfen. • Flexibilität: Änderungen in den Anforderungen (z. B. neue Priorisierungsregeln) lassen sich einfach umsetzen, indem wir die KI-Prompts anpassen. • Effizienz: Im Gegensatz zu einer festen Programmierung ermöglicht die KI schnelle Analysen und Rückmeldungen, ohne den PHP-Code manuell anzupassen. Das Projekt zeigt, wie sich PHP, eine KI-API und Google Maps zu einem leistungsstarken Logistik-Tool kombinieren lassen. Dies ist natürlich nur eine erste Version und verarbeitet nur wenige Daten.

[vawagencymarketing]

WordPress Development: Build Stunning Websites Easily

In today’s digital landscape, having a robust and visually appealing online presence is non-negotiable for businesses and individuals alike. WordPress, a versatile and user-friendly content management system (CMS), has emerged as the go-to platform for creating stunning websites with ease. In this blog, we’ll delve into WordPress development, exploring its features, benefits, and how you can leverage it to craft a remarkable online presence.

Why Choose WordPress for Website Development?

WordPress powers over 43% of all websites on the internet, making it the most popular CMS globally. Its popularity stems from several key factors:

1. Ease of Use

WordPress is designed with user-friendliness in mind. Its intuitive interface allows users with little to no coding experience to create and manage websites efficiently.

2. Flexibility and Scalability

Whether you’re building a simple blog or a complex e-commerce site, WordPress can handle it all. Its modular architecture ensures scalability as your needs grow.

3. Extensive Plugin Ecosystem

With over 60,000 plugins available, WordPress lets you add almost any functionality to your website—from SEO tools to contact forms and advanced analytics.

4. Customizable Themes

WordPress offers thousands of free and premium themes, enabling you to design a website that aligns with your brand identity.

5. SEO-Friendly Architecture

Search engine optimization (SEO) is critical for online visibility. WordPress is inherently SEO-friendly and supports additional optimization through plugins like Yoast SEO and Rank Math.

6. Active Community Support

WordPress boasts a large, active community of developers and users who contribute to forums, tutorials, and regular updates.

Getting Started with WordPress Development

1. Set Up Your Hosting and Domain

The first step in WordPress development is selecting a reliable hosting provider and registering a domain name. Hosting options like Bluehost, SiteGround, and WP Engine offer seamless WordPress integration.

2. Install WordPress

Most hosting providers offer one-click WordPress installation. Alternatively, you can download the WordPress software from WordPress.org and install it manually.

3. Choose a Theme

Selecting the right theme is crucial for your website’s appearance and functionality. Explore the WordPress Theme Directory for free options or purchase premium themes from marketplaces like ThemeForest.

4. Install Essential Plugins

Enhance your site’s capabilities by installing essential plugins. For instance:

Elementor: Drag-and-drop page builder

WooCommerce: E-commerce functionality

Akismet Anti-Spam: Protects your site from spam comments

WPForms: User-friendly form builder

5. Customize Your Website

Tailor your site’s layout, colors, fonts, and features using the WordPress Customizer or page builders like Elementor and Beaver Builder.

6. Add Content

Create engaging and high-quality content for your website. Use the WordPress Block Editor (Gutenberg) to add text, images, videos, and other media effortlessly.

Advanced WordPress Development Techniques

For those looking to go beyond the basics, here are some advanced WordPress development techniques:

1. Custom Theme Development

While pre-designed themes are convenient, custom themes offer greater flexibility. By creating your own theme, you can ensure a unique design tailored to your specific needs.

2. Custom Plugin Development

Developing custom plugins allows you to add niche functionalities that aren’t available in existing plugins.

3. Child Themes

When modifying a theme, use a child theme to preserve changes during theme updates.

4. Database Optimization

Optimize your WordPress database regularly to improve site speed and performance. Plugins like WP-Optimize make this task easy.

5. Integrate APIs

Integrate third-party APIs to expand your site’s functionality. For example, you can use the Google Maps API for location-based services or a payment gateway API for secure transactions.

6. Use Git for Version Control

Version control systems like Git help you manage code changes effectively, especially in collaborative projects.

Tips for Building Stunning Websites with WordPress

Prioritize Mobile Responsiveness: Ensure your website looks and functions flawlessly on all devices.

Optimize for Speed: Use caching plugins like W3 Total Cache and optimize images to enhance site speed.

Focus on User Experience (UX): Design intuitive navigation, use clear calls-to-action, and ensure accessibility for all users.

Secure Your Website: Implement SSL certificates, use security plugins like Wordfence, and perform regular backups.

Monitor Analytics: Use tools like Google Analytics to track visitor behavior and improve site performance.

Common Challenges in WordPress Development and How to Overcome Them

1. Slow Loading Times

Solution: Optimize images, enable caching, and use a content delivery network (CDN).

2. Plugin Conflicts

Solution: Regularly update plugins and deactivate conflicting ones to identify issues.

3. Hacking and Security Threats

Solution: Keep WordPress, themes, and plugins updated, and use robust security measures.

4. Customization Limitations

Solution: Learn basic coding (HTML, CSS, PHP) to make advanced customizations.

5. SEO Challenges

Solution: Use SEO plugins and follow best practices for keyword optimization and site structure.

Future Trends in WordPress Development

1. Headless WordPress

Headless WordPress decouples the front end from the back end, allowing developers to use modern frameworks like React or Vue.js for enhanced performance and flexibility.

2. AI Integration

Artificial intelligence is revolutionizing website development. Expect to see more AI-powered tools for content creation, personalization, and analytics in WordPress.

3. Voice Search Optimization

With the rise of voice assistants, optimizing websites for voice search is becoming essential.

4. Progressive Web Apps (PWAs)

Transforming WordPress sites into PWAs can improve user engagement by offering app-like experiences.

5. Sustainability Focus

As digital sustainability gains traction, WordPress developers are exploring energy-efficient hosting and lightweight designs to reduce carbon footprints.

Conclusion

WordPress development empowers individuals and businesses to create stunning, functional websites with ease. Whether you’re a beginner or an experienced developer, WordPress’s flexibility, extensive resources, and active community make it an invaluable tool. By leveraging the tips and techniques shared in this blog, you’ll be well-equipped to build a website that stands out in today’s competitive digital world. Embrace the power of WordPress and transform your online vision into reality.

[videoddd]

Five Key Lessons for Google Earth Engine Beginners

Practical information from a Python API user Land cover map of the Paute River Basin in Ecuador for 2020. Image created using Google Earth Engine Python API and Geemap. Data source: Friedl M., Sulla-Menashe D. (2022); Lehner B., Grill G. (2013) & Lehner B., Verdin K., Jarvis A. (2008). As a climate scientist, Google Earth Engine (GEE) is a powerful tool in my toolbox. No more downloading heavy…