#ETL processes

[tudip123]

The Importance of Data Engineering in Today’s Data-Driven World

In today’s fast-paced, technology-driven world, data has emerged as a critical asset for businesses across all sectors. It serves as the foundation for strategic decisions, drives innovation, and shapes competitive advantage. However, extracting meaningful insights from data requires more than just access to information; it necessitates well-designed systems and processes for efficient data management and analysis. This is where data engineering steps in. A vital aspect of data science and analytics, data engineering is responsible for building, optimizing, and maintaining the systems that collect, store, and process data, ensuring it is accessible and actionable for organizations.

Let's explore how Data Engineering is important in today's world:

1. What is Data Engineering

2. Why is Data Engineering Important

3. Key Components of Data Engineering

4. Trends in Data Engineering

5. The Future of Data Engineering

Let’s examine each one in detail below.

What is Data Engineering?

Data engineering involves creating systems that help collect, store, and process data effectively.It involves creating data pipelines that transport data from its source to storage and analysis systems, implementing ETL processes (Extract, Transform, Load), and maintaining data management systems to ensure data is accessible and secure. It enables organizations to make better use of their data resources for data-driven decision-making.

Why is Data Engineering Important?

Supports Data-Driven Decision-Making: In a competitive world, decisions need to be based on facts and insights. Data engineering ensures that clean, reliable, and up-to-date data is available to decision-makers. From forecasting market trends to optimizing operations, data engineering helps businesses stay ahead.

Manages Big Data Effectively: Big data engineering focuses on handling large and complex datasets, making it possible to process and analyze them efficiently. Industries like finance, healthcare, and e-commerce rely heavily on big data solutions to deliver better results.

Enables Modern Technologies: Technologies like machine learning, artificial intelligence, and predictive analytics depend on well-prepared data. Without a solid modern data infrastructure, these advanced technologies cannot function effectively. Data engineering ensures these systems have the data they need to perform accurately.

Key Components of Data Engineering:

Data Pipelines: Data pipelines move data automatically between systems.They take data from one source, change it into a useful format, and then store it or prepare it for analysis.

ETL Processes: ETL (Extract, Transform, Load) processes are crucial in preparing raw data for analysis. They clean, organize, and format data, ensuring it is ready for use.

Data Management Systems: 

These systems keep data organized and make it easy to access. Examples of these systems are databases, data warehouses, and data lakes.

Data Engineering Tools: From tools like Apache Kafka for real-time data streaming to cloud platforms like AWS and Azure, data engineering tools are essential for managing large-scale data workflows.

Trends in Data Engineering:

The field of data engineering is changing quickly, and many trends are shaping its future:

Cloud-Based Infrastructure: More businesses are moving to the cloud for scalable and flexible data storage.

Real-Time Data Processing: The need for instant insights is driving the adoption of real-time data systems.

Automation in ETL: Automating repetitive ETL tasks is becoming a standard practice to improve efficiency.

Focus on Data Security: With increasing concerns about data privacy, data engineering emphasizes building secure systems.

Sustainability: Energy-efficient systems are gaining popularity as companies look for greener solutions.

The Future of Data Engineering:

The future of data engineering looks bright. As data grows in size and complexity, more skilled data engineers will be needed.Innovations in artificial intelligence and machine learning will further integrate with data engineering, making it a critical part of technological progress. Additionally, advancements in data engineering tools and methods will continue to simplify and enhance workflows.

Conclusion:

Data engineering is the backbone of contemporary data management and analytics. It provides the essential infrastructure and frameworks that allow organizations to efficiently process and manage large volumes of data. By focusing on data quality, scalability, and system performance, data engineers ensure that businesses can unlock the full potential of their data, empowering them to make informed decisions and drive innovation in an increasingly data-driven world.

Tudip Technologies has been a pioneering force in the tech industry for over a decade, specializing in AI-driven solutions. Our innovative solutions leverage GenAI capabilities to enhance real-time decision-making, identify opportunities, and minimize costs through seamless processes and maintenance.

If you're interested in learning more about the Data Engineering related courses offered by Tudip Learning  please visit:  https://tudiplearning.com/course/essentials-of-data-engineering/.

[oditeksolutions]

In the dynamic landscape of modern business, data is the lifeblood that fuels informed decision-making and drives innovation. To harness the full potential of data, organizations often rely on (Extract, Transform, Load) ETL processes. ETL systems are the backbone of data integration, enabling seamless data movement between systems and transforming it to meet specific requirements.

ELT systems define the core stages of data processing. In the Extract phase, data is sourced from various outlets focusing on efficiency and data integrity. Post-extraction, data undergoes Loading into a centralised storage system, while Transformation refines it for analysis by cleaning, normalizing, and merging data sets. ELT processes are essential, enabling businesses to efficiently manage and analyse vast volumes of data, driving informed decisions and insights.

Components of ETL

ETL systems refer to the three key stages of data integration. Each stage serves a distinct purpose in the journey of data from source to destination. This process involves the following stages:

Extract

ETL process, the extraction phase acts as the cornerstone, gathering data from diverse origins. This phase sets the foundation by gathering raw data for analysis and integration.

Transform

Data collected in the extraction phase; rarely aligns perfectly with the intended analytical structure. Data collected in the extraction phase often needs to be cleaned, standardized, and enriched to make it usable. Transformations can include data validation, aggregation, and formatting.

Load

The final phase of ETL process flow involves the load phase, where the refined and transformed data finds its ultimate destination, whether it be a data warehouse, database, or another repository. Ensuring a seamless and efficient transfer of the processed information is imperative during this stage.

ETL Processes

Boomi is a cloud-based integration platform as a service (iPaaS) that offers a variety of features for ETL Systems, including:

1. Connectors

Boomi comes packed with ready-to-use connectors spanning databases, CRM, ERP, and cloud apps, streamlining ETL flow. Enabling smooth data extraction, transformation, and loading between various systems. For instance, it simplifies syncing customer data between Salesforce and ERP systems like SAP, expediting integration while ensuring data coherence across platforms.

2. Data Mapping and Modeling

Boomi excels at effortlessly translating data between different formats, a crucial aspect of the ETL systems. This intuitive drag-and-drop approach empowers users, even those without extensive coding experience, to efficiently manage complex data integration tasks. In essence, ETL flow provides features for mapping source data to destination data models, ensuring seamless data flow through ETL process flow.

3. Data transformation

Boomi’s data transformation tools act like digital alchemists—cleansing, filtering, and aggregating data with finesse. This ETL process flow-centric approach within Boomi not only enhances data quality but also ensures that transformed data aligns with the specific requirements of the target systems, contributing to more effective and delivering a performance that amplifies business intelligence.

4. Process orchestration

Boomi allows you to orchestrate your ETL processes into workflows, so you can automate your data integration.

5. Error handling

Robust ETL systems streamline issue identification with early detection, mechanisms for error handling, logging, customizable responses, and automatic rerouting. Integration with monitoring tools provides a holistic view, while alerts aid quick responses. Error classification, escalation procedures, and continuous improvement contribute to efficient issue management, supporting compliance and enhanced reliability and maintaining data integrity.

Boomi ETL in Action

Let us take a closer look at how Boomi simplifies ETL processes by providing the below tools:

1. Data Source Setup: Begin by configuring your data source within Boomi. This could be an application, database, or file location.

2. Transformation: Boomi’s intuitive interface allows you to design data transformations visually, without writing code. You can validate data, apply business rules, and manipulate data as needed.

3. Loading Data: Once data is transformed, you can load it into your target system. Boomi’s connectors make this process seamless.

4. Monitoring and Optimization: Boomi offers robust monitoring and logging capabilities, enabling you to track the performance of your ETL processes and identify areas for improvement.

5. Improve data quality: Boomi ETL can be used to clean and standardize data before it is loaded into a data warehouse or other target system. This can help to improve the quality of data analysis and reporting.

6. Improve customer experience: Boomi ETL can be used to integrate data from different customer systems, such as CRM and marketing automation systems. This can help to create a more unified view of the customer and improve the overall customer experience.

7. Accelerate innovation: Boomi ETL can help organizations accelerate innovation by making it easier to integrate new data sources and applications.

Why Boomi for ETL Processes?

Boomi is a leading Integration Platform as a Service (iPaaS) that simplifies ETL process flow. Here is why it is an excellent choice for ETL systems:

1. Cloud-based: Boomi is a cloud-based platform, so you do not have to worry about installing or maintaining any software.

2. Easy to use: Boomi is designed to be easy to use, even if you do not have any programming experience.

3. Scalable: Boomi is scalable to meet the needs of businesses of all sizes.

4. Affordable:Boomi is a cost-effective solution for ETL systems, especially when compared to on-premises solutions.

5. Efficiency: Automated data transformations and integrations enhance operational efficiency.

6. Historical Data Analysis: ETL processes can be configured to capture and store historical data, facilitating trend analysis and long-term insights.

7. Time and Cost Savings: Automation reduces manual intervention, saving time and resources. ETL processes can efficiently handle large volumes of data, optimizing overall costs.

Conclusion

Boomi’s ETL systems capabilities offer a user-friendly, efficient, and scalable approach to data integration. Whether you are a small business looking for a cost-effective solution or a large enterprise in need of robust data integration, Boomi’s ETL process flow in a low-code/no-code environment simplifies the process while ensuring the integrity and quality of your data. Unlock the true potential of your data with the Boomi ETL systems.

At OdiTek, we understand the critical role of data integration in today’s digital era. Our expertise in Boomi ETL systems ensure that businesses can navigate this complex terrain effectively.

Contact us today!

[codesorcerer]

Mastering Data Engineering: Techniques, Practices, and Strategies

Introduction In today’s data-driven world, effective data engineering plays a crucial role in enabling organizations to harness the power of data for insights, decision-making, and innovation. Data engineering involves the processes and technologies used to transform, store, and manage data in a way that is efficient, scalable, and reliable. In this comprehensive guide, we will delve into the…

[thedbahub]

SSIS: Navigating Common Challenges

Diving into the world of SQL Server Integration Services (SSIS), we find ourselves in the realm of building top-notch solutions for data integration and transformation at the enterprise level. SSIS stands tall as a beacon for ETL processes, encompassing the extraction, transformation, and loading of data. However, navigating this powerful tool isn’t without its challenges, especially when it…

[123albert]

The process of extract, transform and load is a method to move data from various sources to data warehouse. Check out to get complete overview of ETL process.

[satvikasailu6]

"Real-Time ETL Testing: Stock Market Data"

ETL testing

ETL testing (Extract, Transform, Load) is a critical component of data management and plays a pivotal role in ensuring data quality in the data pipeline. The ETL process involves extracting data from various sources, transforming it into a suitable format, and loading it into a target destination such as a data warehouse, data lake, or database.

ETL Process:

Data Ingestion: The ETL testing process starts by ingesting live stock market data from various stock exchanges, financial news feeds, and social media platforms. This data includes stock prices, trading volumes, news articles, social media sentiment, and economic indicators.

Real-time Transformation: As data is ingested, it undergoes real-time transformations. For example:

Data cleansing: Removing duplicates, handling missing values, and correcting data anomalies.

Data enrichment: Enhancing raw data with additional information such as company profiles and historical price trends.

Sentiment analysis: Analyzing social media data to gauge market sentiment and news sentiment.

Loading into Data Warehouse: The transformed data is loaded into a data warehouse, which serves as the foundation for real-time analytics, reporting, and visualization.

Key Testing Scenarios:

Data Ingestion Testing:

Verify that data sources are connected and data is ingested as soon as it becomes available.

Test data integrity during the ingestion process to ensure no data loss or corruption occurs.

Real-time Transformation Testing:

Validate that real-time transformations are applied accurately and promptly.

Verify that data cleansing, enrichment, and sentiment analysis are performed correctly and do not introduce delays.

Data Quality and Consistency Testing:

Perform data quality checks in real-time to identify and address data quality issues promptly.

Ensure that transformed data adheres to quality standards and business rules.

Performance Testing:

Stress test the ETL Testing process to ensure it can handle high volumes of real-time data.

Measure the latency between data ingestion and data availability in the data warehouse to meet performance requirements.

Error Handling and Logging Testing:

Validate the error handling mechanisms for any data ingestion failures or transformation errors.

Ensure that appropriate error notifications are generated, and errors are logged for analysis.

Regression Testing:

Continuously run regression tests to ensure that any changes or updates to the ETL process do not introduce new issues.

Real-time Analytics Validation:

Test the accuracy and timeliness of real-time analytics and trading insights generated from the data.

Security and Access Control Testing:

Ensure that data security measures, such as encryption and access controls, are in place to protect sensitive financial data.

Compliance Testing:

Verify that the ETL process complies with financial regulations and reporting requirements.

Documentation and Reporting:

Maintain comprehensive documentation of test cases, test data, and testing results.

Generate reports on the quality and performance of the real-time ETL process for stakeholders.

[nitor-infotech]

ETL testing is performed for data extraction, transformation as well as loading for BI reporting, be it India or US. Read our blog to learn about its importance, approach, types, tools, methodologies, bugs, best practices, and key challenges. 

[rajaniesh]

Streamline Your Big Data Projects Using Databricks Workflows

Databricks Workflows is a powerful tool that enables data engineers and scientists to orchestrate the execution of complex data pipelines. It provides an easy-to-use graphical interface for creating, managing, and monitoring end-to-end workflows with minimal effort. With Databricks Workflows, users can design their own custom pipelines while taking advantage of features such as scheduling,…

[hannyoontify]

[21:23] jeonghan sighed to himself before gently knocking on your bedroom door.

'i'm home'

he heard you shuffling on the other side of the door before your soft voice called out. 'come in'

he pushed the door open and walked into the sight of you covered with tissues, plushies, and pillows. your laptop was resting on your lap (duh) and your eyes were red. jeonghan felt a fond smile tugging on the corners of his lips and he made his way to your side of the bed.

'what movie was it this time? big hero 6? inside out? up? coco? ratatouille?' jeonghan cupped your face with his hands and wiped away a stray tear with his thumb.

you pouted and hit his chest. 'ratatouille was sad, okay? they opened a new restaurant and remy was able to live the life he wanted with the support of his family' you sniffled. you felt your eyes sting as they began to tear up again and hit jeonghan's chest once more when he laughed. 'it's not funny! linguini and colette were in love and they ended up together'

jeonghan smiled. 'and that's why you're covered in tissues. because a rat can cook'

''better than you, at least'

he gasped and you giggled in delight in the way he took (pretend) offense to that. you smiled and pulled away as he reveled in his shock, his mouth hanging wide open. 'go get changed, i don't want your outside germs on the bed'

jeonghan did as he was told. he climbed into bed next to you (pushing a couple plushes off the bed in the process–you would kill him for that but that was a future jeonghan problem. right now he just wanted to hold you in his arms) and guided your head to rest on his chest. his arm wrapped around you and rested on your waist and pulled your body closer to his.

'how'd you know?' you asked more quietly. jeonghan rested his lips on the top of your head, inhaling the gentle scent of aloe shampoo.

your boyfriend simply hummed. 'what's there not for me to know about you, my darling? i can read you like a book- actually not a book, i don't like books'

you snorted.

'i can read you like.. a magazine! yeah. magazines. magazines are better because i'm in some of them. and they have pictures. lots of pictures'

you wrinkled your nose at his short ramble and pressed a quick kiss to his collarbone. 'i think you're sleep deprived, hannie'

'nuh-uh'

'yuh-huh. what if i told you that best friends to lovers was better than enemies to lovers'

you never got a response because jeonghan had already fallen fast asleep.

(although if he heard you say that, he would've been whipped up into a frenzy and present a 125 page PPT about why ETL was better than FTL)

a/n: and what if i wrote a jeonghan enlistment fic. would that be too horrible

[aniseya]

so about the leaks.

tbh this doesn’t frighten me because it could be a number of things going on. osha approaching sol and mae and then “betraying” qimir isn’t even the worst thing for me, just angst and makes them more of a slowburn. or it could be a trick where qimir and osha act like enemies just to catch sol and mae off-guard. or it could also be osha finding out about her mom’s death and going complete grief-mode and threatening anyone who comes near her because it’s all too much to process at once.

i mean tbh leslye already established it is a romance and even though we’ve had good/peaceful moments with them so far, maybe osha has a way to go before she accepts anything. if it is the off-chance she goes with mae and sol after betraying qimir, like i said, ship angst. i’d take this scenario over stuff that could be worse. in a way, it feels kinda sylki to me, or just traditional etl.

[cailynwrites]

Harry Potter Rec Fest Day 13 - Over 100K

I so appreciate the dedication that it takes for someone to write over 100K words; I think even my M.A. thesis topped out at 30K (with much academic padding). I've read a ton of long fics so there's probably some recency bias here for @hprecfest day 13, but I've also tried to share a couple different pairings ...

Blood Magic (Podfic) written and read by houseofthehebrideanblcks and thestralsofspinnersend Pairing: Draco x Harry Word count: 334,676 Length: 33:05:11 Rating: E

@thistlecatfics already said all the things about this fic in their recommendation for the podfic the other day, and said it in a much lovelier and more eloquent way than I ever could. I guess I came at it from a different perspective though - as someone who is very lucky to feel not too traumatized by life or struggling with addiction or mental health (not a brag, just context), I got to see inside the brains of people like that and feel great empathy for them. Isn't that what art should do - put you in the shoes of different people and make you see the world differently? This is a slow, beautiful story of down-and-out Draco AND Harry heading toward something like peace and love, but it's a long road to get there. Also featuring endlessly patient counselor Luna, supportive Ron & Hermione, and lots of magical creatures.

The podfic is also really well done. The authors recorded it as a podcast originally. If the 33 hour single file on InternetArchive isn't really your speed, you can get the podcast version here, which includes their commentary after each chapter. Maybe not for everyone, but I found it interesting as a companion piece to hear their writing process and talk about trauma, treatment, and recovery in their own lives.

Choice and Chance by @chaoticcrumpets (Podfic by @etl-echo-audiobooks) Pairing: Draco x Hermione Word count: 116,972 Length: almost 10 hours Rating: M

This fic is so interesting. I had gotten a little burned out on Dramione before listening to this fic and this fully resparked my interest. I don't want to give too much away, but it's a mystery, a time travel adventure, and a romance. All the characterizations are very real, even though there might be more than one characterization of some of the characters. Can I use character more times in one sentence? Ah, and the twist at the end!!! *Chef's kiss*

Sweater Weather written by @lumosinlove (Podfic by @itsaash & cast) Pairing: Wolfstar, other adorable OC pairings and more! Word count: 156,108 Length: 15:23:00 Rating: E

I didn't think a hockey AU would be for me, even though I kinda like hockey. I was tempted by the prospect of Everyone Lives (TM) and I'm so grateful for it. This fic is beautiful, the development of all the characters, both canon and original, is incredible. I laughed, I cried, I gasped, I sighed, and when I was done, I wanted to start reading it all over again. At the time, it was newly finished and a sequel was on the way. I've been waiting for a nice vacation or sick day to reread it and its sequel, Vaincre. @lumosinlove has created a wonderful world and I just want to see the characters play around in it forever. I haven't listened to the podfic, but I'd encourage everyone to give it a shot!

************************************************************************

This one barely counts as a self-rec because I'm really just gushing about the author ...

Way Down We Go by @xiaq Podfic read by Cailynwrites (with @etl-echo-audiobooks) with beautiful album art by @abrilas-art Pairing: Draco x Harry Word count: 109,767 Length: 13 hours Rating: T

If you had asked me the ratings of my two out of three of my favorite novel-length Drarry fics yesterday, I wouldn't have thought the answer would be T, but apparently it is. This story, along with Away Childish Things, rewired my brain. I love the pace, I love the development of both boys' characters, I love the ancillary characters both old and new. It's just a perfectly told story. Although I should be encouraging you to go listen to my podfic on AO3 or Spotify, I can definitely also recommend the reading experience. Don't skip the adorable author's notes containing the adventures of the author's dog and grad school woes (relatable).

If you want to get a taste for the podfic before diving in, try this snippet or this one.

[govindhtech]

AI Frameworks Help Data Scientists For GenAI Survival

AI Frameworks: Crucial to the Success of GenAI

Develop Your AI Capabilities Now

You play a crucial part in the quickly growing field of generative artificial intelligence (GenAI) as a data scientist. Your proficiency in data analysis, modeling, and interpretation is still essential, even though platforms like Hugging Face and LangChain are at the forefront of AI research.

Although GenAI systems are capable of producing remarkable outcomes, they still mostly depend on clear, organized data and perceptive interpretation areas in which data scientists are highly skilled. You can direct GenAI models to produce more precise, useful predictions by applying your in-depth knowledge of data and statistical techniques. In order to ensure that GenAI systems are based on strong, data-driven foundations and can realize their full potential, your job as a data scientist is crucial. Here’s how to take the lead:

Data Quality Is Crucial

The effectiveness of even the most sophisticated GenAI models depends on the quality of the data they use. By guaranteeing that the data is relevant, AI tools like Pandas and Modin enable you to clean, preprocess, and manipulate large datasets.

Analysis and Interpretation of Exploratory Data

It is essential to comprehend the features and trends of the data before creating the models. Data and model outputs are visualized via a variety of data science frameworks, like Matplotlib and Seaborn, which aid developers in comprehending the data, selecting features, and interpreting the models.

Model Optimization and Evaluation

A variety of algorithms for model construction are offered by AI frameworks like scikit-learn, PyTorch, and TensorFlow. To improve models and their performance, they provide a range of techniques for cross-validation, hyperparameter optimization, and performance evaluation.

Model Deployment and Integration

Tools such as ONNX Runtime and MLflow help with cross-platform deployment and experimentation tracking. By guaranteeing that the models continue to function successfully in production, this helps the developers oversee their projects from start to finish.

Intel’s Optimized AI Frameworks and Tools

The technologies that developers are already familiar with in data analytics, machine learning, and deep learning (such as Modin, NumPy, scikit-learn, and PyTorch) can be used. For the many phases of the AI process, such as data preparation, model training, inference, and deployment, Intel has optimized the current AI tools and AI frameworks, which are based on a single, open, multiarchitecture, multivendor software platform called oneAPI programming model.

Data Engineering and Model Development:

To speed up end-to-end data science pipelines on Intel architecture, use Intel’s AI Tools, which include Python tools and frameworks like Modin, Intel Optimization for TensorFlow Optimizations, PyTorch Optimizations, IntelExtension for Scikit-learn, and XGBoost.

Optimization and Deployment

For CPU or GPU deployment, Intel Neural Compressor speeds up deep learning inference and minimizes model size. Models are optimized and deployed across several hardware platforms including Intel CPUs using the OpenVINO toolbox.

You may improve the performance of your Intel hardware platforms with the aid of these AI tools.

Library of Resources

Discover collection of excellent, professionally created, and thoughtfully selected resources that are centered on the core data science competencies that developers need. Exploring machine and deep learning AI frameworks.

What you will discover:

Use Modin to expedite the extract, transform, and load (ETL) process for enormous DataFrames and analyze massive datasets.

To improve speed on Intel hardware, use Intel’s optimized AI frameworks (such as Intel Optimization for XGBoost, Intel Extension for Scikit-learn, Intel Optimization for PyTorch, and Intel Optimization for TensorFlow).

Use Intel-optimized software on the most recent Intel platforms to implement and deploy AI workloads on Intel Tiber AI Cloud.

How to Begin

Frameworks for Data Engineering and Machine Learning

Step 1: View the Modin, Intel Extension for Scikit-learn, and Intel Optimization for XGBoost videos and read the introductory papers.

Modin: To achieve a quicker turnaround time overall, the video explains when to utilize Modin and how to apply Modin and Pandas judiciously. A quick start guide for Modin is also available for more in-depth information.

Scikit-learn Intel Extension: This tutorial gives you an overview of the extension, walks you through the code step-by-step, and explains how utilizing it might improve performance. A movie on accelerating silhouette machine learning techniques, PCA, and K-means clustering is also available.

Intel Optimization for XGBoost: This straightforward tutorial explains Intel Optimization for XGBoost and how to use Intel optimizations to enhance training and inference performance.

Step 2: Use Intel Tiber AI Cloud to create and develop machine learning workloads.

On Intel Tiber AI Cloud, this tutorial runs machine learning workloads with Modin, scikit-learn, and XGBoost.

Step 3: Use Modin and scikit-learn to create an end-to-end machine learning process using census data.

Run an end-to-end machine learning task using 1970–2010 US census data with this code sample. The code sample uses the Intel Extension for Scikit-learn module to analyze exploratory data using ridge regression and the Intel Distribution of Modin.

Deep Learning Frameworks

Step 4: Begin by watching the videos and reading the introduction papers for Intel’s PyTorch and TensorFlow optimizations.

Intel PyTorch Optimizations: Read the article to learn how to use the Intel Extension for PyTorch to accelerate your workloads for inference and training. Additionally, a brief video demonstrates how to use the addon to run PyTorch inference on an Intel Data Center GPU Flex Series.

Intel’s TensorFlow Optimizations: The article and video provide an overview of the Intel Extension for TensorFlow and demonstrate how to utilize it to accelerate your AI tasks.

Step 5: Use TensorFlow and PyTorch for AI on the Intel Tiber AI Cloud.

In this article, it show how to use PyTorch and TensorFlow on Intel Tiber AI Cloud to create and execute complicated AI workloads.

Step 6: Speed up LSTM text creation with Intel Extension for TensorFlow.

The Intel Extension for TensorFlow can speed up LSTM model training for text production.

Step 7: Use PyTorch and DialoGPT to create an interactive chat-generation model.

Discover how to use Hugging Face’s pretrained DialoGPT model to create an interactive chat model and how to use the Intel Extension for PyTorch to dynamically quantize the model.

Read more on Govindhtech.com

[enid-rhees]

Enemies to lovers trope with Enid! 🫶

hello !! tysm for requesting (and ur other reqs omg i’ll try to get to them as soon as i can) hope you enjoy! i’ve done an ETL fic with Enid before so i’m gonna try to do ETL HCS instead if that’s okay! 🫶🏻

okay so like

you don’t know why

but you and Enid HATED each others ass

whenever the opportunity was given — so many insults would be thrown back and forth between the two of you

Rick tried to stop it a few times but it never worked and he eventually gave up

ever since the day you arrived in Alexandria, Enid despised you

you didn’t want to hate her, she was the only other girl your age you’ve seen alive

but with the way she was, you had no other choice but to start acting the same way towards her

it got worse as months passed by and it was becoming a genuine problem

but one day

she got too close to you as she was yelling and you couldn’t help but stare at her lips the entire time

that night you couldn’t stop thinking of her

the next time you guys got into it again, there was visible tension that neither of you could ignore, but didn’t want to admit

you tried to force yourself to think it was stupid to think of her like that and you guys would never be anything more than enemies

but it only got worse

you eventually stopped trying so hard to get under her skin because you finally realized it was pointless

Enid took notice

the next time she found you, she taunted you endlessly, trying to break you

you grew more and more annoyed as she did

so you stood up and got in front of her

she expected you to start saying worse things than what she was saying

but you grabbed the collar of her shirt and connected your lips in a messy kiss

when she didn’t pull away and started to kiss back, you knew everything she would do to you was just an act

it progressed into a heavy make out session that lasted for longer than you could process

your group had started to question why they could no longer hear your arguments throughout the day and then Rick walked in your room and caught you guys kissing

all of Alexandria found out within 10 minutes

Enid confessed that she started it because she had found you attractive but didn’t want you to know

it’s now something you make fun of her ENDLESSLY for

[thedbahub]

SSIS on a Solo vs. a Dedicated SQL Server?

Pros and cons are like two sides of a coin, especially when we’re talking about where to run SQL Server Integration Services (SSIS). If you’re pondering whether to run SSIS on your sole SQL server or to go the extra mile and set it up on a dedicated server, let’s dive into the nitty-gritty to help you make an informed decision. Pros of Running SSIS on a Single SQL Server: Cost Savings: The most…

[mytechnoinfo]

The various ETL steps not only help to get new information, but also gives historical context as well. Take a look in detail regarding various ETL steps.

[mytalemyworld]

Dramas and couples that handled the tropes right

Enemies to lovers

Friends to lovers

Arranged marriage to love

I mostly pick Trdramas but this is what I do in this blog anyway.

Enemies to Lovers: Hilal & Leon (Vatanım Sensin - Turkish drama.), Zeynep & Kerem (Güneşi Beklerken - Turkish drama), Asi & Alaz (Yabani - Turkish drama) They also share the crown about the most favorite love confessions. Yep. I am always an etl fan girl.

Friends to Lovers: I can count Chinese or Korean dramas, they kind of deliver this trope every time. Qiao Yi & Yan Mo (Le Coup de Foudre - Chinese drama), Xiang Yuan & Yan Xi (Here We Meet Again - Chinese drama), Işık & Sinan (Love 101 - Turkish drama).

Arranged Marriage to Love: Usually seen as a rom-com trope but I love when used for angst and dramatic serious reasons for arranged marriage.

Songül & Sadi (Gelsin Hayat Bildiği Gibi - Turkish drama). He's an ex-mob, she's a police officer. He chooses to be a secret witness so the cops can catch the criminals red-handed and Ministry of the Interior gives him a new identity. She is tasked with protecting him. They act like they're husband and wife while he turns a new page.

And if I can also add some other similar tropes, like "Marriage Before Love" which is another way to tell they have to live under the same roof and know each other in the process, then I should say one of my favorites:

Sahra & Mithat (Sahra - Turkish drama). While everybody thinks she's dead, she creates a new foreign identity and comes back to Turkiye. She has to stay in the country to be able to get revenge on her ex husband and take her daughter away from him. She marries the male lead so she can acquire Turkish citizenship. The male lead already loves her, she falls in love with him along the journey.