#Difference between Databricks and Snowflake

[databricksmigration]

Difference between Databricks and Snowflake

Databricks and Snowflake are two different technologies that are often used together for data analysis and processing.

Databricks is a cloud-based data processing platform that provides tools for data engineering, data science, and machine learning. It is based on Apache Spark, an open-source distributed computing framework that can process large amounts of data in parallel across multiple computers. Databricks provides a unified workspace for data engineers and data scientists to collaborate and work with data, using a combination of programming languages like Python, R, and SQL.

Snowflake, on the other hand, is a cloud-based data warehousing platform that provides a scalable and secure solution for storing and analyzing large amounts of data. Snowflake uses a unique architecture that separates storage and computing, which allows users to scale up or down their compute resources as needed, without affecting the underlying data. Snowflake also provides a SQL-based interface for querying data, and it supports various BI tools for data visualization and reporting.

In summary, Databricks and Snowflake are both cloud-based technologies for data processing and analysis, but they serve different purposes. Databricks is more focused on data engineering, data science, and machine learning, while Snowflake is more focused on data warehousing and analytics. However, they can be used together to build end-to-end data solutions that can handle large amounts of data at scale. For more details, contact at https://celebaltech.com/significance-of-databricks

[getondata]

Databricks vs Snowflake: Key Differences & Insights

Discover the key differences between Databricks and Snowflake. Explore features, pricing, performance, and which platform best suits your data analytics needs. Read our in-depth comparison now!

[nitor-infotech]

Databricks vs. Snowflake: Key Differences Explained

What if businesses could overcome the challenges of data silos, slow query performance, and limited real-time analytics? Well, it's a reality now, as data cloud platforms like Databricks and Snowflake have transformed how organizations manage and analyze their data. 

Founded in 2012, Snowflake emerged from the expertise of data warehousing professionals, establishing itself as a SQL-centric solution for modern data needs. In contrast, Databricks, launched shortly after in 2013, originated from the creators of Apache Spark, positioning itself as a managed service for big data processing and machine learning. 

Scroll ahead to discover everything about these platforms and opt for the best option. 

Benefits of Databricks and Snowflake 

Here are the benefits that you can enjoy with Databricks: 

It has been tailored for data science and machine learning workloads. 

It supports complex data transformations and real-time analytics. 

It adapts to the needs of data engineers and scientists. 

It enables teams to work together on projects, enhancing innovation and efficiency. 

It allows for immediate insights and data-driven decision-making. 

In contrast, here are the benefits you can experience with Snowflake: 

It is ideal for organizations focused on business intelligence and analytics. 

It helps with storage and the compute resources can be scaled separately, ensuring optimal performance. 

It efficiently handles large volumes of data without performance issues. 

It is easy to use for both technical and non-technical users, promoting widespread adoption. 

It offers a wide range of functionalities to support various industry needs. 

Note: Visit their website to learn more about the pricing of Databricks and Snowflake. 

Now, let’s compare each of the platforms based on various use cases/features. 

Databricks vs. Snowflake: Comparison of Essential Features  

When comparing essential features, several use cases highlight the differences between Databricks and Snowflake. Here are the top four factors that will provide clarity on each platform's strengths and capabilities: 

1. Data Ingestion: Snowflake utilizes the ‘COPY INTO’ command for data loading, often relying on third-party tools for ingestion. In contrast, Databricks enables direct interaction with data in cloud storage, providing more flexibility in handling various data formats. 

2. Data Transformation: Snowflake predominantly uses SQL for data transformations, while Databricks leverages Spark, allowing for more extensive customization and the ability to handle massive datasets effectively. 

3. Machine Learning: Databricks boasts of a mature ecosystem for machine learning with features like MLflow and model serving. On the other hand, Snowflake is catching up with the introduction of Snowpark, allowing users to run machine learning models within its environment. 

4. Data Governance: Snowflake provides extensive metadata and cost management features, while Databricks offers a robust data catalog through its Unity Catalog (it is still developing its cost management capabilities). 

In a nutshell, both Databricks and Snowflake have carved their niches in the data cloud landscape, each with its unique capabilities. As both platforms continue to evolve and expand their feature sets, the above read will help businesses make informed decisions to optimize their data strategies and achieve greater insights. 

Feel free to share this microblog with your network and connect with us at Nitor Infotech to elevate your business through cutting-edge technologies. 

[govindhtech]

Dell AI Generator: Ready Data Platform for AI Innovations

Dell Technologies‘ DNA is centered on innovation, and its AI and industry experts are committed to supporting you at every turn. The powerful characteristics of the Dell AI generator-Ready Data Platform, which are at the forefront of enabling new generative AI (GenAI) application cases, excite them. They just announced a lot of significant developments at the Data Everywhere vision event, where they unveiled Dell AI generator Anywhere.

Imagining the Contemporary Data Lakehouse The open, contemporary data lakehouse that Dell envisions helps businesses overcome the growing difficulties they have in keeping up with the rapid expansion of data. The requirement for flexibility is more important than ever because of the dispersion of data across several contexts, the multiplicity of data sources, and the ever-growing array of formats and technologies available. Conventional data lakehouses fall short of these changing requirements, often because of architectural constraints. They often impede interoperability by requiring needless data migration, working against the natural flow of your data, and locking you into proprietary data formats.

They take a different approach, one that supports the usage of open table formats like Iceberg and Delta Lake, facilitates data discovery and querying by permitting in-place actions, and is in line with your data gravity. This strategy guarantees independence from vendor lock-in, promoting an environment for data that is more transparent and adaptable.

The collaboration between Dell and Starburst is a big step in the direction of achieving this goal. Together, Starburst’s capabilities and Dell’s top-tier processing and storage technologies are creating the foundation for a scalable, high-performance lakehouse. This foundation further delivers on Dell’s multicloud vision by extending from the edge to the core and cloud.

Highlights of Open Ecosystem of Partners Dell clients are embracing the whole data environment, realizing that the variety of data toolsets is matched by the diversity of their aims. Thanks to Dell’s collaborations with top data companies like Databricks, Snowflake, Teradata, and Cloudera, this is made possible. The goal of the Dell strategy is to create an open ecosystem that encompasses a wider range of tools and data sources across Dell AI generator processes, rather of focusing just on producing Dell solutions. Here are a few noteworthy recent partnerships:

They have unveiled a revolutionary reference architecture for cloud-native AI workflows, and they are combining Dell APEX File Storage with the Databricks and Mosaic ML AI toolkits to provide a dynamic, cloud-native experience. The Dell vision of AI Anywhere on Data Everywhere is supported by this program.

They have launched improved ecosystems and platforms with Teradata that are centered on active archives and data warehousing. These solutions, which were introduced at the beginning of the year, combine Teradata’s software with Dell’s hardware knowledge and have had positive comments from customers.

Dell AI generator Optimisation in Multicloud Environments They acknowledge that the vast majority of businesses nearly 87% are using multicloud strategies in today’s quickly changing digital world as a result of distributed data. This development emphasizes how important data adaptability is, whether it’s in different cloud environments, on-premises, or at the edge. In order to solve this, they provide you the flexibility to handle and store your data wherever it is most useful for your AI use case. This guarantees efficiency, scalability, and easy access to your data no matter where it is stored.

By launching the PowerScale OneFS scale-out software for public cloud last year, they made a big advancement and expanded Dell’s already strong multicloud offering. Building on this success, they are thrilled to share the news of a significant addition to the Dell cloud portfolio: Dell APEX File Storage for Microsoft Azure, a product of a strategic collaboration with Microsoft. Customers may use Dell’s software-defined file services in Azure’s cloud thanks to this partnership. What’s more, the solution works flawlessly with Azure AI Vision and OpenAI, two of Microsoft’s AI services. APEX File Storage for Microsoft Azure keeps on Dell’s customer commitment to provide clients flexibility so they can use AI to develop more quickly.

Providing AI-Ready Results They are excited to discuss how they have improved Dell PowerScale storage to make sure your AI processes operate more quickly and allow you to provide insights for your company. They have significantly improved PowerScale’s hardware and software with the goal of reducing interruptions and maximizing efficiency for AI workloads. Here are a few standouts:

With this achievement, they will become the first storage supplier to get NVIDIA Ethernet SuperPOD certification, demonstrating Dell’s unwavering dedication to cutting-edge innovation and quality.

Another significant release from Dell is the OneFS 9.7 software upgrade, which provides an 80% increase in storage performance. Current Dell PowerScale customers may take advantage of this innovation, which is essential for speeding up GenAI operations, from model training to fine-tuning.

Recently, Dell unveiled its latest all-flash storage nodes, built with Dell AI generator in mind. By decreasing idle time and accelerating data processing, these nodes greatly increase GPU efficiency. They also provide non-disruptive scalability and up to two times the performance in streaming reads and writes. Furthermore, the cutting-edge smart client technology from Dell enhances GPU client performance by dynamically allocating traffic, allowing a single client to stream data at a rate of more than 40 GB per second.

Towards the Future: Innovations in 2024 In 2024, there will be exciting advancements in Dell AI generator, indicating a bright future. For a preview of what’s to come, they cordially encourage you to see the Dell AI generator Anywhere on Data Everywhere event replay. When you start your AI journey with Dell, a committed team of Dell AI generator and industry experts will be at your side from the beginning. They are dedicated to turning your AI goals into observable outcomes, from strategy development to data preparation and implementation. They’re eager to learn more about your applications of Dell AI generator!

Read more on Govindhtech.com

[rajaniesh]

Databricks vs Snowflake: Which platform is best for you?

As more and more companies turn to the cloud for their data processing needs, choosing the right platform can be a crucial decision. Two of the most popular cloud-based data platforms are Snowflake and Databricks, and understanding the differences between them can be challenging. However, by closely examining the features and advantages of each platform, you can make an informed decision about…

[hostor-infotech]

Onehouse is building a neutral data lake integration layer on top of Apache Hudi • TechCrunch

Onehouse emerged last year with a cloud data lake product built on top of the open source Apache Hudi project. The startup wants to act as an integration layer to move data between different repositories, rather than competing directly with larger data lake vendors like Snowflake and Databricks. Today the company announced a $25 million Series A. Company founder and CEO Vinoth Chandar came up…

[un-enfant-immature]

Would the math work if Databricks were valued at $38B?

Databricks, the open-source data lake and data management powerhouse has been on quite a financial run lately. Today Bloomberg reported the company could be raising a new round worth at least $1.5 billion at an otherworldly $38 billion valuation. That price tag is up $10 billion from its last fundraise in February when it snagged $1 billion at a $28 billion valuation.

Databricks declined to comment on the Bloomberg post and its possible new valuation.

The company has been growing like gangbusters, giving credence to the investor thesis that the more your startup makes, the more it is likely to make. Consider that Databricks closed 2020 with $425 million in annual recurring revenue, which in itself was up 75% from the previous year.

As revenue goes up so does valuation, and Databricks is a great example of that rule in action. In October 2019, the company raised $400 million at a seemingly modest $6.2 billion valuation (if a valuation like that can be called modest). By February 2021, that had ballooned to $28 billion, and today it could be up to $38 billion if that rumor turns out to be true.

Databricks raises $1B at $28B valuation as it reaches $425M ARR

One of the reasons that Databricks is doing so well is it operates on a consumption model. The more data you move through the Databricks product family, the more money it makes, and with data exploding, it’s doing quite well, thank you very much.

It’s worth noting that Databricks’s primary competitor, Snowflake went public last year and has a market cap of almost $83 billion. In that context, the new figure doesn’t feel quite so outrageous, But what does it mean in terms of revenue to warrant a valuation like that. Let’s find out.

Valuation math

Let’s rewind the clock and observe the company’s recent valuation marks and various revenue results at different points in time:

Q3 2019: $200 million run rate, $6.2 billion valuation

Q3 2020: $350 million run rate, no known valuation change

EoY 2020: $425 million run rate, $28 billion valuation (Q1 valuation)

Q3 2021: Unclear run rate, possible $38 billion valuation

The company’s 2019 venture round gave Databricks a 31x run rate multiple. By the first quarter of 2021, that had swelled to a roughly 66x multiple if we compare its final 2020 revenue pace to its then-fresh valuation. Certainly software multiples were higher at the start of 2021 than they were in late 2019, but Databricks’s $28 billion valuation was still more than impressive; investors were betting on the company like it was going to be a key breakout winner, and a technology company that would go public eventually in a big way.

To see the company possibly raise more funds would therefore not be surprising. Presumably the company has had a good few quarters since its last round, given its history of revenue accretion. And there’s only more money available today for growing software companies than before.

But what to make of the $38 billion figure? If Databricks merely held onto its early 2021 run rate multiple, the company would need to have reached a roughly $575 million run rate, give or take. That would work out to around 36% growth in the last two-and-a-bit quarters. That works out to less than $75 million in new run rate per quarter since the end of 2020.

Is that possible? Yeah. The company added $75 million in run rate between Q3 2020 and the end of the year. So you can back-of-the-envelope the company’s growth to make a $38 billion valuation somewhat reasonable at a flat multiple. (There’s some fuzz in all of our numbers, as we are discussing rough timelines from the company; we’ll be able to go back and do more precise math once we get the Databricks S-1 filing in due time.)

Databricks co-founder and CEO Ali Ghodsi is coming to TC Sessions: SaaS

All this raises the question of whether Databricks should be able to command such a high multiple. There’s some precedent. Recently, public software company Monday.com has a run rate multiple north of 50x, for example. It earned that mark on the back of a strong first quarter as a public company.

Databricks securing a higher multiple while private is not crazy, though we wonder if the data-focused company is managing a similar growth rate. Monday.com grew 94% on a year-over-year basis in its most recent quarter.

All this is to say that you can make the math shake out for Databricks to raise at a $38 billion valuation, but built into that price is quite a lot of anticipated growth. Top quartile public software companies today trade for around 23x their forward revenues, and around 27x their present-day revenues, per Bessemer. To defend its possible new valuation when public, then, leaves quite a lot of work ahead of Databricks.

The company’s CEO, Ali Ghodsi, will join us at TC Sessions: SaaS on October 27th, and we should know by then if this rumor is, indeed true. Either way, you can be sure we are going to ask him about it.

Why one Databricks investor thinks the company may be undervalued

[techcrunchappcom]

New Post has been published on https://techcrunchapp.com/is-wall-street-losing-its-tech-enthusiasm-breaking-news-tech-news-celebrity-news-bussiness-and-finance-news/

Is Wall Street losing its tech enthusiasm? – Breaking news, Tech news, Celebrity News, Bussiness and Finance News

This is The TechCrunch Exchange, a newsletter that goes out on Saturdays, based on the column of the same name. You can sign up for the email here. Over the past few months the IPO market made it plain that some public investors were willing to pay more for growth-focused technology shares than private investors. We saw this in both strong tech IPO pricing — the value set on companies as they debut — and in resulting first-day valuations, which were often higher. One way to consider how far public valuations rose for tech startups, especially those with a software core in 2020, is to ask yourself how often you heard about a down IPO this year. Maybe a single time? At most? (You can catch up on 2020 IPO performance here, if you need to.) IPO enthusiasm exposed a gap between what many venture capitalists and private investors were paying for tech shares, and what the public market was doing with its own valuation calculations. Insurtech startup Hippo’s $150 million private round from July is a good example. The company was valued at $1.5 billion in the round, a healthy uptick from its preceding private valuation. But if we valued it like the then-newly-public Lemonade, a related company, at the time, Hippo was priced inexpensively. This week, however, the concept of private investors being more conservative than public investors in certain cases (some eight-figure private rounds happened this year at valuations that were even more bullish than public investor treatment of IPOs, to be clear) took a ding as most big tech companies lost ground, SaaS stocks sold off, and other tech firms struggled to keep up with investor enthusiasm. Not only tech companies took a beating, but as I write to you on this Friday afternoon, the American stock markets were on a path for their worst week since March, CNBC reported, “led by major tech shares.” A change in the wind? Perhaps.  Notable is that it was just in September that VCs seemed resigned to having startup valuations pulled higher by public markets’ endless optimism for related companies. Canaan’s Maha Ibrahim told me during Disrupt 2020 that it was a time when VCs had to “play the game” and pay up for startups, so long as companies were being “rewarded in the public markets for high growth the way that Snowflake” was at the time. A16z’s David Ulevitch concurred. Perhaps that dynamic is changing as stocks dip. If so, startup valuations could decline en masse, along with the more exotic areas of startup-related finance. The SPAC boom, for example, may wane. Chatting with Hippo’s CEO Assaf Wand this week, he posited that SPACs were a market-response to the public-private valuation gap, an accelerant-cum-bridge to help startups get public while demand was hot for their equity. Without the same red-hot demand for growth and risk, SPACs could cool. So, too, could private valuations that the hottest startups have taken for granted. Whether what we’re feeling in the wind this week is a hiccup or tipping point is not clear. But the public market’s fever for tech equities may have broken at a somewhat awkward time for Airbnb, Coinbase, DoorDash and other not-quite-yet-IPOs. Market Notes It started to snow this week where I live, putting a somewhat sad cap on an otherwise turbulent week. Still! There’s lots from our world to get into. Here’s our week’s market notes:

Remember when we dug into how quickly startups grew in Q3? Another company that I’ve covered before, Drift, wrote in. The Boston-based marketing software company reported to The Exchange that it grew more than 50% in Q3 compared to the year-ago quarter, with its CEO adding that June and Q3 were the strongest month and three-month periods in its history. The fintech boom continued with DriveWealth raising nearly $57 million this week, with the startup being yet another API-driven play. That a company sitting in-between two key startup trends of the year is doing well is not surprising. DriveWealth helps other fintech companies provide users access to the American equities markets. Alpaca, which also recently raised, is working along similar lines.

This week featured two IPOs that we cared about. MediaAlpha’s debut, giving the advertising-and-insurtech company a $19 per-share IPO price, quickly exploded out of the gate. Today the company is worth nearly $38 per share. Why? On its IPO day MediaAlpha CEO Steve Yi said that he had chosen the current moment because public markets had garnered an appreciation for insurtech. His share price growth seems to concur. Until we look at Root, to some degree. Root, a neo-insurance provider focused on the automotive space, priced at $27 when it debuted this week, $2 above the top-end of its range. The company is now worth less than $24 per share. So, whatever wave MediaAlpha caught appears to have missed Root.  I honestly don’t know what to make of the difference in the two debuts, but please email in if you do know (you can just reply to this email, and I’ll get your note). Regardless, I chatted with Root CEO Alex Timm after his company went public. The executive said that Root had laid down plans to go public a year ago, and that it can’t control market noise around the time of its debut. Timm stressed the amount of capital that Root added to its coffers — north of $1 billion — is a win. I asked how the company intended to not fuck up its newly swollen accounts, to which Timm said that his company was going to stay “laser focused” on its core automotive insurance opportunity. Oh, and Root is based in Ohio. I asked what its debut might mean for Midwest startups. Timm was positive, saying that the IPO could highlight that there are a lot of smart folks and GDP in the middle of the country, even if venture capital tallies for the region remain underdeveloped.

I know that by now you are tired of earnings, but Five9 did something that other companies struggled to accomplish, namely, beat expectations and bolstered its forward guidance. Its shares soared. The Exchange got on the phone with the call center software company to chat about its latest acquisition and earnings. How did it crush expectations as it did? By selling a product that its market needed when COVID-19 hit, the accelerating digital transformation more broadly, and rising e-commerce spend, which is driving more customer support work onto phone lines, it said. A lot of stuff at once, in other words.  Five9 took on a bunch of convertible debt earlier this year, despite making gobs of adjusted profit. I asked its CEO Rowan Trollope how he was going to go about investing cash to take advantage of market tailwinds, while not overspending. He said that the company takes very regular looks at revenue performance, helping it tailor new spend nimbly. It’s apparently working. What else? Peek this week at big, important rounds from SimilarWeb, PrimaryBid and EightFold, a company that I have known for some time. Oh, and I covered The Wanderlust Group’s Series B and Teampay’s Series A extension, which were good fun.

Various and Sundry

What’s going on in the world of venture debt as VC gets back to form? We dug in. For the Europhiles amongst us, here’s what’s up with the continent’s VC receipts. Here are 10 favorites from recent Techstars demo days. And here’s some mathmagic about Databricks, after it was rumored to have an H1 2021 IPO target. We’re way out of space this week, but I have some fun stuff in the tank for later, including a Capital G investor’s take on RPA, a call with the CEO of Zapier about no-code/low-code growth and notes from a chat about developer ecosystems with Dell Capital. More on all of that when the news calms down.

Stay safe, and vote. Alex

Related

[databricksmigration]

contact https://celebaltech.com/significance-of-databricks