Snowflake VS Databricks: How to Survive The Giants War
Riding the Waves of Disruption with the Right Know-How
The world of data is evolving at a breakneck pace, with Snowflake and Databricks leading the charge. As these giants battle for supremacy, it's easy to feel overwhelmed by the rapid changes and what they mean for you as a data professional.
The world of data is evolving at a breakneck pace, with Snowflake and Databricks leading the charge. As these giants battle for supremacy, it's easy to feel overwhelmed by the rapid changes and what they mean for you as a data professional.
But I'm here to tell you that this intense competition is a massive opportunity in disguise. The Snowflake-Databricks rivalry drives unprecedented innovation, creating a more powerful, flexible, and accessible data landscape.
I'll cut through the noise in this article and provide a clear guide to navigating this new world. You'll understand how these platforms shape the future of data and how you can leverage that knowledge to supercharge your projects and career.
Let's dive in together if you're ready to turn confusion into clarity and unlock the secrets to thriving in the era of Snowflake and Databricks.
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❄️ The Origins of Snowflake
I've been using Snowflake for 5 or 6 years, and, together with dbt, it was the best decision we made for our data stack.
Snowflake started as a cloud data warehousing solution, separating storage from computing. It also came with a robust network of plug-and-play partners.
Another key advantage Snowflake has historically had over its competitors has been its simplicity. You don't need to be a technical expert to get started, and the platform handles many details for you.
The one area Snowflake traditionally could have been better is real-time data flows. Overall, Snowflake offers incredible scalability and flexibility. This makes handling massive amounts of data easy without worrying about infrastructure management.
But here's the kicker: Snowflake's ease of use can also lead to unexpected costs if you are not careful enough.
🧱 The Rise of Databricks
Databricks, on the other hand, started as a company focused on developing and popularising open-source data tools. Founded by the creators of Apache Spark, Databricks has been at the forefront of innovation in the data space.
The company has developed and contributed to several key open-source projects, including:
Apache Spark: A fast and general-purpose distributed computing system for big data processing.
Delta Lake: The OG data lakehouse brings reliability and performance.
MLflow: An open-source platform for managing the end-to-end machine learning lifecycle.
By building and promoting these open-source tools, Databricks has empowered data professionals to handle complex, unstructured data and support advanced analytics and machine learning workloads. The company's commitment to open-source has also fostered a vibrant community of data professionals who benefit from these tools.
Databricks has developed a cloud-based platform that allows organisations to leverage these tools at scale. The platform provides a unified environment for data engineering, data science, and analytics, allowing teams to collaborate seamlessly on projects.
Databricks' key strength is its ability to handle the entire data lifecycle, from data ingestion and processing to machine learning and deployment. This end-to-end approach, coupled with the power of open-source tools, has made Databricks a popular choice for organisations looking to build sophisticated data and AI solutions.
Even I, a strong Snowflake proponent, had considered adding Databricks to our stack to unlock greater data science capabilities.
A traditional downside of Databricks is its complexity. It has always required some infrastructure knowledge, and combining it with Spark's steep learning curve makes finding people a bit more complicated.
⚖️ Convergence: More Similarities Than Differences
As Snowflake and Databricks have evolved, they've converged in functionality, user experience, and pricing.
Databricks now offers serverless options and better ACID queries. Meanwhile, Snowflake has begun to support Apache Iceberg and added Snowpark for data science needs.
This convergence is further exemplified by recent acquisitions and product developments:
Databricks acquired Tabular, founded by the original Iceberg developers.
Snowflake started eating its partners' businesses by acquiring them or building their own solutions.
With Snowpark and Iceberg, the lines between Snowflake and Databricks are blurring even further.
What does this mean for you? Confusion!
It will be even more complicated to pick your vendor. As the two platforms become more similar, your decision will mostly depend on these companies' salespeople.
🏆 The Real Winners: Data Professionals
The intense competition between Snowflake and Databricks means better platforms, more innovation, and a comprehensive range of options. One key benefit of this rivalry is it has forced both companies to prioritise user experience and flexibility.
Databricks' CEO, Ali Ghodsi, said, "Don't give us your data." Databricks open-sourced their Unity Catalog, and Snowflake's Polaris announcement includes writing to your Iceberg storage.
This means you have complete flexibility. You can start quickly by using whatever your service of choice provides, but you can also use your own compute, storage, or code runtime when needed.
Another advantage of the Snowflake-Databricks rivalry is it has accelerated innovation in the data space. As each company tries to outdo the other, they must develop new features, improve performance, and lower costs.
This competition drives the entire industry forward, benefiting data professionals and organisations of all sizes.
🔮 What The Stars Are Talking To Me
What do you think will happen in the next few years? Here are my two most significant predictions:
Modern Data Stack 2.0 😎
The future of data storage and processing is modular. There is no turning back!
Nobody likes being locked with a specific vendor and paying whatever the vendor tells them. Roy Hasson mentioned in his LinkedIn post the future might lie in monetising separate components. This statement strongly resonates with me.
The whole concept of the modern data stack (apparently, this is no longer a term) is based on interchangeable components. And this trend is only going to accelerate.
Companies desire to choose which components to use, and vendors will be forced to comply. This will lead to a new era of the data stack, where individual components can be easily swapped in and out based on an organisation's specific needs and preferences.
Of course, vendors like Snowflake and Databricks will continue expanding their gigantic services and providing an easy way to use their all-in-one platforms. However, seeing customers seamlessly hook components from different competitors into their data pipelines will become increasingly common.
This modular approach will give organisations unprecedented flexibility and control over their data infrastructure. Companies will build data stacks perfectly tailored to their unique requirements by avoiding vendor lock-in and choosing best-of-breed components.
As the data landscape evolves, the ability to mix and match components from different vendors will become critical in driving innovation and enabling organisations to stay ahead of the curve.
The future of data is modular, interchangeable, and free from vendor lock-in.
This is the Modern Data Stack 2.0!
The Raise of the Small Players 🙌
While Snowflake and Databricks will likely dominate the market for the next few years, there is still room for other players. In fact, the success of these two giants is likely to create opportunities for smaller, more specialised and innovative vendors.
As the big players focus on serving the needs of large enterprises, they may struggle to cater to the unique requirements of smaller organisations or niche industries. This creates a gap in the market that smaller, more agile and focused vendors can fill.
These tools are well-positioned to thrive in this environment. Newer companies can concentrate on specific use cases, innovate rapidly, and provide tailored solutions meeting the needs of their target customers.
For example, ClickHouse is known for its exceptional performance and scalability in real-time analytics and time series data. Dremio, on the other hand, excels at simplifying data access and governance across multiple sources. DuckDB is a robust in-process database ideal for analytical workloads and embedded analytics.
By focusing on their strengths and carving out a niche in the market, these specialised tools can attract customers looking for alternatives to the big players. Small and medium-sized businesses, in particular, may prefer to work with these more focused vendors to avoid the complexity and cost of massive all-in-one platforms.
Moreover, as the data landscape becomes increasingly modular and interoperable, these specialised tools can easily integrate with other components of the modern data stack. This allows organisations to build best-of-breed data pipelines leveraging the strengths of different vendors and technologies.
In the end, the rise of Snowflake and Databricks is not just a story of two giants battling for dominance. It's also a tale of how their success creates opportunities for a diverse ecosystem of specialised players to thrive and innovate.
As the market evolves, I expect to see a vibrant mix of large platforms and niche tools, all working together to meet the diverse needs of data-driven organisations.
🥷 Surviving and Thriving the Giants
So, how do you navigate this ever-evolving landscape?
Learn Core Concepts 🧑🎓
Lakehouse vs warehouse storage, how queries are executed, and other details. The better you understand the fundamentals, the easier it will be to evaluate different platforms and make informed decisions.
Explore and Compare Features Across Platforms 🧭
While you might spend most of your time with one solution, find time to explore other tools and spot exciting features. This will help you stay up-to-date with the latest developments and ensure you use the best tools for your needs.
Stay Open to Change 📈
The data landscape constantly evolves, and better solutions might exist than what works today. Embrace the opportunity to learn and adapt, and you'll be well-positioned to thrive in this exciting field.
🏁 Summary
The rivalry between Snowflake and Databricks drives rapid innovation, forces each other to improve and adapt, and ultimately provides better options for you. As a data professional, your job is to learn and explore continuously.
The data industry has an exciting future ahead, and you get to be a part of it! You can thrive in this dynamic landscape by staying informed, experimenting with new tools, and being open to change.
In summary, the Snowflake-Databricks war is a thrilling tale of two data giants locked in an epic battle for supremacy. You emerge as the victor as they push each other to new heights.
With a wealth of options, flexibility, and innovation at your fingertips, you have the power to shape the future of data management. So, arm yourself with knowledge, embrace the excitement, and get ready to embark on an unforgettable journey through the world of cloud data platforms!
What's your take on the Snowflake vs Databricks showdown? Have you tried both platforms? What features do you love?
Share your thoughts, and let's keep the conversation going.
Until next time,
Yordan
📚 Picks of the Week
In the spirit of data storage solutions, let’s take a look at Ihor Lukianov’s overview of data management history. (link)
I love Apache Druid and enjoyed this article about Druid’s architecture from
. (link)It is a mid-year performance review time at my workplace. At this point, nothing should be a surprise.
wrote an outstanding piece about fostering a good feedback culture. (link)
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Thank yoy for the mention, Yordan!