Let’s face it: Data Warehouses, Data Lakes, and Data Lakehouses can be a bit like that treadmill gathering dust in the corner if not designed and managed properly – expensive, intimidating, and not exactly known for their agility. But what if there was a cloud data platform that could scale up and down quickly and cost-efficiently to revolutionise your data game? Here, we’ll explore the Snowflake Data Lakehouse, how it compares to other leading data platforms and why it’s worth a look if you’re considering a data warehouse, data lake or, the best of both, a Data Lakehouse.
Scaling with traditional data warehouses can be as enjoyable as untangling fairy lights. You’re either stuck with buying or reserving massive computing power upfront, risking wasted resources if your data needs exceed expectations, or you’re left playing the guessing game. This involves constantly adjusting capacity in the hopes of avoiding a data apocalypse (or, worse, an overflowing spreadsheet).
Snowflake revolutionises this outdated approach with its Data Cloud design, offering elasticity that can go from zero to what you need near-instantaneously. Picture this: you need to run a complex analysis, but your current warehouse is sitting in standby mode. With a Snowflake Data Lakehouse, your virtual warehouse is up and running in milliseconds, not minutes, as with many others. And if you need it to disappear after the job’s done, no worries! Snowflake will automatically pause the warehouse after just 5 minutes (or more, as configured) of inactivity, saving you precious dollars. This seemingly small feature translates into significant performance gains, improved development experience, enhanced user experience when interacting with the data platform, and the agility to transform your data into powerful insights. But Snowflake doesn’t stop there. It also offers a unique pay-as-you-go pricing model, ensuring that you only pay for the computing power you actually use and need, further optimising your costs.
Now, let’s talk about those inevitable spikes in data usage. We’ve all been there – your trusty warehouse starts chugging along when you urgently need it the most. A Snowflake Data Lakehouse has automatic horizontal scaling (meaning adding more workers to get the job done) that kicks in seamlessly, adding more processing power based on workload requests to avoid this type of problem. But here’s the real kicker: with Snowflake, you don’t just rely on your own resources. The platform can also tap into idle computing power outside your Snowflake account. It’s like borrowing a bit of sugar from the neighbour – everyone wins.
It’s not just about throwing data at a problem however, it’s about empowering you to analyse it efficiently and cost effectively. Query acceleration helps you handle unexpected data surges, while features like automatic clustering, partitioning and indexing keep your data organised for optimal performance not requiring any manual intervention like the other warehouses. It’s like having a data butler – always there to anticipate your needs and ensure everything runs smoothly.
The Snowflake Data Lakehouse also brings some other cool features to the table that are worth mentioning:
- SaaS: As a Software-as-a-Service platform, you don’t need to manage the underlying infrastructure and can focus on what matters most.
- Multi-cloud: Snowflake can be deployed on the major cloud providers (Azure, AWS, and GCP) in various regions.
- Data Sharing: Collaborate by securely sharing data within or across organisations. This process uses zero passwords, so there is no need for password management.
- Quick Data Cloning: Instantly clone data for testing, development, sandboxes, and proof of concepts (PoCs). Zero-copy clones are a game changer for the development and innovation lifecycle.
- Integration with BI, ETL, and Visualisation Tools: Seamlessly connect with popular tools that you may already be using on major cloud providers (AWS, Azure, Google Cloud).
- Fully Managed Table Format: Snowflake provides a managed table format, simplifying data organisation.
- Apache Iceberg Table Format: Leverage the power of Iceberg for efficient data management.
- Polyglot, Multi-Cluster Compute Engine: Execute queries using different languages and clusters such as SQL, Python, Java, Scala, etc.
- Cost-Effective Performance for High Concurrency: Achieve scalability without breaking the bank.
How Does Snowflake Compare To Other Cloud Data Platforms?
Now, you might be thinking, “This all sounds too good to be true. Surely there’s a catch?” Well, there’s always room for improvement, but Snowflake comes pretty darn close to Data Lakehouse nirvana. Having worked with most of the leading cloud data platforms, here’s a quick comparison to some of the other options out there:
- The Old-School Data Warehouses: Remember that treadmill? Yeah, that’s what using a traditional on-premises data warehouse (sometimes even a transactional database) for scaling feels like. Inflexible, manual, expensive, and a guaranteed path to data challenges, which is why modern cloud data platforms are now so popular.
- Databricks: Databricks unites data lakes and warehouses, providing support for structured, semi-structured, and unstructured data. It flits between clouds (AWS, Azure, and Google) in a Platform as a Service (PaaS) offering, requiring you to manage the cloud components. The engine start-up time is at a multi-minute scale.
- Microsoft Synapse: Synapse Analytics provides a leading consolidated Microsoft offering with serverless or dedicated warehouse options and even a spark lake engine. However, the dedicated pool is always on and does not scale automatically. In summary, it’s technology is being replaced by Fabric, the next one on our list.
- Microsoft Fabric: Microsoft’s new offering, Microsoft Fabric, stitches together multiple existing tools, but those are being reframed to a more cloud-native approach. It’s like a data kaleidoscope; each shard has a different dimension. It’s a great platform, but it’s still in its very early stages of adoption and maturity so capacity scalability is still manual.
You’re not going to go wrong if considering any of these leading cloud data platforms, however if scalability and cost efficiency are important, then Snowflake should be high on the list. If you need assistance evaluating a new cloud data platform or enhancing what you already have, then our team at BoomData can help. We adhere to a number of best practices and have developed unique Data Platform Accelerators and Frameworks that ensure tasks are automated, repeatable, reliable and consistent to set you up for success.
