Why a Snowflake Computing Warehouse Should Be Part of Your Next Data Platform
A look into why a cloud-native Snowflake warehouse may be a fit for data-driven teams looking at modern data platform options.
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The traction for serverless services, including data warehouses, has gained momentum over the past couple of years for big data and small data alike. Scalable performance, along with removing set up or management of infrastructure, has proven attractive. Also, a model of just paying for “run-time” resources is equally attractive.
Why Snowflake Warehouse?
When we find products that embrace zero data management, data warehouse-as-a-service, we are in. This is why we have taken a closer look at Snowflake Computing. Building on this industry serverless trend is the data warehouse offering from Snowflake Computing. Here is how Snowflake Computing describes their product:
Snowflake Data Warehouse is a single integrated system with fully independent scaling for compute, storage, and services. Unlike shared-storage architectures that tie storage and compute together, Snowflake enables automatic scaling of storage, analytics, or workgroup resources for any job, instantly and easily.
If you are new to serverless warehouses in general and Snowflake in particular, we compiled eight reasons to consider the unique architecture of their virtual warehouse.
1. Ease of Use
Snowflake is recognized for an interface that is simple to use and intuitive. You can get started with the service quickly and you can automatically or on the fly, spin up and down compute clusters of any size for any user or workload without impacting other jobs. With near-infinite and instant elasticity and concurrency, Snowflake promises to deliver the performance your organization needs.
2. Fully Automated, Zero Administration
No need to worry about configuration, software updates, failures or scaling your infrastructure as your datasets and number of users grow with Snowflake. This will free your team to focus on use cases of data consumption via preferred data tools, not the underlying system development. Also, using a data lake and pipelines provide an automated, standards-driven foundation to fuel Snowflake.
Pricing is based on the amount of data you store and the compute hours you use. This cost-optimized, “on-demand” solution ensures data is at the ready for analysis, reporting or other business activities without incurring “idle” system costs. Compute resources and prices are separate from data storage costs. When using Snowflake with a data lake, you realize significant end-to-end cost savings by compressing, partitioning, and converting your data to Apache Parquet prior to loading into Snowflake.
You don’t have to worry about managing, scaling multi-cluster systems, or tuning clusters to get fast performance. For example, Snowflake includes automatic query optimization. They state that there are “no indexes, no need to figure out partitions and partition keys, no need to pre-shard any data for distribution, and no need to remember to update statistics.”
Snowflake, combined with a data lake, offers unparalleled flexibility and value. You also get the flexibility to use Snowflake while having the flexibility to leverage warehouse (Redshift Spectrum) and query services (Amazon Athena) on the same data lake. When you have options, you use Snowflake because you want to, not because you have to.
Snowflake, combined with a data lake, ensures your data is highly available and durable on Amazon S3 (or Azure). Your data is redundantly stored across multiple facilities and multiple devices in each facility, including structured data lakes.
8. Encryption and Security
You have full control over who has access to the data stored in Snowflake. They make it easy to maintain strong security with access management controls, plus data is encrypted at rest and in transit.
Should You Consider Snowflake Warehouse?
The pay-for-usage pricing model is attractive, especially for teams who thought the power of this kind of querying system was out of their budget or required complex systems and DevOps support.
A number of sophisticated BI tools support Snowflake. Is your preferred tool on that list? There are a number of contenders in the market like Tableau, Looker, Mode Analytics, Chartio, Qlikview, and Power BI.
Snowflake delivers the greatest value when your data pipelines provide the fuel to power analytics efforts. Without data, you have nothing to load into Snowflake and nothing to analyze.
Lastly, as easy as Snowflake may be, many teams struggle with their data stacks. Why? Because they are tasked to undertake work that they are not equipped to handle. Building a data team, that will work hard and is passionate about what they do, is critical. Give your team the training and ongoing education needed so they can keep their skills sharp and being fully engaged with Snowflake, analytics tools, and data literacy.
We are big fans of the product direction the Snowflake Elastic Data Warehouse is taking. For data-driven teams looking at modern data platform options, a cloud-native Snowflake warehouse may be a fit.
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