Article 2 in a three-part series
Cloud-native tools like Snowflake Data Cloud have opened new pathways for companies to organize and use their data without the cumbersome and costly on-premises infrastructure. And though it’s never been easier to deploy these platforms, taking the time to put in place a modern data and analytics strategy will ultimately enable you to make smarter, faster decision-making about how to invest in and assemble the right components and capabilities. Think of this strategy as both a map and a set of guardrails for your path forward—following it will not only help you meet today’s goals but will flex to meet your future needs as well.
Which brings us back to Snowflake and the potential value it offers in the context of your data and analytics strategy. What’s the best way to determine where, when, and how Snowflake can deliver the biggest bang for your organization? Based on our work with clients across industries and at varying stages of their cloud-based data and analytics journeys, we’ve identified some of the highest-value opportunities for using Snowflake—as well as a few scenarios in which companies might want to consider pairing Snowflake with other tools to reach their goals.
Looking for maximum value from Snowflake?
Here are a few key ways to go about it:
- Fast-track your data cloud journey
A lot has been written about how quick, easy, and inexpensive to stand up Snowflake. And for the most part it’s all true, so we won’t delve into it too much here except to say that getting off to a great start is a huge benefit, but most organizations find that some customizations will be needed to accommodate their organization’s specific needs and goals.
- Use Snowflake’s flexibility to your advantage
Snowflake is a multicloud platform, which is unique among cloud data services. Why is that good for a modern data and analytics strategy? It means you can use Snowflake with any cloud solution provider you’re already using—AWS, Azure, or GCP—or might select in the future. Snowflake’s multicloud support offers you the platform freedom to essentially plug and play the cloud and analytics technologies that best meet your near- and long-term goals—and your budget. Snowflake can also host and distribute data across multiple clouds to ensure maximum availability and minimal risk of data loss or accessibility.
- Up your data game with Snowflake’s data marketplace and data sharing capabilities
The Snowflake Data Marketplace offers a number of curated data sets—from geolocation, weather, industry-specific logistics, and much more—that organizations can easily access for a fee via its partner ecosystem. Organizations can use this data to fill in strategic gaps in their own data stores and conduct game-changing business and advanced analytics that may otherwise have not been possible. Accessing the Snowflake Data Marketplace couldn’t be easier: When you activate your contract with the Data Cloud partner, the data is automatically integrated into your Snowflake account—no external integrations, cleansing, or ingesting required. You can then join the new data to your queries as if it were you own. And the data you purchase is automatically updated and maintained. Think of it as an App Store for your data warehousing needs.
Snowflake also makes it easier to share data with third parties in your ecosystem. No ETL or data silos to worry about. By providing seamless and secure access to a single, shared data source, Snowflake enables organizations and their outside partners, vendors, and affiliates to collaborate with less friction.
- Harness Snowflake’s scalability to get results quicker (and balance costs)
Because Snowflake separates compute from storage (almost all other databases/platforms combine the two), it can automatically, and almost instantaneously, scale up and down. You don’t have to resize workloads, which can cause costly interruptions, or invest in expensive hardware to support additional databases. There’s also a potential cost savings bonus: Snowflake’s auto “stop and start” compute capability ensures you only use—and pay for—the computing power you need when you need it.
Scenarios where Snowflake may not be the best or only fit
As multifaceted and innovative as Snowflake is, it’s not a silver bullet—no platform or technology ever is. Here are a few instances where you might want to consider alternative approaches or companion tools to Snowflake.
- You have mountains of unstructured data
If your organization is gathering and needs to analyze huge stores of unstructured data—images, videos, text-heavy documents, and the like—Snowflake may not easily fulfill all your needs, at least not now. With Snowflake’s unstructured data capabilities still in beta, you’ll need to house this kind of data in a secondary platform that natively supports unstructured data. That said, Snowflake’s support of unstructured is progressing rapidly, which could allow Snowflake to catch up to other platforms in functionality and capability.
- You’re expecting a built-in modeling capability
Many organizations will assume that a platform as robust and versatile as Snowflake will also meet their data modeling needs. But that’s not the case—and not just for Snowflake, either. Data modeling is an essential capability in a modern data and analytics strategy and needs be intentionally planned and built. Organizations shouldn’t just assume that data modeling will be automatically taken care of by Snowflake—or any cloud platform—especially if they intend to reuse or build off the prototype.
- You’re concerned about the cost of data processing on a large scale
For companies doing a huge amount of routine data ingestion and transformation, Snowflake might not be the most cost-effective solution. You may be better off pairing Snowflake with an ETL-type tool, so the data is processed elsewhere and then imported into Snowflake, which serves as the front-end, consumption environment.
Snowflake is a robust and rapidly evolving cloud platform that can play a vital enabling role in your data and analytics strategy. And because it easily integrates with other tools and platforms, it puts a best-in-class approach to implementation at your fingertips. How to balance the decisions and dynamics of a Snowflake-powered data and analytics strategy is the topic of our next article in this series: A deep dive on how a large manufacturing company created an end-to-end, scalable analytics solution that builds on Snowflake to bring a multisource technology strategy to life.
Dave Mobley, Chief Practices Officer
Greg Stuhlman, Principal, Data Analytics
Shan-Ming Chiu, Senior Manager, Data Architecture
Learn more about Aspirent’s Data & Analytics Practice.
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