Even if you’re new to data engineering, chances are good you’ve heard about Snowflake. Why? Because just as Shipyard is a best-in-class platform for data orchestration, Snowflake is a go-to pick for data warehousing needs and taming the boom of business data.
And this isn’t surprising considering all the inherent advantages it provides professionals responsible for data quality, data management, and data analytics (namely data engineers, data scientists, and IT stakeholders).
Even better? The Snowflake team hasn’t been sleeping on their success, having recently invested in DataOps.live to further increase agility and security for the Data Cloud, expanded programmability to bolster support for AI/ML and streaming pipeline development, strengthened its commitment to public sector customers with U.S. DoD IL4 authorization, and more. (In other words, they’ve been a bit busy.)
Here, we’ll cover these major advantages, the basics of how to set up and use Snowflake for DataOps, and a few tips for turning Snowflake into a full-on data warehousing blizzard.
Why Snowflake is a DevOps dynamo
Snowflake is a cloud data platform, meaning it’s inherently capable of extreme scalability as part of the DevOps lifecycle. This advantage alone is a huge selling point, as the chaos inherent in data environments necessitates great agility and adaptability for development teams building data applications and products. And this advantage is exactly what Snowflake enables, allowing its users to scale data processing and storage capabilities on demand without ever worrying that they’ll overwhelm the platform.
Snowflake also runs on a unique, multi-cluster shared data architecture, allowing Snowflake to separate the resources it uses for storage and compute. This boosts ongoing performance, as the platform can automatically distribute workloads across its multiple compute clusters. These performance increases, in turn, reduce query latency while increasing throughput.
In addition to this exceptional performance, the Snowflake platform is supremely flexible and secure. As for flexibility, Snowflake supports an array of data formats, including semi-structured and structured data, and also integrates easily with many data integration and transformation tools. In short, this makes data ingestion a breeze.
Snowflake also offers robust, built-in security features for optimal data security—a must for modern data governance. These features include role-based access control, end-to-end encryption, and compliance certifications like SOC 2 Type II, PCI DSS, HIPAA, and GDPR. This means, while it’s easy for DevOps teams to get data into Snowflake, they can also be confident that said data is secure while it’s stored, processed, and analyzed.
However, this security doesn’t come at the expense of usability—quite the opposite is true. Snowflake enables data engineers and operations teams to focus on delivering value from data, as it can abstract much of an IT environment's maintenance tasks and underlying infrastructure. Additionally, its data-sharing capabilities simplify the process of sharing data across an organization and with external partners.
All of this comes with a pay-as-you-go pricing model, allowing organizations to only pay for the compute resources and storage consumed. This scale-friendly approach to pricing—in addition to the sheer abundance of these other factors listed—makes Snowflake an exceptional fit for business leaders looking to enhance overall business operations through DataOps.
And if that sounds appealing, read on, because we’ll cover the basics of how to set up Snowflake as a DataOps platform in addition to covering how to use its most popular features (and how to get the most out of them).
How to set up Snowflake for DataOps
Fortunately, Snowflake’s as easy to get up and running as it is to operate. Here are the basics:
At the time of this writing, new users get their first 30 days of Snowflake for free after signing up through their website.
As a new user, you'll select a Standard, Enterprise, or Business Critical tier depending on your needs and then select a cloud provider—Microsoft Azure, Amazon Web Services (AWS), or the Google Cloud Platform.
You're ready to begin once you've confirmed your provided email address. When setting up your virtual warehouse to process data in Snowflake, select the size based on how much data you have and how many people will be using it. Remember, Snowflake's built to scale. You can always start small and grow later.
After you've made a database and schema, you can then leverage tools like Shipyard to create data pipelines to add, change, and study your data.
While you're getting used to manipulating your data in Snowflake, make sure to also set up access control and data encryption, as you don't want to overlook the critical settings that will keep your data secure and private.
Finally, to ensure you cover the basics of Snowflake's functionality, make sure you learn how to accomplish the following:
Add data: Explore different ways to add data to Snowflake. Experimenting with bulk loaders like Snowpipe or Kafka connectors can give you a better sense of what method of adding data will work best for you.
- Manipulate and analyze your data: Use SQL queries to clean, prep, and group your data, functions, and other tools to assist with advanced analysis.
- Explore automation: In Snowflake, tools like Shipyard and Apache Airflow can be used to create, schedule, and monitor data workflows, while Snowflake tasks and streams can be combined to create ELT pipelines.Vet performance improvement features: Finally, review Snowflake's tools designed to help you find and fix problems. Doing so will give you a sense of how the platform can help make your work better and faster.
Getting the most out of Snowflake for DataOps
For most IT professionals, getting the most out of Snowflake involves two things: appreciating its nuances, as well as the tools that partner best with it.
As for specific must-knows, we recommend you spend additional attention on the following:
Time Travel and Zero-Copy Cloning
Snowflake provides users with two additional features that benefit testing and development specifically: Time Travel and Zero-Copy Cloning. Time Travel provides the ability for users to access historical data states. Snowflake’s cloning feature allows users to clone their data without the need for duplicate storage.
Continuous data loading
Snowflake nudges users toward a continuous loading pattern as opposed to batch-loading data. The former helps minimize latency between when data is added and when it’s available to query.
Snowflake caches query results in order to accelerate the results of identical queries run again within a 24-hour period.
Pre-computed results of your queries are also stored, which can then increase the performance of subsequent and increasingly complex queries.
Snowflake users can structure data specifically to take advantage of the platform's columnar storage. For example, when using array, object, and variant data types, Snowflake can optimize how it stores and queries data.
Snowflake plays nice with an impressive range of tools for data visualization and data science, allowing users to further optimize their data workflows.
It bears noting, however, that a DevOps team's ability to maximize their investment in Snowflake also hinges on uses cases, requirements, and the quality of the IT environment it's introduced into.
For instance, Snowflake becomes an even more potent addition to the DevOps tech chain when coupled with a best-in-class orchestration tool like Shipyard. Here are some potential benefits of this specific partnership:
ETL job automation: When bringing Shipyard into the mix, you can automate the extraction, transformation, and loading (ETL) of data from a variety of sources, piping them right into Snowflake. Automating this process ensures your data is always up-to-date and ready to analyze.
- Data cleaning and preparation: As part of the Shipyard/Snowflake synergy, the former can also automate all your data cleaning and preparation tasks before that data is loaded into Snowflake. The benefits here include deduplication, dealing with missing values, and making sure new datasets are formatted correctly.
- Data validation and syncing across platforms: Yet another data-based advantage comes from Shipyard's ability to validate data and help automate data syncing between systems. For use cases where multiple platforms will need to be used alongside Snowflake, this automation ensures consistency across all platforms, removing the need for manual syncing.
- Potent workflow templates: Shipyard offers a variety of workflow templates for common tasks that take place within Snowflake. Using these templates makes setting up new workflows that much faster.
- Monitoring and alerts: Shipyard also provides additional monitoring and alerting capabilities that can be used to keep track of your Snowflake-related workflows. Using these can help you quickly identify and resolve issues as they arise.
- Backup and recovery: You can orchestrate data backup processes using Shipyard and leverage Snowflake's features like Time Travel and Fail-Safe for robust data recovery processes.
Remember to always test and monitor your workflows to ensure they're functioning correctly and providing the desired benefits. The best approach will depend on your specific needs and the characteristics of your data and workflows.
In the meantime, please consider subscribing to our weekly newsletter, "All Hands on Data." You'll get insights, POVs, and inside knowledge piped directly into your inbox. See you there!