Five Critical Success Factors To Migrate Data to Snowflake
- John Morrell
- May 10, 2021
You’ve decided to modernize your data and analytics stack and migrate analytics workloads to the cloud – specifically Snowflake migration. Making that decision is monumental but is only just the beginning of your Snowflake migration journey.
Any migration is a complex and challenging task, ripe with challenges and potential pitfalls. Without the proper plan, tools, and skills, Snowflake migration could cost your organization time and money, and keep you from gaining the touted and expected ROI benefits.
Let’s explore five key aspects of your Snowflake migration that will play a large factor in determining your success.
5 key aspects of your Snowflake migration
1. Ensure You Have a Complete Feature Match
While Snowflake has been catching up in enterprise features in recent releases, there are still some gaps versus the on-premises data warehouses. And when doing a Snowflake migration, it is also important to ensure you have enterprise feature matches in the tools making up the rest of your modern data stack.
On the bright side, Snowflake continues to roll out new enterprise-level features as seen in their November 2020 and June 2020 releases. Many of these new capabilities were “catch up” features to enterprise data warehouses such as Teradata and Oracle helping to make Snowflake migration more seamless.
The story is not so bright with many cloud-born data integration tools such as Matillion and Fivetran who severely lack many enterprise features. Your new modern data stack should contain an enterprise-ready data pipeline tool that offers:
- A rich set of transformations that can be applied in a code-free manner to deal with the complexity and variety of enterprise data
- Easy to use tools to cleanse data and ensure data usefulness and completeness for high degrees of data quality
- Secure connections to on-premise data sources, end-to-end encryption, and data masking and obfuscation to maintain the highest levels of data privacy and security
- A high performance and scalable engine to execute data pipelines with the high degree of transformation and volume enterprises require
- A complete set of data governance capabilities that allow governance teams to execute policies at a micro and macro level and scale their operations
- Automated operations services and features that ensure a continuous, scalable, and auditable flow of data for analytics
2. Plan for Continuous Data Flow
Snowflake migration is not a “one and done” project where you simply onboard your data from your existing data warehouse. As with your current analytics, you need to plan for a continuous flow of new data into Snowflake, not just the migration of existing data.
DataOps is an emerging new process in the data analytics world that applies DevOps concepts to data management for analytics. The data pipeline tool you choose needs to support a continuous flow of data through robust DataOps capabilities. This would include:
- An easy to use interface and user experience that speeds data pipeline creation and productionizing
- A scalable engine with flexible delivery options to increase the output the DataOps team produces
- Graphical tools that make it easy to cleanse data, ensure data usefulness and completeness, and increase overall data quality
- A complete suite of features that manage governance and allow governance teams to scale effectively
- Automated operations facilities and detailed monitoring, logging, and auditing for data pipeline reliability
3. Cover Your Entire Suite of Data Sources
Your existing analytics come from numerous data sources in multiple locations – on-premises, cloud, and SaaS. And, to improve the accuracy of their analytics, many organizations are now striving towards greater data completeness, which means they are adding more data to their analytics to have more well-rounded datasets.
The data pipeline platform you choose needs to contain a large number of optimized data connectors to proactively work with all your data sources, regardless of type – databases, data warehouses, data lakes, applications, files – and location – cloud, SaaS, and on-premises. There are specific considerations your tool need to take into account:
- The ability to integrate and transform data across a large number and variety of data sources
- Dealing with diverse and complex formats and having automated schema recognition and resolution
- To ensure data completeness, that platform needs to support large data volumes via connectors with high-performance extraction.
- Data protection and privacy of enterprise data will be critical require secure data connections and complete data encryption.
4. Security and Governance
With an ever-growing range of data security threats and data privacy regulations, security and governance are an essential part of a Snowflake migration. Maintaining the same degree and even improving security and governance during Snowflake migration should be an uncompromised objective.
Cloud security is in some ways better than on-premises because it is built from a more modern approach and infrastructure. And the cloud is catching up with data security features, as seen in the June 2020 Snowflake release. But as far as governance goes, the cloud is still playing catch-up to the on-premises tools and techniques.
As you choose your data pipeline platform, you need to carefully plan for your security and governance needs, including:
- Ensuring all connections to on-premises data sources are secured,
- Encrypting all data both in-flight and while being processed,
- Masking or obfuscating private and or sensitive columns
- Having a complete catalog and set of metadata and tracking end to end lineage
- Putting in place data retention and archiving policies
- Making all data pipelines and DataOps processes fully auditable for regulatory compliance
5. Manage Your Costs
Snowflake and many other cloud services have on-demand, consumption-based pricing models, which means you will see monthly bills. Snowflake, in particular, has a credit model where organizations pre-pay for credits that are burned based on the volume of data stored and the compute resources used for queries.
Cloud migrations and analytics may have extra, hidden costs, particularly with data pipeline tools that only support the ELT model – extract, load, and transform. The ELT model can unknowingly burn many of your Snowflake credits because:
- Both raw, intermediate, and analytics-ready data is loaded into Snowflake, driving up storage costs and burning extra credits, and
- All transformations, to both intermediate and analytics-ready forms, are executed as Snowflake queries driving up compute costs and burning credits.
Data transformation queries are often expensive on a cloud data warehouse like Snowflake because they require compute-intensive joins and aggregations over large volumes of raw data.
An ELT approach also adds security risks to your DataOps. You end up moving all your raw data into the cloud, placing an extra burden on your security and governance teams to secure the data and posing the risk of exposing this data.
You need to look at the overall costs of your entire stack and take into account these extra hidden costs should you choose the ELT approach. A better approach is to select a tool that gives you processing options for integrating and transforming your data – ETL or ELT – and can optimize these processing options to keep your costs in line.
Pick the Right Data Pipeline Tool
Several keys for successful Snowflake migration have less to do with choosing Snowflake and more to do with what tools and platforms you choose to round out your modern data stack. At the heart of this stack is your data pipeline tool and platform.
Successful Snowflake migration begins and ends with a data pipeline platform that combines ease of use, extensive integration and transformation capabilities, scalability, secure connectivity, enterprise-grade security and governance, and a processing model that keeps costs low.
Explore how Datameer Spectrum provides an enterprise-class data pipeline platform and tool to make your Snowflake migration and ongoing DataOps successful. Schedule a demo with our team or take Spectrum for a test drive by requesting a free trial to see how it can effectively be a hub for your modern data stack.