Five Critical Success Factors To Migrate Data to Snowflake
- John Morrell
- August 30, 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.
Data transformation queries get expensive on a cloud data warehouse like Snowflake because they require compute-intensive joins and aggregations over large volumes of raw data. This requires you to optimize your data transformation – both the process and the actual data transformation models. Read our guide – How to Optimize Your Data Transformation for Snowflake – to learn more about creating highly optimized data transformation models and processes in Snowflake.
Pick the Right Data Transformation 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 transformation 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.
Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake. The multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data. Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance. Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.