The moment of truth has arrived. After many meetings with data experts and a careful analysis of the state of data of your organization, you are now ready for a Snowflake database migration. For companies dealing with legacy data, data migration to the cloud is a key step in their data modernization journey.
As of 2022, Gartner research reports that over 70% of companies in the United States have migrated at least some of their workload to the cloud. Although the benefits of the cloud are great, many organizations struggle to manage the cost of cloud computing. In fact, by 2024, Gartner also projects that 83% of business leaders are likely to encounter cloud cost overruns.
To avoid the most common hurdles of a Snowflake database migration process, we’ll go over the key factors that determine both failure and success.
Migrating to Snowflake: What Can Go Wrong?
Understanding common mistakes and misconceptions about the challenges of Snowflake database migration can help teams develop better strategies for their own data migration projects.
One of the most common mistakes business leaders make when transitioning their data to the cloud is believing that migrating data implies moving from point A to point B. The data migration process involves extracting, transforming and loading (ETL) data. Throughout the course of this process, data is first identified and categorized depending on factors such as format and sensitivity.
All relevant data must comply with the parameters set in a data migration plan, thus ensuring information performs accordingly. While technical data teams are likely to understand all that a database migration entails, it’s important that the leadership team across your organization is clear on the complexities, challenges, and overall workflow of a comprehensive Snowflake database migration. Migration will not be a point A to point B transition. Instead, prepare your team for a nuanced journey that ensures data quality and governance along the way.
In data migration efforts, organizations also often struggle with risk identification and management. As early as 2021, Oracle began reporting that when it comes to data migration, “risk usually surfaces very late in the form of load failures in the target system.” Because load failures are a result of a poor understanding of the nuances the data contains, understanding your data and identifying risks in a timely manner is pivotal.
Finally, many leaders fail to include data governance methodologies in their data migration planning. Having a formal approach to data governance is very important. Defining the rules of engagement and determining what “good data” means for your business can eliminate data quality issues that may affect the project later on.
Now that we’ve mapped some frequent mistakes and misconceptions, here are six strategies successful organizations deploy to ensure a smooth Snowflake database migration.
Snowflake Database Migrations: What Successful Companies Do Differently
They Engage Their Whole Business
Any database migration requires the involvement of multiple parties and departments. Because data is key to every single aspect of a company, involving business leaders and other key stakeholders across the organization can help provide a clear panorama of the state of your business’s data.
In a state of data report, Oracle found that failure to incorporate data stakeholders across an organization, along with neglecting discussions about key data needs and future state led migration initiatives to become “IT-centric projects that involve guesswork and assumptions.” The degree of involvement needed throughout a business, however, depends on the migration needs and the specific characteristics of the data.
They Identify All Data Sources
One of the greatest challenges in any data migration is identifying all the numerous data sources in the organization. While this process can be meticulous and time consuming, it’s especially important for companies dealing with legacy and siloed data.
The early planning stages of a Snowflake database migration should take into account all the different data sources and complex formats your technical team will encounter, create automated schema recognition, and ensure data completeness. Organizations that go the extra-mile also prioritize implementing data protection and privacy measures from the get-go. Doing so will help resolve any security issues later on.
They Focus on Data Governance
Migrating to a modern data stack is a highly technical process that requires the input of data experts, engineers and architects. However, it’s also a human process. Every single migration project should also incorporate people and processes, and address any concerns as to how technology will be used.
As mentioned in our Impact of Data Governance on Business 2022 blog, “When done properly, good data governance can lift your company’s decision-making processes to new heights. But just about shoehorning in some policies just to have them. Companies need to adopt a new mindset toward data governance writ-large in order to tap into the full benefits of these strategies. They need to understand governance as a proactive framework and philosophy, instead of just regulatory necessity.” Therefore, encouraging your team to think about who should be involved, under what conditions and how specific cases should be resolved can help your organization deal with potential risks effectively.
They Have a Clear and Actionable Plan
To mitigate unwanted outcomes, such as TSB Bank’s infamous 2018 migration, companies require a solid migration plan. One of the trickiest aspects of creating a plan is the absence of a one-size-fits-all strategy. Instead, understanding your data needs and selecting an approach and method that adapts to your organization will help you achieve your goals.
To ensure this plan fits your specific business needs, be sure to consider all the software and technology you’ll be migrating off, as well as what replacements you’ll be migrating to. The modern data stack isn’t one size fits all, and chances are, as you move to the cloud, you’ll need to implement additional tools to help your organization handle issues like governance, resilience, and specific business use cases. Looking to understand how to start? Take a look at our 2023 Business Guide to Data Management Modernization.
They Manage the Cost of Their Snowflake Database Migration
In order to make the most out of Snowflake’s consumption-based model and avoid cost overruns, organizations should clearly map out their data needs and optimize their data processes. Pairing up with a team of data experts, like the Snowpro certified consultants at Hakkoda, can help you set clear objectives and methods to make the most out of your budget.
From a technical standpoint, engineers must factor in plenty of variables and objectives to ensure optimized costs. This includes selecting your sources, determining your infrastructure and schema, identifying security and safety measures, and other requirements.
Companies that have a plan in place to help monitor and manage their consumption with Snowflake can confidently keep costs in check. Hakkoda’s data experts identified such a significant business need for Snowflake consumption optimization among clients that they built and implemented a unique solution.
Hakkoda clients can access resource monitoring tools that help them optimize queries, reduce consumption, and get customized alerts for cost saving opportunities. Learn more about Hakkoda RM.
They Ensure a Continuous Process
One of the most common misconceptions about data migration is that it is a one-off event. Because data inflow is consistent and ever-growing, businesses should consider building a process and plan that allows data to flow consistently into Snowflake.
A successful Snowflake database migration employs both MLOps and DataOps for data management. MLOps is the practice of deploying and architecting machine learning solutions that can be automated and scalable within a company’s workflow. Once the data pipeline is created, engineers can implement DataOps principles to automate operations, promote scalability and improve governance.
Looking for extra help? Hakkoda’s managed DataOps team maintains and adapts your data operations processes, maintaining ELT, observability, and data lineage.
The Hakkoda Approach to Data Modernization
Hakkoda’s understanding of the caveats and difficulties associated with data migration and modernization has helped many organizations meet their data needs. We like to look at the big picture, understand the panorama and make informed decisions to create effective data pipelines, improve speed and meet business objectives.
From modernizing your ingestion process with ELT and building DataOps processes into your pipelines to the implementation of a data catalog across your platform, Hakkoda is here to help. Contact one of our data experts today to understand how to guide your data transformation.