If you have big data, then you need big storage. And if you want to leverage that big data, then you’ll need to migrate from a traditional data warehouse to a modern, cloud-based solution to benefit from the scale, flexibility, and agility of cloud computing.
A cloud-based data warehouse can also unlock new, advanced capabilities that wouldn’t otherwise be possible with traditional infrastructure. For example, you can process massive amounts of data in the cloud, in any format, from any source, and extract insights from that data to power advanced analytics. As a result, there’s a growing need for modern data warehousing solutions.
In the Google Cloud ecosystem, BigQuery is Google’s fully managed, serverless enterprise data warehouse that offers a powerful, scalable option for data warehouse modernization. Not only does it support all data types, but it also works across all clouds (not just Google Cloud) and it’s AI-ready with built-in machine learning, geospatial analysis, and business intelligence. Plus, it can integrate with BI engines, and its in-memory query execution capabilities can improve data analytics performance.
Here are some considerations for migrating an on-premise data warehouse to BigQuery.
Assess your readiness for migration. Start by conducting an assessment of your current on-premise data warehouse environment, including an inventory of your data. Is your data accurate and reliable? Which workloads can tolerate downtime (and for how long)? Determine a workload’s importance to your business, its dependencies, and its compliance requirements. You should also evaluate workload suitability for BigQuery. Google Cloud has a migration assessment tool that can help you prepare for a BigQuery migration and understand the value proposition when compared to your legacy data warehouse.
Develop a migration strategy. Once you have a thorough understanding of your current environment, determine the right migration method to bring it into the cloud. Do you need to transform your data? Can you lift and shift your current data warehouse into the new cloud environment? Or will it need to be re-platformed or rearchitected to function properly in the cloud? Answering these questions will help determine your migration approach. From there, set clear goals and timelines that are aligned with your business objectives.
Transform and optimize your data. Before the actual migration, you can transform and optimize your data through data cleansing, transformation, and schema optimization. For example, you’ll want to make sure your data is clean, accurate, reliable, and up-to-date. Since BigQuery is column-oriented, de-normalizing your schema can also improve query performance. There are several Google Cloud tools that can help. For example, Dataflow provides pre-designed templates and tools for data integration, and a job builder allows you to build and run pipelines — even without knowledge of coding. And, as part of Gemini in BigQuery—a newly introduced BigQuery data preparation AI solution—you’ll be able to streamline and simplify the data preparation process.
Execute a phased migration approach. Migrating via incremental data transfers to BigQuery can help to reduce risks, minimize downtime, and possibly ensure a smoother transition. For medium to large datasets, Google Cloud Storage Transfer Service can help move on-premise data to Google Cloud Storage, which can then be loaded into BigQuery. Be sure to prioritize use cases and conduct proof-of-concept (PoC) projects with measurable criteria. A PoC can help you validate your approach and ensure the data in the destination database is accurate—plus, you can learn from any mistakes while they’re in a non-critical environment.
Ensure performance, security, and compliance. Following best practices during the migration process can help optimize BigQuery performance. That includes partitioning, clustering, and query optimization. For example, through partitioning (dividing large tables into smaller ‘partitions’), you can improve query performance and speed, while controlling costs. You can achieve these same benefits through clustering, and can optimize queries (such as using history-based optimizations) for improved performance. In addition, Google Cloud’s security features such as Cloud IAM and encryption can help maintain data security and compliance throughout the migration process.
Ready to get started?
From assessing your migration readiness, to transforming and optimizing your data, to ensuring a secure, compliant migration, these key best practices can help you successfully migrate an on-premise data warehouse to BigQuery. From there, you’ll have a scalable, cloud-based data warehouse that can support large datasets and a variety of workloads across multi-cloud environments, so you can leverage enterprise data for advanced analytics and AI, all while protected by robust security controls.
Want to find out more? Contact Pythian Pythian for expert guidance and support throughout your data warehouse migration journey.
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