You’ve already moved some of your workloads to the cloud—probably the ones that were easiest to migrate, or the ones that provided a quick win. But your most complex workloads are probably still sitting in an on-prem data warehouse.
While it’s rare to find a company these days that isn’t in the cloud, at least to some extent, it’s also rare to find a company that’s all-cloud. Certain workloads may be better off in your on-prem environment, while others could benefit from the scalability, performance and cost-saving potential of the cloud.
But if you’re dealing with complex data warehouse solutions like Teradata, Netezza or Oracle—which are likely mission-critical to your business—there’s no such thing as a simple migration. To optimize these workloads in the cloud, you’ll need to restructure your data and processing jobs, which requires an orchestrated approach.
So, it’s complicated. You’ll need to do a thorough review of your current data sources, analytics workloads, components and features being used, such as database objects, database resident transformation code, dependencies on external objects, relative data volumes, data products, refresh rates and analytics tools—to name a few.
Here are a few questions to ask when assessing your current technical and business data warehouse environment before the migration:
Looking at other migration strategies but not sure where you should start? Read our Google Cloud Migration Handbook to learn how to migrate to Google Cloud with confidence.
Cue BigQuery: Google’s serverless enterprise data warehouse allows you to remove administrative complexity while modernizing your environment. You’ll replace your expensive CapEx-based data warehouse with a pay-as-you-go OpEx model. And while that alone is an enticing reason to migrate your data warehouse to the cloud, BigQuery takes it a step further.
For example, BigQuery allows you to manage and analyze data with features like machine learning, geospatial analysis and business analysis. And with a highly scalable distributed analysis engine that lets you query terabytes in seconds—and petabytes in minutes—you can move beyond descriptive analytics to predictive and prescriptive analytics.
And there are even more benefits of moving your data warehouse to Google Cloud, including:
Eliminate data silos with data integration: Quickly integrate data from any number of sources to serve a range of users and use cases—with easy monitoring and management.
Leverage the power of data: Take advantage of flexible data storage, powerful data transformation and lightning-fast queries on a petabyte scale.
Visualize your data: Get a better understanding of your data through integration with third-party reporting and BI providers, such as Tableau, MicroStrategy or Looker.
No-worries management: Focus on innovation instead of maintenance with a fully managed, no-ops data warehouse.
Protect your bottom line: Get the most from a range of Google Cloud data services while optimizing cost and performance.
There are many benefits, but also much to consider in plotting your way forward. Having a partner who specializes in complex data environments – and in getting more business value from your data – can help ensure that you’re on the path to success.
If you’re ready to make a move but don’t know where to start, Pythian can help with our GCP-based Data Warehouse Migration Assessment. Our GCP data strategists will analyze your existing on-premises data warehouse to determine the level of technical difficulty of the migration from your data warehouse to Google Cloud. This can help to reduce your financial and technical risks—and help you start benefiting from Google Cloud as early in the migration process as possible.