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Migrating a large-scale data warehouse to the cloud can be a demanding task, yet it’s a crucial step for many organizations aiming to modernize their data infrastructure and enhance their data management capabilities. As data volumes continue to surge, traditional data warehousing solutions often falter under the increasing demands for scalability, performance, and advanced analytics.
Transitioning to Amazon Redshift provides organizations with the opportunity to achieve better cost-efficiency, enhanced data processing, quicker query response times, and improved integration with technologies like machine learning (ML) and artificial intelligence (AI). However, significant challenges may arise during the planning phase of a large-scale data warehouse migration. These challenges encompass ensuring data quality and integrity throughout the migration process, addressing technical complexities related to data transformation, schema mapping, performance, and compatibility between source and target data warehouses. Organizations also need to deliberate on cost implications, security compliance requirements, change management processes, and potential disruptions to ongoing business operations during the migration. Effective planning, thorough risk assessment, and a comprehensive migration strategy are essential for overcoming these challenges and ensuring a successful transition to the new data warehouse environment on Amazon Redshift.
In this article, we explore best practices for assessing, planning, and implementing a large-scale data warehouse migration to Amazon Redshift.
Success Criteria for Large-Scale Migration
The following diagram illustrates a scalable migration pattern for an extract, load, and transform (ELT) scenario using Amazon Redshift data-sharing patterns.
It’s essential that all stakeholders (producers, consumers, operators, auditors) align on success criteria for a smooth transition to a new Amazon Redshift modern data architecture. The success criteria act as key performance indicators (KPIs) for each element of the data workflow, encompassing ETL processes that capture source data, the refinement and creation of data products, aggregation for business metrics, and consumption from analytics, business intelligence (BI), and ML.
KPIs ensure that you can track and audit optimal implementation, achieve consumer satisfaction and trust, while minimizing disruptions during the final transition. They measure workload trends, cost usage, data flow throughput, consumer data rendering, and real-life performance, ensuring that the new data platform meets both current and future business objectives.
Migrating from a large-scale mission-critical monolithic legacy data warehouse (like Oracle, Netezza, Teradata, or Greenplum) typically requires 6–16 months of planning and implementation, depending on the complexity of the existing setup. These monolithic environments, developed over the last three decades, contain proprietary business logic and various data design patterns, including operational data stores, star or Snowflake schemas, dimensions and facts, data warehouses and data marts, online transaction processing (OLTP) real-time dashboards, and online analytic processing (OLAP) cubes with multi-dimensional analytics. Given the critical nature of these data warehouses, minimal downtime is permissible. If your data warehouse platform has undergone multiple enhancements, your operational service level documentation may not reflect the latest metrics and SLAs for each tenant (such as business units or data domains).
As part of the operational service levels success criteria, you need to document the expected service levels for the new Amazon Redshift data warehouse environment. This includes response time limits for dashboard and analytical queries, runtime for daily ETL jobs, desired elapsed time for data sharing with consumers, the total number of tenants with concurrency loads and reports, and crucial reports for executives or factory operations.
The migration goal for a modern data architecture transition to a new Amazon Redshift platform is to leverage its scalability, performance, cost-optimization, and additional lake house capabilities, ultimately improving the existing data consumption experience. Depending on your organization’s culture and goals, you might consider one of the following migration strategies:
- Leapfrog Strategy – This approach involves migrating to a modern AWS data architecture one tenant at a time. For instance, refer to how JPMorgan Chase built a data mesh architecture to enhance their enterprise data platform.
- Organic Strategy – This method employs a lift-and-shift data schema using migration tools. For an example, check how GE Aviation modernized their technology stack and improved data accessibility using Amazon Redshift.
- Strangler Strategy – This strategy entails creating an abstraction layer for consumption and transitioning one component at a time. For further details, see the Strangler Fig Application.
Most organizations opt for the organic strategy (lift and shift) when migrating their large data platforms to Amazon Redshift. This approach utilizes AWS migration tools, such as the AWS Schema Conversion Tool (AWS SCT) or the managed service version DMS Schema Conversion, to rapidly achieve goals around data center exit, cloud adoption, reducing legacy licensing costs, and replacing outdated platforms.
By establishing clear success criteria and monitoring KPIs, you can facilitate a seamless migration to Amazon Redshift that aligns with performance and operational goals. Thoughtful planning and optimization are vital, including optimizing your Amazon Redshift configuration and workload management, addressing concurrency needs, implementing scalability, fine-tuning performance for large result sets, minimizing schema locking, and optimizing join strategies. This will ensure that the Redshift data warehouse is right-sized to meet workload demands cost-effectively. Thorough testing and performance optimization will allow for a smooth transition with minimal disruption to end-users, enhancing user experiences and satisfaction. Achieving a successful migration involves proactive planning, continuous monitoring, and performance fine-tuning to align with business objectives.
Migration encompasses the following phases, which we will explore in subsequent sections:
- Assessment
– Discovery of workload and integrations
– Dependency analysis
– Effort estimation
– Team sizing
– Strategic wave planning - Functional and Performance
– Code conversion
– Data validation - Measure and Benchmark KPIs
– Platform-level KPIs
– Tenant-level KPIs
– Consumer-level KPIs
– Sample SQL - Monitoring Amazon Redshift Performance and Continual Optimization
– Identify top offending queries
– Optimization strategies
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