Data Migration Strategies For Large Scale Systems

Data Engineering Podcast

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21d ago 1:00:00

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Summary

Any software system that survives long enough will require some form of migration or evolution. When that system is responsible for the data layer the process becomes more challenging. Sriram Panyam has been involved in several projects that required migration of large volumes of data in high traffic environments. In this episode he shares some of the valuable lessons that he learned about how to make those projects successful.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Sriram Panyam about his experiences conducting large scale data migrations and the useful strategies that he learned in the process

Interview

Introduction
How did you get involved in the area of data management?
Can you start by sharing some of your experiences with data migration projects?
- As you have gone through successive migration projects, how has that influenced the ways that you think about architecting data systems?
How would you categorize the different types and motivations of migrations?
- How does the motivation for a migration influence the ways that you plan for and execute that work?
Can you talk us through one or two specific projects that you have taken part in?
Part 1: The Triggers
- Section 1: Technical Limitations triggering Data Migration
  - Scaling bottlenecks: Performance issues with databases, storage, or network infrastructure
  - Legacy compatibility: Difficulties integrating with modern tools and cloud platforms
  - System upgrades: The need to migrate data during major software changes (e.g., SQL Server version upgrade)
- Section 2: Types of Migrations for Infrastructure Focus
  - Storage migration: Moving data between systems (HDD to SSD, SAN to NAS, etc.)
  - Data center migration: Physical relocation or consolidation of data centers
  - Virtualization migration: Moving from physical servers to virtual machines (or vice versa)
- Section 3: Technical Decisions Driving Data Migrations
  - End-of-life support: Forced migration when older software or hardware is sunsetted
  - Security and compliance: Adopting new platforms with better security postures
  - Cost Optimization: Potential savings of cloud vs. on-premise data centers
Part 2: Challenges (and Anxieties)
- Section 1: Technical Challenges
  - Data transformation challenges: Schema changes, complex data mappings
  - Network bandwidth and latency: Transferring large datasets efficiently
  - Performance testing and load balancing: Ensuring new systems can handle the workload
  - Live data consistency: Maintaining data integrity while updates occur in the source system
  - Minimizing Lag: Techniques to reduce delays in replicating changes to the new system
  - Change data capture: Identifying and tracking changes to the source system during migration
- Section 2: Operational Challenges
  - Minimizing downtime: Strategies for service continuity during migration
  - Change management and rollback plans: Dealing with unexpected issues
  - Technical skills and resources: In-house expertise/data teams/external help
- Section 3: Security & Compliance Challenges
  - Data encryption and protection: Methods for both in-transit and at-rest data
  - Meeting audit requirements: Documenting data lineage & the chain of custody
  - Managing access controls: Adjusting identity and role-based access to the new systems
Part 3: Patterns
- Section 1: Infrastructure Migration Strategies
  - Lift and shift: Migrating as-is vs. modernization and re-architecting during the move
  - Phased vs. big bang approaches: Tradeoffs in risk vs. disruption
  - Tools and automation: Using specialized software to streamline the process
  - Dual writes: Managing updates to both old and new systems for a time
  - Change data capture (CDC) methods: Log-based vs. trigger-based approaches for tracking changes
  - Data validation & reconciliation: Ensuring consistency between source and target
- Section 2: Maintaining Performance and Reliability
  - Disaster recovery planning: Failover mechanisms for the new environment
  - Monitoring and alerting: Proactively identifying and addressing issues
  - Capacity planning and forecasting growth to scale the new infrastructure
- Section 3: Data Consistency and Replication
  - Replication tools - strategies and specialized tooling
  - Data synchronization techniques, eg Pros and cons of different methods (incremental vs. full)
  - Testing/Verification Strategies for validating data correctness in a live environment
  - Implication of large scale systems/environments
  - Comparison of interesting strategies:
    - DBLog, Debezium, Databus, Goldengate etc
What are the most interesting, innovative, or unexpected approaches to data migrations that you have seen or participated in?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data migrations?
When is a migration the wrong choice?
What are the characteristics or features of data technologies and the overall ecosystem that can reduce the burden of data migration in the future?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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