Migrating Workloads from Netezza to Big Data: An Automated Approach

Manual migration can be complicated. Niche technologies, human error, lengthy assessment phases, testing, validation, execution, and the migration itself – these all contribute to the complexity.

The Impetus Workload Migration Solution addresses these complexities and minimizes migration risks.

Our approach: We work with you to ensure 100% execution

The Impetus Workload Migration Solution migrates workloads in four phases:

  • Initial business assessment

  • Execute a pilot workload to prove the ROI

  • End-to-end migration: Using the complete data and workloads

  • Post-migration considerations

 

 

Phase 1: Initial business assessment

In the first phase, our automated tools assess business goals and the existing data warehouse environment.

  • The goals are mapped to the pre-defined SLAs and performance benchmarks.

  • The assessment engine processes the query logs (or?) for your workloads to perform an in-depth analysis of all your system entities and provides recommendations for migration.

The comprehensive assessment of the Netezza data warehouse does the following:

  • Recommends the ideal migration candidates and their precise positioning on Hadoop

  • Furnishes low-level insights such as the most active users and applications, the most expensive transactions, as well as the most complex, resource-intensive, and frequently used entities

With this automated assessment paradigm, the Impetus Workload Migration Solution defines a clear migration scope and strategy.

Phase 2: PoC with client workloads

"83% of data migration projects either fail or exceed their budgets and schedules.” – Gartner

Automating the complete migration process helps determine the budget for cost and schedule. The pilot project then highlights how the Impetus Workload Migration Solution does that.

The pilot solution migrates all your sample (data and logic) workloads in less than a week. Additionally, you can validate your migrated data and metadata by applying numerous aggregate checks using the automated validation framework.

The pilot phase allows you to do the following:

  • Automagically translate SQL scripts and stored procedures to HQL/ Spark SQLs

  • Access a Netezza specific in-built library of User-Defined Functions (UDFs) and keywords to fill in target system gaps

  • Output lock-in free code

  • Avoid long development, testing, and validation cycles

  • Achieve 100% automated logic translation of your scripts using the translation experts contained within our automation engine.  Create migration workflows for execution of the transformed scripts on your preferred execution engine

  • Reload the transformed data to your Netezza data mart for critical reporting/analytical consumption

Phase 3: End-to-end migration

The end-to-end migration is implemented in small phases. A comprehensive business and workload assessment is conducted to establish business priorities, based on the workload to be migrated.

For instance, optimized data modeling can be performed during this phase to get a performance boost on Hadoop.

The Impetus Workload Migration Solution also lets you:

  • Load data directly from files to Hive tables

  • Migrate schema using the DDL files

  • Check audit logs and lineage

  • Migrate database views

Phase 4: Post-migration considerations

Some essential considerations post-migration are:

  • How often must the data be refreshed?

  • Apart from migration, are there workloads that need to be archived, manipulated, retained, or destroyed?

  • How can data lake-based infrastructure be capitalized and employed with business goals?

  • How would data be governed?

  • How would incremental data and continuous ingestion be handled?

  • What should be the access pattern for various workloads?

  • Does my platform provide a unified view?

Conclusion

The Impetus Workload Migration Solution allows a quick, effortless migration to Big Data, ensuring faster time-to-value for both short-term and long-term business benefits. It also lets you add new data processing capabilities, address the capacity constraints, and traditional tools that can choke your systems.