Migrating Analytics to the Cloud at Fannie Mae

Migrating Analytics to the Cloud at Fannie Mae

Tuesday, June 19
2:50 PM - 3:30 PM
Executive Ballroom 210B/F

This presentation will describe the analytics-to-cloud migration initiative underway at Fannie Mae. The goal of this effort is threefold: (1) build a sustainable process for data lake hydration on the cloud and (2) modernize the Fannie Mae enterprise data warehouse infrastructure and (3) retire Netezza.

Fannie Mae partnered with Impetus for modernization of its Netezza legacy analytics platform. This involved the use of the Impetus Workload Migration solution—a sophisticated translation engine that automated the migration of their complex Netezza stored procedures, shell and scheduler scripts to Apache Spark compatible scripts. This delivered substantial savings in time, effort and cost, while reducing overall project risk.

Included in the scope of the automation project was an automated assessment capability to perform detailed profiling of the current workloads. The output from the assessment stage was a data-driven offloading blueprint and roadmap for which workloads to migrate. A hybrid cloud-based big data solution was designed based on that. In addition to fulfilling the essential requirement of historical (and incremental) data migration and automated logic translation, the solution also recommends optimal storage formats for the data in the cloud, performing SCD Type 1 and Type 2 for mission-critical parameters and reloading the transformed data back for reporting/analytical consumption.

This will include the following topics:

i. Fannie Mae analytics overview

ii. Why cloud migration for analytics?

iii. Approach, major challenges, lessons learned

Presentation Video

SPEAKERS

Kevin Bates
Vice President for Enterprise Data Strategy Execution
Fannie Mae
Kevin Bates is Fannie Mae’s Vice President for Enterprise Data Strategy Execution. Reporting to the Chief Data Officer, Bates is responsible for leading Enterprise Data’s execution and delivery of end-to-end solutions supporting corporate initiatives. This role includes responsibility for delivery of the Enterprise Data Infrastructure (EDI), the primary vehicle for enterprise-scale data aggregation, integration, and standardization at Fannie Mae.