A US-based Fortune 500 telecom conglomerate struggled to maintain Teradata costs within the projected scope of investments and wanted to move to a modern data platform. However, the key challenge was to work in the highly sensitive Teradata production environment without affecting business continuity. Their earlier in-house migration efforts were unsuccessful, as it was difficult to migrate Fact tables with terabytes of records.
Assessed the entire Teradata footprint with 35 active nodes, 1300+ users, and 50+ databases with 700 TB of data
As the first step, transforming legacy Teradata workloads to modern platforms involved identifying and transforming costly and resource-consuming ETL, analytical, and reporting workloads.
The telecom conglomerate was looking for a reliable solution that would simplify and accelerate the transformation process. In addition, they wanted an automated solution to assess, identify, and recommend workloads to offload to a modern platform and free up the Teradata capacity.
Offloaded 10+ applications/ users, 15 databases with 100+ TB data, and 1000+ tables
The Impetus team used LeapLogic to identify the top resource consumers. As a result, resource-intensive Teradata workloads like BTEQ, mLoad, TPT, FExp, shell scripts, and associated tables were automatically identified and migrated to the modern platform, thereby offering expanded capabilities and data exploration and analytics opportunities.
Workload identification → Schema replication and data migration → Logic transformation → Workload validation and execution → Analytical data moved to data warehouse
Released 24% Teradata capacity beating the promised SLA of 20%
Impetus delivered an end-to-end production-ready solution with multiple utilities for:
• Log analysis for data ingestion, processing, orchestrating, and auditing
• Split data ingestion workflow to avoid performance bottlenecks
• Identification of reports execution time, job run status, and highest query execution time
The solution enabled the Fortune 500 telecom conglomerate to scale up its Teradata capacity, create a central data repository, and categorize, process, and analyze data to make it consumption-ready across diverse groups within the enterprise. It also enabled the telecom conglomerate to go live with code packaging, job orchestration, tuning, support for system integration test (SIT), and user acceptance testing (UAT).
- Released 24% Teradata capacity beating the promised SLA of 20%
- Auto-transformed 80% of Teradata queries
- Created a 100% production-ready enterprise data lake on time and within the designated budget