BigQuery Optimization for Global Cloud-Based Learning and Talent
Client
The client is a leading global provider of cloud-based learning
and talent management software. The company offers
comprehensive solutions designed to help organizations
recruit, train, manage, and engage their employees effectively.
Project Context
The client needed to manage large-scale data analytics while controlling costs without compromising the efficiency and speed of data retrieval and analysis.
Challenges
High costs due to dropping and recreating large tables with every ETL job
Inefficient use of computing resources
Need for scalable, cost-effective analytics environment
Solution
Historical Data Preservation using persistent tablesIncremental Data Processing to fetch only recent data
Column pruning and SQL join optimization
Table clustering and partitioning
Use of Common Table Expressions (CTEs)
Project Objectives
Optimize BigQuery setup to reduce computational cost and improve processing speed. Replace inefficient ETL operations with a more strategic and incremental data approach.
Solution Delivery
SquareShift re-engineered the client’s BigQuery setup to preserve data efficiently, streamline transformations, and reduce processing cost using clustering, partitioning, and optimized SQL logic.
Testimonial
We saw an instant improvement in cost efficiency and reporting speed after SquareShift’s BigQuery optimization
