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Key Takeaway
Migration from Databricks to BigQuery simultaneously improved analytical performance and cost efficiency
Successfully migrated tables, queries, and notebooks from the Databricks environment to BigQuery, improving query performance and building a cost-effective and scalable data analytics environment based on a serverless architecture.
Fandom platform (H Company)
Client :Fandom platform (H Company)
Industry :Telco / Media / Software
Service Area :Data & AI
Applied Solution :AIR
1. Overview (Project Background)
This project was pursued with the goal of comprehensively optimizing data pipelines and related processes to
maximize data utilization and analytical efficiency.
In the existing environment, there were continuous demands for improvement in analytical query performance, operational complexity, and cost aspects.
To address these issues, a transition to a more efficient data platform that could reduce data management costs in the long term and improve analytical speed was necessary.
In particular, query performance improvement, cost efficiency through migration,
mitigation of operational management burden, and ease of integration with various GCP services were important considerations.
2. Solution (Resolution Approach)
In this project, the core objective was set to migrate data stored in the Databricks platform to GCP BigQuery,
and work was performed to stably transition the existing analytical environment to a BigQuery-based platform.
To achieve this, tables, queries, and notebook assets used in the Databricks environment were restructured to fit the BigQuery environment,
and the main implementation details are as follows.
Newly defined and created table structures suited to the BigQuery environment
Modified and converted code and SQL queries used in Databricks to match BigQuery syntax
Executed converted code and queries in the actual BigQuery environment to
verify error occurrence and result accuracy through validation procedures
Through this, stability and reliability were secured in the data migration process.
3. Result (Achievements)
Key Improvements
By leveraging BigQuery's serverless architecture and optimized query engine,
the processing speed of large-scale data analysis queries was improved,
and the costs and burden associated with infrastructure operations and management were reduced.
Additionally, based on Google Cloud's strong security and stable infrastructure,
we were able to build a stable and scalable data platform environment,
and secured a structure that can flexibly respond to future data growth.
By consolidating functions necessary for data analysis and processing around BigQuery,
we established an environment where data-driven decision-making can be performed more quickly and efficiently.
