At TRM Labs, a groundbreaking petabyte-scale data analytics platform has been developed to process vast amounts of blockchain data. This platform, powered by distributed Postgres and BigQuery, serves over 500 customer queries per minute with unparalleled speed. However, as the need for multi-environment deployment arose, the limitations of these systems became evident, prompting the creation of a new data lakehouse solution using Apache Iceberg and StarRocks.
In the initial phase, TRM utilized a distributed Postgres cluster alongside BigQuery to handle analytics queries efficiently. However, with the platform’s expansion and the demand for on-premise deployments, a more scalable solution was required. This led to the development of a modern data lakehouse architecture that combines the flexibility of a data lake with the performance of a data warehouse.
The shift towards a data lakehouse was driven by the necessity for multi-site deployment capabilities and improved performance for customer-facing analytics. By leveraging Apache Iceberg for storage and StarRocks as the query engine, TRM achieved exceptional results in terms of scalability, performance, and cost-effectiveness.
Apache Iceberg’s open standards and efficient metadata management made it an ideal choice for storing and managing data across multiple locations. Paired with StarRocks, which offers ultra-low latency and high concurrency, the data lakehouse architecture proved to be a game-changer for TRM’s analytics platform.
Through rigorous experimentation and benchmarking, TRM determined that StarRocks outperformed other query engines like Trino and DuckDB in terms of response times and scalability. The seamless integration of Iceberg and StarRocks provided TRM with the flexibility to adapt to evolving technology trends and optimize performance for various workloads.
Real-world stress testing further validated the robustness of the data lakehouse architecture, showcasing its ability to handle high-concurrency workloads with minimal latency. The successful implementation of this architecture resulted in a significant improvement in query response times and overall system performance.
Looking ahead, TRM plans to delve deeper into the deployment and optimization of Apache Iceberg and StarRocks, particularly in on-premise environments. By continuing to refine and enhance their data platform, TRM aims to set new standards in blockchain analytics and financial crime detection.
The team behind TRM’s data engineering efforts, including key members like Vijay Shekhawat and Andrew Fisher, has played a crucial role in shaping the future of blockchain intelligence. Their expertise and dedication have been instrumental in driving TRM’s mission of combating financial crime and ensuring a safer financial ecosystem for all.
As TRM Labs continues to innovate and push the boundaries of data analytics in the blockchain space, they invite like-minded individuals to join their mission. By offering opportunities to work on cutting-edge technologies and make a real impact in the fight against financial crime, TRM is paving the way for a more secure and transparent financial landscape.
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