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The Hugo evolution: Engineering Grab's unified, one-click data ingestion platform with Apache Flink
Grab dramatically reduced data pipeline onboarding from days to minutes by unifying self-service data ingestion on a new Flink-based platform called Hugo, replacing Kafka Connect and Sprinkler.
What: Grab's Hugo platform evolved from siloed data ingestion workflows to a unified, automated Flink-based system for MySQL CDC and Kafka pipelines. This change reduced operational overhead, eliminated schema risks via dynamic validation, and increased new pipeline adoption significantly.
Why it matters: This case study demonstrates how consolidating and automating fragmented data ingestion tools with a stream processing engine like Flink can yield massive productivity gains and democratize data access within a large organization.
Takeaway: Consider adopting a unified streaming data platform like Apache Flink for CDC and Kafka ingestion to reduce operational complexity and accelerate data onboarding.
Decoder
- CDC (Change Data Capture): A set of software design patterns used to determine and track changes to data so that actions can be taken based on those changes. In databases, this often means reading transaction logs (binlogs).
- Apache Flink: An open-source stream-processing framework for distributed, high-performing, and always-on data applications. It can perform stateful computations over unbounded and bounded data streams.
- Kafka Connect: An open-source framework for connecting Kafka with other systems, allowing data to be streamed in and out of Kafka.
- Apache Iceberg: An open table format for huge analytic datasets. Iceberg adds SQL table capabilities to files in data lakes, like schema evolution, hidden partitioning, and time travel.
- Confluent Schema Registry: A service for storing and retrieving Avro, Protobuf, and JSON Schema schemas. It helps ensure data compatibility and evolution in Kafka-based systems.
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