Hoodie lowers data latency across the board, while simultaneously achieving orders of magnitude of efficiency over traditional batch processing.

Hoodie manages storage of large analytical datasets on HDFS and serve them out via two types of tables

  • Read Optimized Table - Provides excellent query performance via purely columnar storage (e.g. Parquet)
  • Near-Real time Table - Provides queries on real-time data, using a combination of columnar & row based storage (e.g Parquet + Avro)
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By carefully managing how data is laid out in storage & how it’s exposed to queries, Hoodie is able to power a rich data ecosystem where external sources can be ingested into Hadoop in near real-time. The ingested data is then available for interactive SQL Engines like Presto & Spark, while at the same time capable of being consumed incrementally from processing/ETL frameworks like Hive & Spark to build derived (Hoodie) datasets.

Hoodie broadly consists of a self contained Spark library to build datasets and integrations with existing query engines for data access.

Hoodie is a new project. Near Real-Time Table implementation is currently underway. Get involved here