StarTree's Apache Iceberg integration addresses the architecture bloat problem prevalent in data lakehouse setups. Organizations often struggle to provide customer-facing data products due to complicated multistep processes that introduce latency and complexity. Traditional methods rely on separate query and serving layers, leading to operational inefficiencies and higher costs. StarTree innovatively combines these layers, utilizing Apache Pinot's capabilities for real-time indexing and caching on Iceberg data. This approach allows for a streamlined architecture that enhances performance and reduces the need for intermediate systems.
This introduces latency, complexity and what we call 'bloat,' explains Chad Meley, SVP of Marketing at StarTree. We're collapsing that serving and query layer into one piece of the puzzle, significantly reducing the bloat and simplifying that architecture.
We're leveraging all the unique things about Apache Pinot and applying it to Iceberg, notes Chinmay Soman, Head of Product at StarTree. We have various kinds of indexes: Numerical, JSON, geospatial.
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