Official twitter handle of Apache Hudi, an open data lakehouse platform. https://xitter.cf/t.co/SXay7oHNah

Joined January 2019
Hudi 1.0 is the most powerful release to date for data lakehouses. Read the blog for details: Secondary Indexing, Expression Indexes, Partial Updates, Non-blocking Concurrency Control, New LSM timeline, +more: hudi.apache.org/blog/2024/12… #datalakehouse #opentableformat
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A Hudi upsert is just a Spark DAG. Tune it like one. Two phases ↓
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• hoodie.memory.merge.fraction — bump if upserts spill to temp folder • Timeseries / log data — tune bloom accuracy OR use bucket index; time-prefix keys for range pruning Links ↓ #ApacheHudi #SparkTuning
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Unsafe (workarounds required): non-nullable column (make it nullable), datatype demotion (rewrite), field reorder (breaks partial-coverage upsert reads).
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For column renames + deletes + complex evolution: opt into schema-on-read with hoodie.schema.on.read.enable=true. Spark 3.2.1+, can't be disabled once on. Proven at scale at Uber, Walmart, LinkedIn. Links ↓ #ApacheHudi #SchemaEvolution
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Upstream added a column. Did your pipeline survive? Hudi supports schema evolution on write out of the box — the changes that happen in production all the time.
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Safe (writes succeed, reads return the full dataset): add nullable column at root or nested level, add complex type with default, promote int→long / float→double / string↔bytes, add Hudi meta columns.
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Upstox — one of India's largest online trading platforms — ingested 300 GB of new data per day for 12 million customers on Apache Hudi. Capital markets workloads stress every part of a lakehouse ↓
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Streaming-first lakehouse, built for upserts on S3 — what trading-day workloads require. 300k data requests served daily. Links ↓ #ApacheHudi #FinTech
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