claude

polymath-streaming

Data & streaming craft: Kafka and Event Hubs, stream processing, pipelines, lakehouse performance, lake layout, schema evolution.

0 on GitHubby Mohammad Bafkarv0.12.0MITupdated 15 days agodata
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Plugin polymath-streaming from marketplace polymath in MohammadBafkar/polymath. Commands read from the repo's manifest — 12 days ago.

What it installs

Skills (9)

  • azure-event-hubs-design

    Design and size Azure Event Hubs — tier, throughput units, partitions, consumer groups, checkpointing, Capture, Kafka endpoint.

  • data-lake-layout

    Design the physical data lake — zones, paths, Parquet and Delta/Iceberg, file sizing, partitioning, compaction, lifecycle tiers.

  • data-modeling

    Model analytical data — dimensional (star/snowflake) vs normalized vs lakehouse medallion layers, grain, partitioning, slowly-changing dimensions, and schema evolution.

  • data-pipeline

    Design a data pipeline — ETL vs ELT, batch vs streaming, orchestration/DAG, idempotent backfills, schema handling, data-quality checks, and lineage.

  • event-schema-evolution

    Evolve event schemas safely — registry, Avro/Protobuf compatibility modes, upgrade order, breaking-change playbook, CI contract checks.

  • kafka-performance-tuning

    Tune Kafka throughput, latency and consumer lag — producer batching, acks, fetch, rebalances, broker IO. Not topology (streaming-design).

  • lakehouse-performance

    Tune Databricks/Spark and Delta — small files, OPTIMIZE, clustering, shuffle, skew, Auto Loader, cluster cost. Not lake layout.

  • stream-processing

    Design stream processing — windowing (tumbling/sliding/session), watermarks for late data, stateful operators, exactly-once, stream-stream/table joins, and CEP.

  • streaming-design

    Design a streaming topology (Kafka/Pulsar/Kinesis) — topics, partitioning key, delivery semantics, consumer groups, retention/compaction, ordering, and backpressure.

View source on GitHub

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