Issues we've encountered while building a Kafka based data processing pipeline

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  • core

    Build platforms that flexibly mix SQL, batch, and stream processing paradigms (by gazette)

  • If you're in the Go ecosystem, Gazette [0] offers transactional integrations [1] with remote DB's for stateful processing pipelines, as well as local stores for embedded in-process state management.

    It also natively stores data as files in cloud storage. Brokers are ephemeral, you don't need to migrate data between them, and you're not constrained by their disk size. Gazette defaults to exactly-once semantics, and has stronger replication guarantees (your R factor is your R factor, period -- no "in sync replicas").

    Estuary Flow [2] is building on Gazette as an implementation detail to offer end-to-end integrations with external SaaS & DB's for building real-time dataflows, as a managed service.

    [0]: https://github.com/gazette/core

  • pg_cron

    Run periodic jobs in PostgreSQL

  • Maybe something like https://github.com/citusdata/pg_cron or if it's not a rolling time window, just trigger functions that get called whenever some expression in SQL is true.

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    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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