Airflow vs Dagster vs Prefect: Data Orchestration Compared (2026)
Airflow vs Dagster vs Prefect compared: execution model, asset vs task orchestration, backfills, dynamic pipelines, observability, and a decision matrix for 2026.
Feature Store Architecture: Online/Offline Parity and Point-in-Time Correctness
A feature store architecture deep-dive: online/offline parity, point-in-time correct joins, materialization, and the registry - how to stop training/serving skew in production ML.
Flink vs Spark Streaming vs Kafka Streams (2026)
Flink vs Spark Structured Streaming vs Kafka Streams in 2026: processing model, state, exactly-once, latency, and a decision matrix for your streaming workload.
Apache Iceberg vs Paimon: Lakehouse Table Formats 2026
Apache Iceberg vs Apache Paimon in 2026: streaming upserts, compaction, metadata design, catalog support, and a decision matrix for your lakehouse table format.
Kafka vs Redpanda vs WarpStream: Edge Telemetry ADR
An ADR-style 2026 comparison of Kafka, Redpanda, and WarpStream for edge telemetry: cost, latency, ops burden, and a clear decision framework.
Iceberg vs Delta vs Hudi for Lakehouses: 2026 ADR
An ADR for picking your lakehouse format in 2026 — Iceberg, Delta, and Hudi compared on writes, streaming, time travel, and ecosystem.
InfluxDB 3 vs TimescaleDB vs ClickHouse for IoT Time Series (2026)
Decision matrix comparing InfluxDB 3.0, TimescaleDB 2.17, and ClickHouse 24 for IoT time-series workloads — ingestion rates, compression, query latency, and TCO at scale.
Iceberg vs Delta Lake vs Hudi: Lakehouse Table Format ADR (2026)
ADR-style comparison of Apache Iceberg, Delta Lake, and Apache Hudi for production lakehouses — consequences, trade-offs, migration paths, and a decision rubric for 2026.
Trino vs Presto vs Apache Spark: Lakehouse Query Engines Compared (2026)
Trino vs Presto vs Apache Spark for the lakehouse: MPP vs batch execution, latency, concurrency, Iceberg support, cost, and a decision matrix for query engines in 2026.
TimescaleDB Internals: Hypertables, Chunks, Compression, and Continuous Aggregates (2026)
TimescaleDB internals explained: hypertables and chunk partitioning, columnar compression, continuous aggregates, retention, and tuning Postgres for time-series at scale.