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Templates & starter projects
Copy-paste starting points: layered dbt projects, pipeline scaffolds and full starter repos with tests, CI and sample data already wired in.
dbt Project Template
A layered dbt project (staging/intermediate/marts) with tests, docs, a CI workflow that builds against a staging schema, and a source freshness config.
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Local Docker Data Stack
A docker-compose stack (Postgres, Kafka, MinIO, Spark, and a DuckDB CLI) for developing and testing pipelines entirely offline.
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Airflow DAG Template
An idempotent DAG skeleton with task groups, retries and backoff, SLA, pools, and a sensor using reschedule mode.
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Data Contract Template
A contract skeleton (ownership, schema, semantics, SLA, compatibility policy) with the CI check that enforces it and a deprecation checklist.
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PySpark Project Template
A testable PySpark project: src/transforms, tests with a local Spark session, a Dockerfile, and a Makefile that runs lint + tests offline.
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Kafka Streaming Job Template
A structured-streaming job with checkpointing, watermarking, a dedupe stage and an idempotent MERGE sink to Delta/Iceberg.
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Terraform AWS Data Platform
S3 buckets with lifecycle + encryption, Glue catalogue, IAM roles scoped to least privilege, and Athena workgroups with query limits.
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dbt → warehouse → tests → docs
Transform raw landed data into a tested, documented star schema with dbt, and gate every change on data-aware CI.
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API → S3 → Spark → Parquet
Land a paginated REST API into S3 raw, transform with PySpark into partitioned Parquet, and make the whole thing idempotent and testable offline.
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Pipeline → DQ → quarantine → publish
Put a contract in front of a table: validate each batch, publish the clean rows, quarantine the rest with reasons, and alert on the quarantine rate.
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S3 → Glue → Athena
A serverless, query-in-place pattern: land Parquet in S3, catalogue it with Glue, and query with Athena while keeping scanned bytes (and cost) low.
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Kafka → Spark Streaming → Delta
A low-latency stream into Delta Lake with watermarking, windowed aggregation and idempotent upserts that survive replays.
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Postgres → CDC → Kafka → Lakehouse
Capture changes from Postgres with Debezium, stream through Kafka, and merge into a lakehouse table with correct ordering, deletes and replay safety.