Data Pipeline Template (Spark + Kafka + ES)
Data flows. You sleep.
A production data pipeline template built on Apache Spark, Kafka, and Elasticsearch: ETL jobs, streaming aggregations, backfill utilities, and operational runbooks. The starting point for real-time analytics platforms.
Tech stack
The Problem
Connecting Spark, Kafka, and ES from scratch involves days of dependency and config debugging
Backfill jobs and streaming jobs share no code in most pipeline implementations
Operational runbooks for pipeline failures are never written until after the first incident
What's Included
Everything you need to ship production-grade code
Streaming ETL
Kafka → Spark Structured Streaming → Elasticsearch with exactly-once guarantees.
Batch Backfill
Spark batch job sharing transformation logic with the streaming job — no code duplication.
Schema Management
Avro schemas with Confluent registry. Index templates in Elasticsearch.
Operational Runbooks
Documented procedures for: lag spike, DLQ overflow, ES indexing backpressure, and job restart.
CI/CD Pipeline
GitHub Actions: lint → test → Docker build → deploy to Kubernetes or EMR.
Get the Template
One-time payment. Full source code. Lifetime updates.
Personal License
- Full source code (Scala + PySpark)
- Docker Compose dev stack
- Runbooks PDF
- Lifetime updates
Frequently Asked Questions
Scala or Python?
Both are included. Scala is recommended for production; PySpark for teams preferring Python.
Does this work on AWS EMR or Databricks?
Yes. Deployment configs for EMR, Databricks, and self-hosted Kubernetes are included.