Implement CI/CD for Data Pipelines

Medium

Pipelines

Asked at 75 companies75OrchestrationSchedulingDependencies

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Problem

Context

FinWave, a payments analytics company, runs 120 batch and streaming data pipelines across Apache Airflow, dbt, Spark, and Kafka on AWS. Today, deployments are manual: engineers merge code to main, trigger Airflow DAG updates by hand, and promote dbt/Spark changes without automated testing, causing broken DAGs, schema regressions, and inconsistent production releases.

You need to design a CI/CD platform for data pipelines that standardizes build, test, deployment, rollback, and observability across batch ETL, ELT, and stream processing workloads.

Scale Requirements

Pipelines: 120 active pipelines, growing to 250 within 12 months
Deployments: 40-60 production releases/day across DAGs, dbt models, and Spark jobs
Data volume: 15 TB/day batch + 180K events/sec streaming peak
Latency: CI feedback < 10 minutes for PR validation; CD promotion to prod < 15 minutes
Environments: dev, staging, prod across 3 AWS accounts
Recovery target: rollback or disable faulty release within 5 minutes

Requirements

Design a CI pipeline that validates Python, SQL, DAG definitions, dbt models, and Spark jobs before merge.
Implement automated tests for unit, integration, schema compatibility, and data quality checks.
Design CD workflows for Airflow DAG deployment, dbt model promotion, and Spark job release with environment-specific configs.
Ensure idempotent deployments, versioned artifacts, and reproducible builds.
Support safe rollout strategies for streaming jobs and backward-compatible schema evolution.
Include secrets management, approval gates for production, and rollback mechanisms.
Define monitoring for deployment health, pipeline failures, test flakiness, and post-release data incidents.

Constraints

AWS-first stack; existing services include EKS, MWAA, S3, EMR, Kafka MSK, Snowflake
Team of 6 data engineers and 1 platform engineer; solution must minimize operational overhead
Monthly platform budget increase capped at $18K
SOX and PCI controls require audit logs, approval history, and restricted production access
Some pipelines are stateful streaming jobs and cannot tolerate duplicate processing during redeployments

Problem

Context

You need to design a CI/CD platform for data pipelines that standardizes build, test, deployment, rollback, and observability across batch ETL, ELT, and stream processing workloads.

Scale Requirements

Pipelines: 120 active pipelines, growing to 250 within 12 months
Deployments: 40-60 production releases/day across DAGs, dbt models, and Spark jobs
Data volume: 15 TB/day batch + 180K events/sec streaming peak
Latency: CI feedback < 10 minutes for PR validation; CD promotion to prod < 15 minutes
Environments: dev, staging, prod across 3 AWS accounts
Recovery target: rollback or disable faulty release within 5 minutes

Requirements

Design a CI pipeline that validates Python, SQL, DAG definitions, dbt models, and Spark jobs before merge.
Implement automated tests for unit, integration, schema compatibility, and data quality checks.
Design CD workflows for Airflow DAG deployment, dbt model promotion, and Spark job release with environment-specific configs.
Ensure idempotent deployments, versioned artifacts, and reproducible builds.
Support safe rollout strategies for streaming jobs and backward-compatible schema evolution.
Include secrets management, approval gates for production, and rollback mechanisms.
Define monitoring for deployment health, pipeline failures, test flakiness, and post-release data incidents.

Constraints

AWS-first stack; existing services include EKS, MWAA, S3, EMR, Kafka MSK, Snowflake
Team of 6 data engineers and 1 platform engineer; solution must minimize operational overhead
Monthly platform budget increase capped at $18K
SOX and PCI controls require audit logs, approval history, and restricted production access
Some pipelines are stateful streaming jobs and cannot tolerate duplicate processing during redeployments

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Next question

Context

You need to design a CI/CD platform for data pipelines that standardizes build, test, deployment, rollback, and observability across batch ETL, ELT, and stream processing workloads.

Scale Requirements

Pipelines: 120 active pipelines, growing to 250 within 12 months

Deployments: 40-60 production releases/day across DAGs, dbt models, and Spark jobs

Data volume: 15 TB/day batch + 180K events/sec streaming peak

Latency: CI feedback < 10 minutes for PR validation; CD promotion to prod < 15 minutes

Environments: dev, staging, prod across 3 AWS accounts

Recovery target: rollback or disable faulty release within 5 minutes

Requirements

Design a CI pipeline that validates Python, SQL, DAG definitions, dbt models, and Spark jobs before merge.

Implement automated tests for unit, integration, schema compatibility, and data quality checks.

Design CD workflows for Airflow DAG deployment, dbt model promotion, and Spark job release with environment-specific configs.

Ensure idempotent deployments, versioned artifacts, and reproducible builds.

Support safe rollout strategies for streaming jobs and backward-compatible schema evolution.

Include secrets management, approval gates for production, and rollback mechanisms.

Define monitoring for deployment health, pipeline failures, test flakiness, and post-release data incidents.

Constraints

AWS-first stack; existing services include EKS, MWAA, S3, EMR, Kafka MSK, Snowflake

Team of 6 data engineers and 1 platform engineer; solution must minimize operational overhead

Monthly platform budget increase capped at $18K

SOX and PCI controls require audit logs, approval history, and restricted production access

Some pipelines are stateful streaming jobs and cannot tolerate duplicate processing during redeployments

Context

You need to design a CI/CD platform for data pipelines that standardizes build, test, deployment, rollback, and observability across batch ETL, ELT, and stream processing workloads.

Scale Requirements

Pipelines: 120 active pipelines, growing to 250 within 12 months

Deployments: 40-60 production releases/day across DAGs, dbt models, and Spark jobs

Data volume: 15 TB/day batch + 180K events/sec streaming peak

Latency: CI feedback < 10 minutes for PR validation; CD promotion to prod < 15 minutes

Environments: dev, staging, prod across 3 AWS accounts

Recovery target: rollback or disable faulty release within 5 minutes

Requirements

Design a CI pipeline that validates Python, SQL, DAG definitions, dbt models, and Spark jobs before merge.

Implement automated tests for unit, integration, schema compatibility, and data quality checks.

Design CD workflows for Airflow DAG deployment, dbt model promotion, and Spark job release with environment-specific configs.

Ensure idempotent deployments, versioned artifacts, and reproducible builds.

Support safe rollout strategies for streaming jobs and backward-compatible schema evolution.

Include secrets management, approval gates for production, and rollback mechanisms.

Define monitoring for deployment health, pipeline failures, test flakiness, and post-release data incidents.

Constraints

AWS-first stack; existing services include EKS, MWAA, S3, EMR, Kafka MSK, Snowflake

Team of 6 data engineers and 1 platform engineer; solution must minimize operational overhead

Monthly platform budget increase capped at $18K

SOX and PCI controls require audit logs, approval history, and restricted production access

Some pipelines are stateful streaming jobs and cannot tolerate duplicate processing during redeployments