In the age of big data, open-source tools have become the backbone of modern data engineering. They help build robust, scalable, and flexible data platforms—without the vendor lock-in or high costs of proprietary solutions.

In this blog post, I’ll walk you through 10 powerful open-source tools that every data engineer should know and ideally have in their stack.

1. Apache Airflow 🛠️

Category: Workflow Orchestration

Apache Airflow is the industry standard for orchestrating complex data workflows using DAGs (Directed Acyclic Graphs). It lets you schedule and monitor data pipelines, and has a vibrant ecosystem with providers for AWS, GCP, Spark, and more.

  • Use cases: ETL/ELT orchestration, ML pipelines, data validation
  • Bonus: The TaskFlow API and dynamic DAGs make development faster and more Pythonic.

2. DBT (Data Build Tool) 🧱

Category: Data Transformation

DBT brings software engineering practices to SQL-based transformations. It lets analysts and engineers write modular SQL models with version control, testing, documentation, and lineage.

  • Use cases: ELT transformations in Snowflake, BigQuery, Redshift, Databricks
  • Bonus: DBT Cloud and DBT Core both support modern data stack workflows.

3. Apache Kafka ⚡

Category: Real-Time Data Streaming

Kafka is a distributed event streaming platform used to build real-time data pipelines and streaming applications. It guarantees high throughput, fault tolerance, and scalability.

  • Use cases: Event-driven architectures, real-time ingestion, data lake streaming
  • Bonus: Kafka Connect and Kafka Streams extend its power for integration and processing.

4. Great Expectations ✅

Category: Data Quality & Validation

Great Expectations helps you define, document, and test data pipelines. It acts as a unit testing framework for data and integrates seamlessly with tools like Airflow, DBT, and Spark.

  • Use cases: Data quality checks, CI/CD testing in pipelines, data validation reports
  • Bonus: Data Docs make data profiling and test reporting beautiful and shareable.

5. Apache Flink 🔄

Category: Stream & Batch Processing

Flink is a powerful engine for processing unbounded (streaming) and bounded (batch) data. Unlike Spark, Flink is native to stream processing and supports complex event-time windowing.

  • Use cases: Real-time analytics, fraud detection, anomaly detection
  • Bonus: Used by Uber, Alibaba, and Netflix for massive stream workloads.

6. Metabase 📊

Category: Open Source BI / Data Visualization

Metabase makes it easy to explore, visualize, and share data with non-technical users. It supports SQL and GUI-based query building, dashboards, and alerting.

  • Use cases: Self-service BI, data exploration, real-time dashboards
  • Bonus: It can be embedded into apps for white-labeled analytics.

7. Delta Lake 🧬

Category: Storage Layer / Data Lakehouse

Delta Lake, developed by Databricks, adds ACID transactions, schema enforcement, and time travel to Apache Spark and cloud object storage (S3, ADLS, GCS).

  • Use cases: Reliable data lakes, lakehouse architecture, time travel queries
  • Bonus: Works out of the box with Apache Spark.

8. Apache Iceberg ❄️

Category: Table Format for Data Lakes

Iceberg is an open table format designed for large analytic datasets. It supports schema evolution, partition evolution, and is optimized for performance in data lakes.

  • Use cases: Lakehouse systems, data versioning, time travel
  • Bonus: Supported by engines like Trino, Presto, Spark, and Flink.

9. OpenLineage 🔗

Category: Data Lineage & Metadata

OpenLineage provides a standard for metadata collection and lineage tracking across various tools. It helps you understand how data flows across your pipelines.

  • Use cases: Governance, debugging, auditing, impact analysis
  • Bonus: Integrates with Airflow, DBT, Spark, and more through the Marquez project.

10. Dagster 🌀

Category: Orchestrator + Metadata-Aware Pipelines

Dagster is a modern orchestration tool that emphasizes developer experience and data asset lineage. Unlike Airflow, it comes with a strong typing system, testing support, and native asset tracking.

  • Use cases: ELT/ETL, ML pipelines, asset-aware orchestration
  • Bonus: It enables building modular, testable pipelines with built-in observability.

🔚 Final Thoughts

These open-source tools are transforming how data is processed, validated, stored, visualized, and governed. Whether you’re building a real-time ingestion system or managing a data lakehouse, incorporating the right combination of these tools will boost your productivity and system reliability.

👉 Which of these tools do you use in your data engineering stack? Let me know on LinkedIn!

Stay curious,
Prashant Singh