Open Source Big Data Tools

I've built a batch data pipeline with observability, data quality, and lineage tracking using open-source tools. → Apache Airflow for orchestration → DuckDB for fast analytical processing → Delta Lake for ACID transactions on data lakes → MinIO (S3-compatible) for storage → Trino Software Foundation for distributed SQL queries → Metabase for dashboards → Soda core for data quality checks → marquez + OpenLineage for end-to-end lineage → Prometheus Group + Grafana Labs for monitoring The pipeline follows the Medallion Architecture (Bronze → Silver → Gold) with automated data cleaning, validation, and business aggregations. Everything runs with Docker, including automated dashboard generation in Metabase and Grafana. If you're into data engineering or operating data pipelines, check it out: https://lnkd.in/euD5mndA Happy to hear your thoughts or if you're working on something similar.

Open Source is Eating the Data Stack. What's Replacing Microsoft & Informatica Tools? I've been reading a great discussion about replacing traditional proprietary data tools with open-source alternatives. Companies are increasingly worried about vendor lock-in, rising costs, and scalability limitations with tools like SQL Server, SSIS, and Power BI. The consensus is clear: open source is winning in modern data engineering. 💡 What's particularly interesting is the emerging standard stack that data teams are gravitating toward: • PostgreSQL or DuckDB for warehousing • dbt or SQLMesh for transformations • Dagster or Airflow for orchestration • Superset, Metabase, or Lightdash for visualization • Airbyte or dlt for ingestion As one data engineer noted, "Your best hedge against vendor lock-in is having a warehouse and a business-facing data model worked out. It's hard work but keeping that layer allows you to change tools, mix tools, lower maintenance by implementing business logic in a sharable way." I see this shift every day. Teams want the flexibility to choose best-of-breed tools while maintaining unified control and visibility across their entire data platform. That's exactly why you should be building your data platform on top of tooling that integrates with your favorite tools rather than trying to replace them. Vertical integration sounds great, if you enjoy vendor lock-in, slow velocity, and rising costs. Python-based, code-first approaches are replacing visual drag-and-drop ETL tools. We all know SSIS is horrible to debug, slow and outdated. The modern data engineer wants software engineering practices like version control, testing, and modularity. The real value isn't just cost savings - it's improved developer experience, better reliability, and the freedom to adapt as technology evolves. For those considering this transition, start small. Replace one component at a time and build your skills. Remember that open source requires investment in engineering capabilities - but that investment pays dividends in flexibility and innovation. Where do you stand on the proprietary vs. open source debate? And if you've made the switch, what benefits have you seen? #DataEngineering #OpenSource #ModernDataStack #Dagster #dbt #DataOrchestration #DataMesh

The data engineering stack just changed completely. Here's what's actually worth learning in 2025: 1. DuckDB - Replaces Spark for 80% of use cases Processed 50GB on my laptop in 30 seconds. No cluster. Just SQL. If you're spinning up Spark for < 500GB, you're wasting money. 2. Apache Iceberg - The table format wars are over Time travel, ACID transactions, schema evolution on data lakes. Building a new data lake without Iceberg? You're building legacy. 3. Polars - Pandas is done 10-50x faster. Same code structure. Handles bigger-than-RAM data. Switched our daily ETL: 2 hours → 8 minutes. Just changed the import. 4. Modal - Serverless compute that actually works Deploy Python that scales to 1000 machines. Pay per second. No more forgetting to shut down EMR clusters. 5. Evidence.dev - BI in Markdown + SQL Dashboards in Git. Peer reviews. Version control. Free. Looker costs $70/user/month. This is better. 6. Mage.ai - Airflow without the pain Visual pipelines. Built-in testing. Actually debuggable. If starting fresh, skip Airflow. 7. MotherDuck - DuckDB in the cloud Query GBs for pennies. Perfect for startups/side projects. Not ready for enterprise scale yet. The controversial take: Most teams are overengineering. You don't need: ❌ Spark for 100GB files (use DuckDB) ❌ Airflow for simple pipelines (use Mage) ❌ Snowflake for side projects (use MotherDuck) ❌ Pandas anymore (use Polars) The best stack is the simplest one that solves your problem. Stop chasing trends. Start solving problems. What new tool are you testing? 👇 #DataEngineering #DuckDB #Polars #ModernDataStack #DataTools

Building a data pipeline today does not have to mean endless code, cron jobs, and chaos. Open-source tools have evolved to make data orchestration, transformation, and automation faster and more reliable than ever. Here are the best open-source tools for building modern data pipelines 👇 1. Apache Airflow The industry standard for orchestrating, scheduling, and monitoring complex ETL workflows. 2. Prefect A Pythonic alternative to Airflow, simplifies automation with hybrid execution and intuitive workflows. 3. Dagster Helps you build testable, maintainable data pipelines with strong data quality controls. 4. Apache NiFi Visually design real-time dataflows with drag-and-drop processing for streaming and batch data. 5. DBT (Data Build Tool) Transform, test, and document your data models directly within the data warehouse. 6. Metaflow A human-friendly framework from Netflix for managing data science and machine learning workflows. 7. StreamSets Design and monitor smart data pipelines for hybrid and multi-cloud environments. 8. Luigi Automate and manage long-running batch jobs with clear dependency management. 9. Kedro Create modular, reproducible, and production-ready data pipelines with clean architecture. 10. Apache Beam Run unified batch and streaming pipelines seamlessly across multiple execution engines. 11. Flyte A Kubernetes-native orchestration platform designed for large-scale ML and data workflows. 12. Argo Workflows Manage and orchestrate containerized workflows natively in Kubernetes. 13. Bonobo Lightweight and beginner-friendly ETL framework for small, fast data transformation tasks. 14. Mage Build no-code, real-time ELT pipelines with an interactive visual interface. 15. Meltano Simplify ELT processes with Singer connectors and modular, open-source workflows. Data pipelines are the backbone of analytics and AI, and open-source tools give you the flexibility to build, automate, and scale them your way. Pick your stack wisely, and your data will never stop flowing.

dbt – free Kafka – free Spark – free Airflow – free Docker – free Parquet – free VS Code – free Postgres – free Superset – free AWS Free Tier – free Even the best open-source notebooks and data viz tools? Free. With a laptop and solid Wi-Fi, you can build a lot of leverage today. Every tool you need to become a top 1% data engineer is already out there. Every concept you need to learn, schemas, pipelines, orchestration, streaming, batch, SQL optimization, it’s on GitHub, in the docs, in open courses, waiting for someone willing to break stuff and build again. Nobody is stopping you from launching your first end-to-end pipeline, from joining the DataEngineering community, from reading warehouse benchmarks, or open-source PRs. You can deploy a large-scale warehouse on a free tier, learn distributed joins and shuffles on your own laptop, practice partitioning, build data lakes, automate with Python scripts and see exactly how the world runs behind the scenes. Don’t wait for the “perfect project” or a certificate. Don’t tell yourself you need permission, or a course, or someone’s LinkedIn thread to validate your skills. The tools are there. The docs are there. The community is there. What are you waiting for? Go build.

If your data stack still relies on “maybe” tools, you’re building tomorrow’s problems with yesterday’s gear. That’s what it’s like skipping these tools as a data engineer in 2026. Apache Spark → Because your laptop can't handle petabytes Apache Kafka → Real-time isn't optional, it's expected dbt Labs (Data Build Tool) - Analytics engineering framework. Transforms data in warehouses using SQL. The bridge between engineering and analytics. Apache Airflow - Workflow orchestration powerhouse. Schedule, monitor, and manage data pipelines programmatically. Industry standard for ETL orchestration. Snowflake - A data warehouse built for scale. Separates compute from storage. Growing 45% YoY in adoption. Databricks - Unified analytics platform built on Spark. Combines data engineering, ML, and analytics. Fastest-growing data platform. Iceberg/Delta Lake/Hudi Table formats - Data consistency is the superpower. Iceberg fixes the biggest reliability and performance issues associated with traditional data lakes. Docker, Inc & Kubernetes - Containerization of applications and cluster-level orchestration. Terraform - Infrastructure as Code (IaC) tool. Provision cloud resources reproducibly. Essential for modern data platform management. Python/SQL - Non-negotiable. Not tools, but literacy. If you can't write advanced, optimized SQL and production-grade Python (for complexity/APIs), you're not an Engineer, you're a query runner. How's the pattern? → Everything scales. Everything's distributed. Everything's in the cloud. Skip these, and you’re living dangerously. Embrace them, and you’re future-proof. Reality check: → 70% of job posts require Spark → Kafka skills grew 45% YoY → Companies pay $50K+ more for cloud-native expertise Ready to upgrade your toolbox and leave the ropes behind? ✨ Drop the one tool you can’t live without in the comments—let’s crowdsource the ultimate 2026!

Open Source Data Engineering Landscape 2025... The open source data engineering landscape continues to evolve rapidly, with significant developments across storage, processing, integration, and analytics. Current Momentum - ➟ While this growth demonstrates continued innovation, the year also saw some concerning developments regarding licensing changes. ➟ Established projects including #Redis, #CockroachDB, #ElasticSearch, and #Kibana transitioned to more closed and proprietary licenses, though Elastic later announced a return to open source licensing. ➟ These shifts were balanced by significant contributions to the open source community from major industry players. Snowflake's contribution of #Polaris, Databricks' open sourcing of Unity Catalog, OneHouse's donation of Apache XTable, and Netflix's release of Maestro demonstrated ongoing commitment to open source development from industry leaders. 💡#Apache Foundation maintained its position as a key steward of data technologies, actively incubating several promising projects. 💡#Linux Foundation has also strengthened its position in the data space, continuing to host exceptional projects such as Delta Lake, Amundsen, Kedro, Milvus, and Marquez. The Data engineering landscape 2025 ( https://lnkd.in/dPSpKkq3 ) ➟ Storage Systems: Databases and storage engines spanning OLTP, OLAP, and specialised storage solutions. ➟Data Lake Platform: Tools and frameworks for building and managing data lakes and lakehouses. ➟Data Processing & Integration: Frameworks for batch and stream processing, plus Python data processing tools. ➟ Workflow Orchestration & DataOps: Tools for orchestrating data pipelines and managing data operations. ➟ Data Integration: Solutions for data ingestion, CDC (Change Data Capture), and integration between systems. ➟ Data Infrastructure: Core infrastructure components including container orchestration and monitoring. ➟ ML/AI Platform: Tools focused on ML platforms, MLOps and vector databases. ➟ Metadata Management: Solutions for data catalogs, governance, and metadata management. ➟ Analytics & Visualisation: BI tools, visualisation frameworks, and analytics engines. The open source data ecosystem is entering a phase of maturity in key areas such as data lakehouse, characterised by consolidation around proven technologies and increased focus on operational efficiency. Source - https://lnkd.in/dpZP7WdD

A Roadmap for Data Engineering After receiving hundreds of DMs seeking guidance on Data Engineering, I’ve compiled this roadmap . What is Data Engineering? It’s about building systems to efficiently process, model, and make data production-ready. This includes formats, resilience, scaling, and security, enabling vast data to translate into insights. Key Areas to Focus On: Languages: Master SQL and at least one programming language like Python, Scala, or Java. Processing: Batch: Use Spark (de facto standard) or Hadoop. Stream: For real-time data, learn Flink or Spark Streaming. Databases: Understand SQL and NoSQL differences—schemas, scaling, and suitability for structured vs. unstructured data. Data Warehouse: Centralize data with tools like Hive or any solution your company uses. Message Queue: Kafka is the industry standard. Storage: Learn HDFS and a cloud storage solution. Delta Lake: Delta Lake (Databricks) is increasingly essential for big data applications. Cloud Computing: Familiarize yourself with tools from major cloud providers. Workflow Management: Learn Airflow for scheduling and monitoring workflows. Resource Management: Understand resource management with tools like Yarn. Fast Ingestion: For low-latency event data ingestion, learn Druid. Visualization: Tools like Power BI, Tableau, Kibana, Prometheus, or Superset help you generate reports and monitor real-time data. Let me know if I missed anything in the comments. I’ll cover each area in detail in future posts. 📌 If you find this useful, follow me, Rocky Bhatia and click 🔔 on my profile to stay updated! This version is concise, engaging, and easy to follow while retaining all key details.

In this hands-on project, we build a modern distributed data lakehouse from scratch using cutting-edge open-source technologies: Apache Iceberg, Trino, Airflow, DBT, MinIO, and Project Nessie. This end-to-end data engineering project takes you through every stage of designing, setting up, and running a fully functional data lakehouse with practical insights into distributed systems, data pipeline orchestration, query optimization, and modern data lakehouse best practices. 🎥 Full video here: https://lnkd.in/eA7itMGe #DataEngineering #ApacheIceberg #Trino #Airflow #DBT #MinIO #DataLakehouse #BigData #DistributedSystems

Explore the landscape of Open Source Data Engineering 1. Storage Systems: From relational OLTP databases like PostgreSQL and MySQL to distributed SQL DBMS like CockroachDB and TiDB, find the right storage solution for your needs. Includes NoSQL options like MongoDB and Redis for diverse data requirements. 2. Data Integration: Tools for CDC, log and event collection, and data integration platforms such as Kafka Connect, CloudQuery, and Airbyte ensure seamless data flow and event management. 3. Data Infrastructure & Monitoring: Manage and monitor your data infrastructure with tools for resource scheduling like Kubernetes and Docker, security solutions like Apache Knox, and observability frameworks like Prometheus and ELK. 4. Data Processing & Computation: Optimize your data processing with unified processing platforms like Apache Beam and Spark, batch processing with Hadoop, and stream processing with Flink and Samza. 5. ML/AI Platform: Empower your machine learning and AI initiatives with vector storage solutions like Milvus, MLOps platforms like MLflow and Kubeflow, and other AI tools for enhanced data insights. 6. Data Lake Platform: Efficiently manage large-scale data with distributed file systems like Hadoop HDFS, open table formats like Iceberg, and serialization frameworks like Parquet. 7. Workflow & Data Ops: Streamline your data operations with workflow orchestration tools like Apache Airflow, data quality solutions like Great Expectations, and data warehousing with LakeFS. 8. Metadata Management: Organize and manage your metadata with platforms like Amundsen and Apache Atlas, and ensure data security with tools like Hive and Schema-registry. 9. Analytics & Visualization: Enhance your data analysis and visualization with BI tools like Superset, query and collaboration tools like Hue, and semantic layers like Cube, AtScale, etc. #data #ai #dataengineering #opensource #theravitshow