I often get questions like, how would you build a data tech stack using just open source tools? Well, I firmly believe in tools and platforms that are fit for purpose and best of breed, however here are some options if you are looking to build a pure open source data platform.
Data Ingestion - Tools that help you gather data from various sources:
- Airbyte: An open-source ELT (Extract, Load, Transform) tool to move data from APIs, databases, and other data sources into data warehouses. Comes with many pre-built connectors. Self hosted version is free.
- Apache Kafka: A distributed event streaming platform that’s often used for real-time data ingestion and streaming. Its ability to handle large-scale, high-throughput data makes it a popular choice for companies dealing with massive amounts of data
Data Storage - Where you store raw or processed data. I break this down by the type of data you want to store
Relational
- PostgreSQL: Highly extensible, ACID-compliant, supports advanced features like full-text search, geospatial data (PostGIS), JSON, and time-series data. It’s known for stability, performance, and extensibility, with a large community and support for both OLTP and OLAP workloads.
- MySQL / MariaDB: Reliable, supports replication, clustering, and full-text indexing. One of the most widely used databases, especially in the LAMP stack, with MariaDB being a community-driven fork of MySQL.
NoSQL
- Apache Cassandra: A highly scalable NoSQL database designed to handle large amounts of unstructured data. Excellent for use cases requiring large-scale, distributed systems (e.g., IoT, logs, social networks), with strong availability and eventual consistency
Columnar
- ClickHouse: Columnar database, extremely fast query performance, compression, supports OLAP queries. Designed for high-speed queries on large datasets (billions of rows), ClickHouse is ideal for real-time data analytics and reporting.
- DuckDB: in-process SQL OLAP database management system (DBMS) designed for efficient analytical queries. It's optimized for running on a single machine, making it suitable for local development, embedded applications, and integration within data science workflows
Time-Series
- Prometheus: Monitoring and alerting systems. Time-series data storage, dimensional data model, powerful querying capabilities (PromQL). It’s widely adopted for monitoring metrics in cloud-native environments, especially Kubernetes.
Distributed File System
- MinIO (Community/Upstream): Object storage, similar to Amazon S3. High performance, distributed, compatible with S3 API. Often used for object storage in private cloud or hybrid cloud setups, MinIO is lightweight but highly scalable.
Graph
- Neo4j (Community Edition): General-purpose graph database with a large community and ecosystem. ACID-compliant with native graph storage.
Supports both property graph and the Cypher query language.
Powerful traversal and graph algorithms. Scales to handle complex relationships between billions of nodes and edges.
Key-Value
- LevelDB: Lightweight key-value storage. Fast reads and writes, embeddable, supports arbitrary data types as values. Good for use in applications where a simple, embeddable key-value store is required, such as mobile apps or browsers.
Data Processing - Tools for transforming and analyzing data:
- Apache Spark: A powerful distributed computing system for big data processing, real-time stream processing, and machine learning.
- Apache Flink: stream-first data processing engine that excels at real-time data processing, though it also supports batch processing. It provides sophisticated windowing and stateful computations.
- Pandas (Python): Pandas is a Python library that provides high-performance, easy-to-use data structures and analysis tools. It’s best for small to medium datasets.
Data Orchestration - Managing and automating workflows:
- Apache Airflow: one of the most popular open-source tools for orchestrating workflows. It allows users to define complex data workflows as Directed Acyclic Graphs (DAGs) using Python.
Data Warehousing - Where processed data is stored for querying and analysis:
- ClickHouse: ClickHouse is a fast, columnar database management system designed for real-time analytics and online analytical processing (OLAP). It excels at executing analytical queries on large datasets with extremely low latency.
- Apache Druid: Druid is a high-performance, real-time analytics database designed to handle streaming and batch data. It excels at fast, real-time queries, making it a popular choice for time-series data and interactive dashboards
- Trino: is a distributed SQL query engine for big data. While not a traditional data warehouse, it allows users to query data from various sources (HDFS, relational databases, S3, etc.) in a distributed manner. High performance, handles complex queries.
Data Visualization and BI - Tools for visualizing and reporting insights
- Metabase: An open-source BI tool that allows users to query databases without writing SQL and visualize data. It is very user-friendly, allowing business users to quickly create visualizations without writing any code. It’s perfect for smaller teams or businesses looking for simple, fast insights.
- Superset: A modern data exploration and visualization platform. Superset is highly flexible, allowing complex visualizations and queries. It’s designed to handle large-scale data and be deployed in cloud environments, making it ideal for enterprises.
- Redash: An open-source tool for connecting to various data sources and visualizing queries. It is lightweight, easy to use, and designed for data teams to query databases directly and visualize the results. It’s also highly collaborative, making it suitable for team environments.
Data Governance & Observability - Tools that ensure the health and security of your data pipeline
- Apache Atlas: A scalable and extensible set of core foundational services for the management of data governance. It provides a framework for defining, managing, and sharing metadata across various data sources, enabling organizations to establish a comprehensive understanding of their data assets.
- Amundsen: A data discovery and metadata engine that enables data visibility and data governance. By providing a centralized catalog of data assets, rich metadata, and lineage tracking, Amundsen enhances the productivity of data teams and supports data governance initiatives. Developed by Lyft.
Reach out if you'd like to know more or how Data Canuck can help you in your data journey.
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