Open Source Analytics Studio

You will not regret building your our open source analytics infrastructure. It like a treasure chest of tools and knowledge, just waiting to be unlocked. Why settle for canned, off-the-shelf solutions when you can build your own custom setup that fits your exact needs and preferences?

With an open source approach, the possibilities are endless. Need to wrangle some messy data? No problem, there’s a tool for that. Want to build a quick web app? Piece of cake, just fire up your favorite web development framework. And don’t even get me started on the joy of scrapping, acquiring, exploring and visualizing your data with freely available tech… it’s like mining gold with free tools…why not? Eventually you can grow into commercial solutions if needed.

Plus, with the community-driven nature of open source, you’ll never be alone on your quest for data and analytics enlightenment. There’s a whole world of fellow data nerds out there, ready to help you troubleshoot, share tips and tricks, and even collaborate on projects. So why settle for a boring, closed-off data infrastructure when you can have a fun and vibrant open source one? Let’s go forth and conquer the data jungle together!

  • Compute and Storage Server (aka Private Cloud): Set up a reliable compute and data storage system to store and process your data including a network-attached storage (NAS) device, SSD and/or HDD, upgradable CPU and GPU.
  • Accessible Database: Choose and set up a database management system (DBMS) that can handle your data storage and retrieval needs. Open-source options like MySQL, PostgreSQL, and MongoDB are popular choices.
  • Analytics environment: Choose and set up analytics tools that can help you analyze and visualize your data, such as R, Python, and SQL with Anaconda and/or Jupyter Notebook.
  • Data pipeline and monitoring automation: Build data pipelines that can automate the process of collecting, cleaning, and transforming your data from various sources into a format that can be analyzed and track changes over time and detect anomalies or trends with automate data refreshes. Open-Source options like Airflow.
  • Data visualization platform: Build visualizations to help you explore and communicate your data, such as scatter plots, bar charts, and heat maps.
  • Data security controls: Ensure that your data is secure by setting up access controls, backups, and encryption to protect sensitive data.
  • Code Development Environment: Ability to integrate a wide range of programming languages, workflows with intelligent code completion, debugging, and Git integration. VSCode is one of the best open-source options.
  • Documentation and collaboration: Document your processes and tools so that others can understand and replicate your work, and collaborate with others to build a community of data enthusiasts. Use tools like GitHub and Jupyter notebooks to facilitate collaboration and version control.