Open Data Stack
As data engineering evolves, it’s crucial to understand the potential of open-source solutions that adhere to open standards, providing an integral and adaptable framework known as the Open Data Stack. This approach outshines the term ‘Modern Data Stack,’ which has garnered some negativity and created confusion within the industry. So, what exactly is the Open Data Stack, and why should we focus on it?
The Open Data Stack addresses all aspects of the Data Engineering Lifecycle. While it shares the same goal as the Modern Data Stack, the Open Data Stack provides better tool integration due to its open nature, resulting in greater usability for data practitioners. The key word here is open, which implies that the tools or frameworks employed are either open-source or compliant with Open Standards. This openness facilitates tools like Dremio, a data lakehouse platform. While Dremio itself isn’t open source, it operates based on open standards like Apache Iceberg and Apache Arrow, allowing for seamless integration without vendor lock-in for larger organizations.
Some data practitioners propose alternative names such as ngods (new generation open-source data stack), ‘DataStack 2.0’’, or ‘DAD Stack’. Nonetheless, the essence remains the same: better, more integrated tooling is used by more individuals within every company that can genuinely comprehend the data it manipulates. This contemporary data stack will significantly differ from its predecessors.
It’s important to note the core distinction between ‘old data stack vs. modern data stack,’ monolith vs. microservices’, or ‘orchestrations vs choreography’. These terms illustrate the ongoing shift from Monolith Data, bundled solutions to a more microservices-driven, unbundled approach. This shift enables data pipelines to operate as ‘microservices on steroids,’ improving scalability and alignment across various code services.
The Open Data Stack is accessible and maintained by all users, fostering an environment where companies can leverage existing, tested solutions instead of re-implementing critical components for each data stack component. In effect, the ‘open’ aspect makes this stack embeddable with various tools, unlike closed-source services. This level of integration allows you to easily incorporate tools like Airbyte, dbt, Dagster, Superset, and more into your services.
You might wonder which ones to adopt in a world with over 100 tools. This guide introduces the Open Data Stack, emphasizing the advantages of reusing and building upon existing solutions. With the open data stack approach, you no longer need to write custom code for each step of the data engineering lifecycle. Instead, the Open Data Stack allows you to quickly address common challenges, ranging from data extraction and visualization to monitoring and scaling. Consequently, the Open Data Stack ensures that you’re not reinventing the wheel but building upon proven foundations to expedite and streamline your data engineering processes.
Whether you’re just starting to delve into the field or seeking to enhance your existing knowledge, the Open Data Stack provides robust, flexible solutions to help you master data engineering. Check out the GitHub repo Open-Data-Stack to see the Open Data Stack in action and start your journey toward a more effective and efficient data-driven operation.
Better, more integrated tooling, used by more humans inside of every company, that actually understands the data that it is operating on.
This modern data stack—if we still want to call it that!—will be unrecognizable to its former self.