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Data Architecture & Engineering

Data Engineering, DataOps, Data Science, Machine Learning, and AI are considered specialty occupations. On a daily basis, both engineers and data scientists in these categories, work on different frameworks and techniques to support their company’s data strategy.


Data Engineering, DataOps, Data Science, Machine Learning, and AI are considered specialty occupations. On a daily basis, both engineers and data scientists in these categories, work on different frameworks and techniques to support their company’s data strategy.

Photo by Lakshmi Shiva Ganesh Sontenam

Data architecture is the foundation of any data strategy. The goal of any data architecture is to show the company’s infrastructure on how the data is acquired, transported, stored, queried, secured, and analyzed.

Data and Operations engineers build and maintain these infrastructure systems that allow Data and ML scientists to access and interpret data. “A good sketch is better than a long speech,” said, Napoleon Bonaparte. Hope the diagram presented above helps the audience as a great quick reference, to spot the key components and techniques in building a state-of-the-art Data & Analytics architecture.

Also, below is a presentation of the enterprises’ mostly used open-source data stack based on the aforementioned data architecture and engineering principles. Apache open-source tools are still empowering the data teams to work in unimaginable ways for the past 5–10 years or more.

From data engineers building efficient data pipelines to analytics engineers adopting software engineering best practices, open-source makes it possible to enable productive engineer workflows and brings a great vibe to the data team culture, given its community nature. There are tools for data engineers to own the data ingestion and orchestration and tools for analytics engineers to transform data — operational analytics tools to get analytically derived data into operational systems, and more.

As a result, we are no longer stuck in the single-enterprise-analytics mindset. We can choose the open-source tools that work best for us at each point in building our data pipelines. We get more flexibility, not locked into a single enterprise vendor, but also allow us to integrate with the commercial products we choose. Above all, Data protection and privacy are no longer a “nice-to-have.” Open-source gives us a chance to “own” our data. Data governance, observability, and discovery to just get started. It takes less time and capital than ever to get data into warehouses and lakes, transform it into models, and build dashboards.

Over the period, open-source has continued to drive innovation on the modern data stack, but that’s for another post. Massive respect to all the engineers and enterprises who have contributed such wonderful open-source projects to the community.


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