Data catalog - Modern data stack
Data catalog - Modern data stack
Data catalog 3.0: Modern Metadata for the Modern Data Stack
It is necessary to implement a contemporary metadata solution that is able to match the speed, adaptability, and scalability of the modern data stack.
The data world has converged around the best set of tools for dealing with massive amounts of data, the “modern data stack”. It includes implementing a combination of top-performing tools for data ingestion, storage in data lakes, and data warehousing to establish a strong data infrastructure.
The benefits of the modern data stack include its high speed, ability to quickly increase in size, and low maintenance. However, it lacks the ability to provide governance, trust and context to data.
That is where the role of metadata becomes necessary ✨.
Why is it important for modern metadata to be just as fast, flexible, and scalable as the rest of the modern data stack? How can basic data catalogs be transformed into a powerful tool for data democratization and governance? Why does the way we manage metadata need to change in order to meet the demands of today's data landscape?
Let me know I comment your opinions about these questions.
Why does the modern data stack need “modern” metadata management more than ever?
The modern data stack is facing new challenges such as increasing data volume and complexity, need for real-time data processing and analytics, and rising demands for data governance and trust. In order to meet these demands, metadata management must evolve to be more efficient, accurate, and automated. This "modern" approach to metadata management is essential for organizations to effectively navigate and utilize their data in today's landscape.
I also realized that in today data teams, there are diverse roles - data engineers, analysts, analytics engineers, data scientists, products managers, and more. Each of them have their own favorite data tools: Jupyter, SQL, python, looker …
This diversity can be both an advantage and a challenge. Each person brings their own perspective, methods, expertise, technology, and work style, creating a unique "data DNA" for the organization.
The outcome is commonly a chaotic environment in collaboration, where simple questions such as "what does this column name signify?" 🥲 and "why are the transactions/discounts used volumes in the insights dashboard inaccurate again?"📊 lead to delays in the team's progress due to confusion with the data.
These questions have existed for some time, as evidenced by Gartner's publication of its Magic Quadrant for Metadata Management Solutions since November 2020.
Despite these ongoing issues, there has yet to be an effective solution developed. Many current data catalogs are outdated, providing only temporary fixes and not keeping pace with the advancements and innovation of the current modern data stack.
What do you think, is there a solution that can fulfill these issues ?
The 4 characteristics of Data Catalog 3.0
From my analysis of various sources such as articles, research reports, whitepapers, and vendor product descriptions, I have identified four key characteristics that define the evolution of data cataloging:
It is built for the modern data stack and can keep up with the speed and scale of today's data.
It provides a comprehensive view of data across the entire organization, including its lineage, quality, and governance.
It is fully automated, using machine learning and AI to improve the accuracy and efficiency of data management.
It enables data democratization by making it easy for all users to discover, understand, and trust the data they need to make informed decisions.
So what's to come?
As the amount of data being generated and stored continues to grow at an unprecedented rate, it's more important than ever to have a robust and efficient data catalog system in place.
A modern data catalog allows for easy discovery, organization, and management of data assets, making it a crucial component of any data-driven organization. With the advent of new technologies and advancements in metadata management, we can expect to see an evolution of data catalogs in the coming months/years, providing even more powerful and intuitive ways to manage and utilize data.
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