2021 Roundup: Top 5 Blogs on Modern Data Stack
Photo by Pawel Czerwinski on Unsplash

2021 Roundup: Top 5 Blogs on Modern Data Stack

We made it! ??? Finally it’s the end of 2021, and what a year this has been. Can we call this the year when the “modern data stack” finally became mainstream? Well, only time will tell.

In the modern data stack world, this year has been a series of new companies entering the space, funding announcements, some wars, and lots of fun data memes. As a community, we’ve definitely solved a lot of challenges for data teams, but many remain for us to meet head-on in 2022.

This year, I also started bringing you my recommended reads and sharing my (meta?) thoughts on everything around metadata, the modern data stack, and data teams. Thank you for giving this newsletter space in your inbox. If you haven’t subscribed yet, you can subscribe to the newsletter on Substack and connect with me on LinkedIn here .?

Welcome to a special edition of this week’s ??Metadata Weekly ?

For this edition, I am sharing my top five blogs that I enjoyed reading in 2021, along with some follow-up reading to keep you thinking. I am excited to follow the trends from these blogs and look forward to seeing how the modern data stack takes shape in 2022. Happy reading!

1. We the Purple People by Anna Filippova

A common career path for purple people can look like jumping around between what are on the surface very different roles — some flavor of analytics, data science, data engineering, operations, people management (and many more!). Each iteration builds a new and different kind of empathy for the problems at hand. Helping purple people grow in their careers is, therefore, less about gaining a predefined set of skills and more about growing the scope of their impact on the business and on others around them.

In the coming years, we will see more and more examples of purple people. You will know them by titles like "Analytics Engineer", "Operations Analyst"... or some combination of business function + "Analytics/Analyst" that doesn't yet exist but soon will.

Follow-up Reading:

2. Run Your Data Team Like a Product Team by Emilie Schario and Taylor A. Murphy, PhD

“Most data teams aren’t set up for success. For many years, data teams have been buried in the IT function. As IT functions, those data teams handled getting data out of their systems and presenting them to the stakeholder as CSVs from which the stakeholders could work their magic and come up with conclusions.

When a data team emerges in a company as an afterthought, they often end up being built like service-based departments with a “submit a ticket with a question, get a very specific answer” mindset. Data folks who are bound to this model rarely spend time being proactive. Without intentional space, they are unlikely to be anything more than ticket closers.”

Follow-up Reading:

3. It’s Time for the Modern Data Culture?Stack by me! :)

“In the past four years, the modern data stack has made a ton of progress, and it’s gone mainstream thanks to the adoption of tools like Snowflake and dbt. Gaps in the modern data stack that were there as early last year (in areas like metadata management, data governance, and observability) are quickly being filled thanks to the advent of newer tools. With so much innovation in the space, I’m certain that in the next few years, all data teams will finally have a close to “perfect” data stack.

This makes me believe that, as we enter 2022, the conversation needs to shift from the need for better tooling to the next “delta” that will finally help us create dream data teams — the?modern data culture stack. These are the best practices, values, and cultural rituals that will help us diverse humans of data (or the “purple people ” as dbt coined) come together and collaborate effectively.”

Follow-up Reading:

4. The Modern Data Experience by Benn Stancil

...the modern data stack isn’t an architecture diagram or a gratuitous think piece on Substack or a fight on Twitter. It’s an experience—and often, it’s not a great one. It’s trying to figure out why growth is slowing before tomorrow’s board meeting; it’s getting everyone to agree to the quarterly revenue numbers when different tools and dashboards say different things; it’s sharing product usage data with a customer and them telling you their active user list somehow includes people who left the company six months ago; it’s an angry Slack message from the CEO saying their daily progress report is broken again.

Follow-up Reading:

5. Bring Data Analyst to the Table by Petr Janda

“We shouldn’t hire data analysts to write SQL queries and churn out dashboards and spreadsheets. We should employ analysts to analyse and help us understand business problems using data.

Finding the correct data and putting it together is necessary, but there’s much more to do than that, and analysts are uniquely equipped to follow through.

Besides excellent data skills, the best analysts I know have a far more critical trait— a relentless curiosity that drives them forward to keep exploring.”

Follow-up Reading:

??Special Picks ?

If you’re like me and spend your downtime catching up on data reading for the year, here are a few more reads that I highly recommend.

Happy new year! :)

Avinash Avi

Senior Associate at Helical IT Solutions

8 个月

You should have a look at World's first chat based Data Engineering tool powered by AI "Ask On Data" : https://www.askondata.com Simply type in English language and create data pipelines - Zero learning curve. Type and get it done. - No technical knowledge required. Anybody can use - Automatic documentation - Super fast speed of development at the speed of typing, save around 93% of time as compared to other tools - Save money in infra by decoupling processing in case if you are using platforms like Snowflake, Databricks etc

回复
Jo?o Vaz?o Vasques

Blockchain Analytics @ Chainlink Labs

2 年

Thanks for mentioning two articles of the Building Data Platform series! Here's the third and final one for those you are interested https://joaovasques.medium.com/building-data-platforms-iii-the-evolution-of-the-software-engineer-bdb3d9c1dd71

Ramdas Narayanan

VP Client Insights Analytics (Digital Data and Marketing) at Bank Of America, Data Driven Strategist, Innovation Advisory Council. Member at Vation Ventures. Opinions/Comments/Views stated in LinkedIn are solely mine.

2 年

Thanks for all the articles, very insightful. Wishing you a very happy new year 2022, also to the entire team at Atlan.

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