Recap of an article I wrote with

Dear Data-Traveller, please note that this is a Linkedin-Remix.

I posted this content already on Linkedin in February 2022, but I want to make sure it doesn´t get lost in the social network abyss.

For your accessibility-experience and also for our own content backup, we repost the original text here.

Have a look, leave a like if you like it, and join the conversation in the comments if this sparks a thought!

Original Post:

Plain Text:

One of the fun things in data is that you find something new every week. It can even be a complete new category that 2 years later is valued as a billion category.

So for me it’s always fun to read predictions about what could be important in the next 12 months (no one looks further). Rockset collected “17 New things every Modern Data Engineer should know in 2022”. Link in the comments.

Read about:
Barr Moses writes about – why Data Products are more than just a buzzword but a significant step to bring data setups to the same level where software development already is.

Michael Del Balso introduces one thing I am quite excited about – Fresh features in real-time ML – crossing my fingers for better personalised app experiences based on these approaches

Zack Khan highlights the advantages of putting the hard modelled data back into the tools your Sales and Marketing teams are using every day. Instead of letting them digest dashboards.

Robert Sahlin points out the entrance of Domain driven design into data setup design. Something I also highly believe in and already start to apply it in small scale

Benjamin Rogojan reminds us that we are serving the other business teams. When we can make the data accessible and work for them we do our job.

David Serna brings something up that I am also trying to tell people in data. You need to learn SQL – it opens up the modern data stack to you.

Alex DeBrie brings up something essential – for careful consideration in choosing your technology and for honesty from vendors. Especially the honesty from vendors might be more crucial in the future. The burned earth of over promising I come across every day in data setups is a massively sad thing.

Kai Waehner obviously points out the benefits of data in motion. And this is something I see now more and more often. Teams building up their infrastructure around an event-streaming like Kafka. Which I love since it enables us with easier data collection and potentially better data models.

Lewis Gavin shows that ML is coming to where we work as data engineers. You can use them within your sql queries, you can experiment without having a deep data science background.

Andreas Kretz sees real-time becoming an essential part in Data engineering which I expect too to see a lot more in 2022.

Dhruba Borthakur brings up my beloved topic of going beyond dashboards and work on data-driven apps. I would to see more and more efforts in this area. But I am not sure about his take on the move from open source to Saas. But we will see.

My take for 2022 in the post is that CDPs belong in the data warehouse and with operational analytics and real-time dbs like Rockset we have the essential pieces to make it happen.

Check out the link to the article in the Rockset-Blog to learn more: