To monitor #dataquality effectively, you need to see how data is changing over time. That's usually more important than whether or not rigid unit tests are raising errors. Here's why: https://t.co/B5Iy2W7avk

Seems to be 2 main headwinds in software currently: 1) new bookings slowing due to macro 2) Optimizations (everything from lowering AWS / Azure / GCP, Snow, DataDog, etc bills, to cutting / consolidating vendors) 1 will last until macro turns around. But how about 2?

Optimizing cloud expenses helps free money for new investments in AI and other IT ventures. Another reason to adopt #FinOps. #AI #cloud #strategy https://t.co/CKFVsnUODM

Here are a few trends I am observing, from talking to a few people: - Optimize cloud spending bill: understand where things can be cut down, identify waste - Optimize logging provider spend. Basically: stop logging stuff that doesn't matter - Review pricing of eg pager systems

Most data observability products fall short. Here's why. #data #dataengineering #datamonitoring #observability https://t.co/cRaz9Yc9o9

Looking for help with #FinOps and cloud cost-cutting? Our new Streamliner service can help. #cloud #cloudops #dataengineering #datawarehouses #snowflakedb https://t.co/HxKRy6yddk

For data teams to deliver #dataproducts that meet the needs of their customers according to #datacontracts, they need to consider context. Here's why. #data #dataengineering #datascience #enterpriseIT https://t.co/6e4EKITao5

Data teams expect a lot of their data. In fact, it's possible to even identify a #dataquality hierarchy of needs. #dataengineering #datamanagement #datascience https://t.co/3yblHNlLnc

To make #datacontracts work, you need to agree on what's really important in a #datapipeline. Keeping track of #datacontext can help. #dataengineering #datascience https://t.co/6e4EKITIdD

Data mesh architectures are on the rise, but they create special challenges for #dataquality #monitoring. Is your data team ready to address them? #data #dataengineering #datamesh #datascience #observability https://t.co/K4svmd7uHN

The best #dataquality monitoring for busy data teams relies on relative baselines, not rigid unit tests. Here's why. #AI #BI #dataengineering #datascience #MLOPs #observability https://t.co/B5Iy2W7avk

Bad quality data is worse than no data: ML models will do wrong predictions. Dashboards will show wrong metrics. Still data quality, monitoring and observability are not treated as priorities in many companies. #DataScience

Explained in class today that delta means a small positive number you choose and epsilon means a small positive number your enemy chooses.

THEY'RE CALLED DATA CONTRACTS THEY'RE API-LIKE AGREEMENTS BETWEEN THE SOFTWARE ENGINEERS WHO OWN THE SERVICES AND THE DATA CONSUMERS THAT RELY ON THEM. IT'LL ALLOW THE SWES TO WORRY LESS ABOUT BREAKING PRODUCTION DATA PIPELINES AND HELP THE DATA TEAM MOVE AWAY FROM FIXING IN SQL

Want to monitor data without getting deluged with meaningless alerts? Try monitoring against a relative baseline. https://t.co/B5Iy2W6CFM #AI #data #dataengineering #datamonitoring #dataops #dataquality #datascience #MLOps

Just a tiny schema change…

Data Culpa receives a patent for monitoring data quality in data pipelines and databases. Here's our announcement. https://t.co/KQtLwW5Hay #AI #BI #data #databases #dataengineering #dataquality #datascience #patent #observability

Data Culpa receives our first issued patent for monitoring data quality in data pipelines and databases. Here's our announcement. https://t.co/WEun2bluGX #AI #BI #data #databases #dataengineering #dataquality #datascience #patent #observability

Data meshes create new challenges for #dataquality monitoring and #observability #dataengineering #datamesh #datapipeline #datawarehouse https://t.co/K4svmdpDVV

We love MongoDB's flexibility and ease of use. But that flexibility can create challenges with data schemas. Here's a look at the problem and how to fix it. https://t.co/db5f85lUpK #data #dataengineering #dataquality #datascience #mongodb