In this blog, we’ll discuss all things data quality and data observability. We’ll present interviews with leading data scientists, and we’ll discuss our own working building data observability solutions for data engineers and data scientists working in a broad range of industries.
When we founded Data Culpa, one of the first claims we made about the data engineering market was that data monitoring would tie into the testing of data. We believed then — and we still believe — that companies would treat data as a product in the same way they treat...
Recently I was talking to a customer who had used a competitor’s product for data monitoring. It sounded like the product had the users specify parameters about the data; e.g., “this column should never be null.” This is all well and good, except that the product then...
BOSTON, MA. September 13, 2022 – Data Culpa, Inc., a provider of high-scale, low-touch data observability solutions for data engineering projects of all kinds, today announced that the US Patent & Trademark Office has issued patent #11,429,614 to Data Culpa for...
There’s a common ingest pattern we see across customers. Data lands in some file/object storage, e.g., S3, as a JSON/Parquet/CSV/etc, and then is ingested into Snowflake, potentially into two tables: one for a raw load and one or more that transform the data in some...
Data quality is an ongoing challenge for data scientists and data engineers. In this interview, Michal Klos share the insights he's gleaned from a long career building data pipelines and ensuring they deliver high-quality results. Michal is Sr. Director of Engineering...
Here’s what we’re writing on Medium:
The Data Quality Gap (June 11, 2020)
4 Lessons for Data Scientists from the UK’s A-Levels Algorithm Debacle (August 18, 2020)
Feel free to reach out to us!
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