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 lots to love about MongoDB. Compared to traditional databases, MongoDB makes developing and standing up new applications fast, thanks to the implicit schema of the collections. Need to add a new field? No big deal, just start using it. Great! The hierarchical...
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...
Just as Maslow identified a hierarchy of needs for people, data teams have a hierarchy of needs, beginning with data freshness; including volumes, schemas, and values; and culminating with lineage. In this blog post, which was published in the Data Science area of the...
Consistent pipeline behavior is critical for any data process. You can use Data Culpa Validator to ensure consistent operation of pipelines as well as data at rest in databases, data lakes, and data warehouses. This introduction shows you how to validate your results...
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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