If you’re responsible for data pipelines or any other aspect of your company’s data, you’re busy.
You might want to try out new data services, but you probably don’t want to get bogged down in lengthy vendor negotiations or complicated configurations for trials.
Still, though, you could use more insights about your data.. You don’t like getting surprises from third-party data sources. And you definitely want to make sure that code changes haven’t corrupted your data pipelines.
At Data Culpa, we can let you try out Data Culpa Validator and experience the insights it provides about your data in just three days. We’ll show you trends and the alerts that Validator would have raised about your changes in your data’s consistency, including alerts about unexpected changes in:
- Data freshness
- Data volumes
- Data schemas
- Data values
So in less than a work week, you can see analytics revealing trends and trouble spots: alerts, trends, variations—the whole data observability shebang.
See 60 Days of Data Quality Analysis with Data Culpa Instant Training™
We can do this because of a unique feature in Data Culpa Validator we call Instant Training™.
With Instant Training, we can ingest historical data and compute alerts for the past as we roll the clock forward. We support Instant Training in our API as well as through data collected through our BigQuery, MongoDB, Postgres, and Snowflake connectors.
Leveraging the historical data lets us provide customers with a quick Proof of Concept (PoC) showing what changes in data consistency Validator would have detected over a historical period without having to wait weeks or months for results.
What We Do In Just Three Days
From this, we implement what we call “the three-day proof of concept”:
- Day One
Set up your trial account access to your data in Validator, which runs in our secure cloud environment.
- Day Two
Run the ingest. By default, we limit the ingest to 5,000 records a day during the trial; we suggest picking a smaller data set to try or connect Data Culpa Validator to a view that represents a sampling of your data. When configuring the historical data, you’ll configure which column you want Data Culpa to use to infer the proper date bucketing of the data.
- Day Three
Review the results and decide if you’d like to run Data Culpa in a bigger way.
Our pricing is elastic. You can pay month by month and grow up or down as needed. We primarily charge by “daily active rows” — the number of rows presented to Data Culpa Validator per day, up to 200 fields per row. (Additional fields are charged as fractional rows. For sparse data, we only charge for fields presented.)
Our Medium and Enterprise plans include dynamic elastic compute clusters that can spin up thousands of compute nodes concurrently to ingest large data sets rapidly.
With our three-day PoC, setting up a demo of your data and Data Culpa Validator is easy and straightforward—even for the busiest data teams.
Contact us today to arrange a three-day PoC analyzing your team’s data.