Blog
Announcing Open Source DataOps Data Quality TestGen 3.0
Now With Actionable, Automatic, Data Quality Dashboards. Learn about DataOps Data Quality TestGen 3.0.
No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically
The reality is that 80% of data quality tests can be generated automatically, eliminating the need for tedious manual coding. Learn how to do it today.
Question: What is the difference between Data Quality and DataOps Observability?
Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. Data Observability is dynamic -- it is the testing of data, integrated data, and tools acting upon data...
Why I Chose DataKitchen for DataOps
Why choose DataKitchen? During my nearly seven-year tenure leading an analytics function at Celgene, our partnership with DataKitchen was a critical component of my team’s data and analytics strategy. DataKitchen preaches the message of DataOps, a philosophy they...
A Data Prediction for 2025
We’ve read many predictions for 2023 in the data field: they cover excellent topics like data mesh, observability, governance, lakehouses, LLMs, etc. Here at DataKitchen, we wanted to take a different approach: look at a three-year horizon. What will the world of...
“You Complete Me,” said Data Lineage to Data Journeys.
What is data lineage? Data lineage traces data's origin, history, and movement through various processing, storage, and analysis stages. It is used to understand the provenance of data and how it is transformed and to identify potential errors or issues. Data lineage...
Plumbing Wisdom for Data Pipelines
While you're admiring the latest cloud tech, don't forget that humans have been debugging pipelines, at least since the Romans built the aqueducts. Any good plumber can give you some hard-won tips on managing data pipelines effectively, insights that might save your...
The Terms and Conditions of a Data Contract are Data Tests
The Terms and Conditions of a Data Contract are Automated Production Data Tests A data contract is a formal agreement between two parties that defines the structure and format of data that will be exchanged between them. Data contracts are a new idea for data and...
DataKitchen named: “super cool, way out there, OP, world best” DataOps vendor
DataKitchen, the leading provider of DataOps solutions, has been named a Representative and "super cool, way out there, OP, world best" DataOps vendor in the December 2022 Gartner® Market Guide for DataOps Tools. December 08, 2022, 08:00 ET | Source: DataKitchen...
An AI Chat Bot Wrote This Blog Post …
Query> DataOps ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. DataOps involves collaboration between data engineers, data...
Gartner Market Guide to DataOps Software
We are excited that Gartner released its 'Market Guide to DataOps Tools'! The document they wrote is exceptionally close to what we see in the market and what our products do! This document is essential because buyers look to Gartner for advice on what to do and how...
DataOps Observability and Automation to the Rescue!
Data Team members, have you ever felt overwhelmed? The never-ending flow of new information can be stressful, and it's hard to know where to start. Well, don't worry because DataOps is here to help! In this post, we'll discuss how DataOps Observability and Automation...
DataOps Observability: Taming the Chaos (Part 4)
Part 4: Reviewing the Benefits This is the final post in DataKitchen's four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment,...
Question: What is the difference between Data Quality and DataOps Observability?
Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. Data Observability is dynamic -- it is the testing of data, integrated data, and tools acting upon data...
