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.
The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure
The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and...
Introducing The Five Pillars Of Data Journeys
Introducing The Five Pillars Of Data Journeys “There are those who discover they can leave behind destructive reactions and become patient as the earth, unmoved by fires of anger or fear, unshaken as a pillar, unperturbed as a clear and quiet pool.” – Gautama Buddha...
Webinar Summary: Data Mesh and Data Products
Webinar Summary: DataOps and Data Mesh Chris Bergh, CEO of DataKitchen, delivered a webinar on two themes - Data Products and Data Mesh. Bergh started by discussing the complexity within data and analytics teams, stating that complexity makes everything more...
Only One Problem To Solve for Successful Data and Analytics
The real problem in data analytics is that teams need to deliver insight to their customers without error put new ideas into production rapidly minimize their ‘insight manufacturing’ expenses ... all at the same time As former leaders of data teams, we...
The Syntax, Semantics, and Pragmatics Gap in Data Quality Validation Testing
The Syntax, Semantics, and Pragmatics Gap in Data Quality Validate Testing Data Teams often have too many things on their ‘to-do’ list. Customers are asking for new data, people need questions answered, and the tech stack is barely running – data engineers don’t...
Why the Data Journey Manifesto?
Why the Data Journey Manifesto? So why another manifesto in the world? Really? Why should I care? About seven years ago, we wrote the DataOps Manifesto. We wrote the first version because, after talking with hundreds of people at the 2016 Strata Hadoop World...
Webinar Summary: Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms
Webinar Summary: Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms In the webinar "Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms," James Royster, Vice President of Commercial Operations, Insights, and...
A summary of Gartner’s recent DataOps-driven data engineering best practices article
On 24 January 2023, Gartner released the article “5 Ways to Enhance Your Data Engineering Practices.” By Robert Thanaraj, Ehtisham Zaidi, and 2 more. Gartner suggests in the article that successful Data Engineering teams have two crucial challenges. How to...
Data Journey First DataOps
Data Journey First DataOps Putting Problems in Your Data Estate at the Forefront Welcome to the high-octane world of DataOps, a powerhouse that turbocharges data analytics development and management. This innovative approach merges the agility of Agile...
UPCOMING WEBINAR: Automated Test Generation – Why Data Teams Need It
Two Downs Make Two Ups: The Only Success Metrics That Matter For Your Data & Analytics Team
Introduction. How to measure your data analytics team? So it’s Monday, and you lead a data analytics team of perhaps 30 people. You’ve got a new boss. And she is numbers driven – great! But wait, she asks you for your team metrics. Like most leaders of data...
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...