Blog
Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability
The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. This is where the true power of complete data observability comes into play, and it’s time to get acquainted with its two critical parts: ‘Data in Place’ and ‘Data in Use.’
ON DEMAND WEBINAR: Automated Test Generation – Why Data Teams Need It
This webinar discusses how to make embarrassing data errors a thing of the past.
We will start with how data engineers do not understand their data and have difficulty identifying problematic data records. We will also discuss how the vast majority of data engineers are so busy that they don’t know, or have time to write, tests to write to find data errors. We will finish with a demonstration of DataKitchen’s New DataOps Testgen Product.
That missing piece that connects data system expectations and reality is a ‘Data Journey.’ It is the missing piece of our data systems.
Announcing the DataOps Cookbook, Third Edition
Since the first edition of the DataOps Cookbook in 2019, we have talked with thousands of companies about their struggles to deliver data-driven insight to their customers. In many ways, they all have the same problems. They have built data and analytic systems...
A Summary Of Gartner’s Recent Innovation Insight Into Data Observability
On 20 July 2023, Gartner released the article “Innovation Insight: Data Observability Enables Proactive Data Quality” by Melody Chien. In the article, Melody Chien notes that Data Observability is a practice that extends beyond traditional monitoring and detection,...
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...
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...
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...
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...
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...
UPCOMING WEBINAR: Automated Test Generation – Why Data Teams Need It
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...
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...
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...
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...
