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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

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

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

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...

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

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...

Data Journey First DataOps

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

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?

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: Data Mesh and Data Products

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

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...

Monitor every Data Journey in an enterprise, from source to customer value, in development and production.

Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.

Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.

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