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.
The Need For Personalized Data Journeys for Your Data Consumers
Demanding Data Consumers require a personalized level of Data Observability. As opposed to receiving one-size-fits-all status updates, these key stakeholders desire real-time, granular insights into the status of their specific data as it traverses the complicated data production pipeline. Learn why this is essential to your success.
Data Teams and Their Types of Data Journeys
The article illuminates how Data Journeys can enhance data governance, improve operational efficiency, and ultimately lead to organizational success by thoroughly examining different Data Journey types—’ Watcher,’ ‘Traveler,’ ‘Hub & Spoke,’ and ‘Payload.’
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
The new idea showcased in the third edition of the DataOps Cookbook is to focus first on understanding and observing the journey that data takes through your production environment – from ingestion to processing to delivering actionable insights. The DataOps Cookbook-‘Data Journey First DataOps’ Third Edition
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.
DataKitchen Summarizes and comments
The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure
The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. Is it overkill? Paradox of choice? Or the right tool for the right job? We discuss.
The Syntax, Semantics, and Pragmatics Gap in Data Quality Validation Testing
What is the full range of data quality validation tests for data at rest and data in use? Linguistics provides an organizing principle: syntax, semantics, and pragmatics
Introducing The Five Pillars Of Data Journeys
The “Five Pillars of Data Journeys” outline a comprehensive approach to tracking and monitoring data across its lifecycle.