Webinar Summary: Agile, DataOps, and Data Team Excellence

Webinar Summary: Agile, DataOps, and Data Team Excellence

Gil Benghiat, co-founder of Data Kitchen, began by explaining the overarching goal of achieving data team excellence, which involves delivering business value quickly and with high quality. He detailed data teams’ everyday challenges, such as balancing speed and quality, and the impact of Agile methodologies borrowed from software development practices.

ON DEMAND WEBINAR: Beyond Data Observability

ON DEMAND WEBINAR: Beyond Data Observability

Do you have data quality issues, a complex technical environment, and a lack of visibility into production systems?

These challenges lead to poor quality analytics and frustrated end users. Getting your data reliable is a start, but many other problems arise even if your data could be better. And your customers don’t care where the problem is in your toolchain. They want to know when to get their trusted dashboard refreshed (for example).

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. It’s more than just a ‘last mile’ problem in data observability. It’s about personalization for your customers. Demanding Data Consumers require a personalized level of Observability.

Navigating the Chaos of Unruly Data: Solutions for Data Teams

Navigating the Chaos of Unruly Data: Solutions for Data Teams

Data teams have out-of-control databases/data lakes, with many users and tools constantly changing data, many users and tools out of their control, and an unknown/uncontrolled ETL/ELT process with no data quality tests. As a result, they are left with the blame for bad data and have limited ways to affect the actions of others who are changing the data. They need help to quickly identify anomalies and problems in the data before someone finds it.

ON DEMAND WEBINAR: Data Observability Demo Day

ON DEMAND WEBINAR: Data Observability Demo Day

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.

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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.