Christopher Bergh detailed the company’s release of new open-source tools to enhance DataOps practices by addressing common inefficiencies and errors within data teams. During the webinar, he demonstrated how these tools provide robust data observability and automated testing to improve productivity and reliability across data operations.
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.
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.
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.
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.
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.
Something is missing from our data systems. We cannot judge the expectations vs. reality in our production data systems. What is the variance between what is happening now and what should be happening? Is it on time? Late? Is it trustworthy? What is happening now? Will my customers find a problem?
That missing piece that connects data system expectations and reality is a ‘Data Journey.’ It is the missing piece of our data systems.
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 complicated and, in the long run, it kills productivity.
All the cool kids are talking about Data Products and Data Mesh. The data companies have gotten ahold of terms and started to say their twenty-year-old ETL tools are the perfect tools to do that fashionable product-meshy stuff. What is going on?
In the webinar “Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms,” James Royster, Vice President of Commercial Operations, Insights, and Analytics at Karuna Therapeutics, shared his insights on leading efficient and effective data analytics teams.
The Race For Data Quality in a Medallion Architecture
DataOps Observability: Monitor every Data Journey in an enterprise, from source to customer value, and find errors fast! [Open Source, Enterprise]
DataOps Data Quality TestGen: Simple, Fast Data Quality Test Generation and Execution. Trust, but verify your data! [Open Source, Enterprise]
DataOps Automation: Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change. [Enterprise]
DataOps Consulting, Coaching, and Transformation
Commercial Pharma Agile Data Warehouse
Data Production Teams
Data Science/AI
Data Engineering
Data Quality
Business Analytics
Data Products
Data Mesh
Data Contracts
ModelOps / MLOps
Data Observability
DataGovOps
Self-Service Operations
Hybrid Cloud Architectures
Cloud Architectures
Kubernetes Architectures
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.