Why the Data Journey Manifesto?
So why another manifesto in the world? Really? Why should I care?
Why the Data Journey Manifesto?
So why another manifesto in the world? Really? Why should I care?
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?
James guides us through years of experience working in data, giving insight to many customers and leading highly efficient and effective teams. Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms
Why do 78% of data engineers wish their job came with a therapist to help manage work-related stress? THEY DO NOT TEST.
Chris Bergh shares how to do a Data Journey in the on-demand webinar!
Can you draw a map of all the paths data takes from source systems to production insight delivery? How many tools, technologies, configurations, and paths do your data take during its production process? What is the ‘run-time lineage’ of data in your organization?
DataOps Data Quality TestGen:
Simple, Fast, Generative Data Quality Testing, Execution, and Scoring.
[Open Source, Enterprise]
DataOps Observability:
Monitor every data pipeline, from source to customer value, & find problems fast
[Open Source, Enterprise]
DataOps Automation:
Orchestrate and automate your data toolchain with few errors and a high rate of change.
[Enterprise]
DataOps Consulting, Coaching, and Transformation
Commercial Data & Analytics Platform for Pharma
Data Production Teams
Data Science/AI
Data Engineering
Data Quality
Business Analytics
Data Products
Data Mesh
Data Contracts
ModelOps / MLOps
DataGovOps
Self-Service Operations
Data Quality Assessments
Data Quality Testing
Data Observability
Data Orchestration
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.