Just because you’re not hearing about data errors doesn’t mean they don’t exist. This silence could be a ticking time bomb for underlying issues yet to surface. Here are seven compelling reasons why you should care and be proactive, even when all seems well.
Large Language Models (LLMs) and Generative AI are all the rage right now but will only work for organizations that have a solid grasp on the quality of their data and the series of operations acting upon that data to augment the base LLM.
A list o the best Data (and Analytic) Observability & Data Journey – Ideas and Background Links
In data and analytics, one skill stands timeless and universal: the art of blaming someone else when things go sideways. In this humorous blog, learn from the best!
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 analytic team war rooms, often convened for emergency problem-solving, epitomize inefficiency and detract from proactive, value-driven tasks. By leveraging data observability and rigorous testing, issues can be detected and resolved early, negating the need for such reactive measures in the modern era of DataOps.
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.’
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.’
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
On 20 July 2023, Gartner released the article “Innovation Insight: Data Observability Enables Proactive Data Quality” by Melody Chien.
DataKitchen Summarizes and comments
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.
