The Matillion data integration and transformation platform enables enterprises to perform advanced analytics and business intelligence using cross-cloud platform-as-a-service offerings such as Snowflake. The DataKitchen DataOps Platform provides a way to extend...
A DataOps implementation project consists of three steps. First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Second, you must establish a definition of “done.” In DataOps, the definition of...
Watch this live demo of the DataKitchen DataOps Platform.
This blog builds on earlier posts that defined Kitchens and showed how they map to technical environments. We’ve also discussed how toolchains are segmented to support multiple kitchens. DataOps automates the source code integration, release, and deployment workflows...
It’s never good when your boss calls at 5 pm on a Friday. “The weekly analytics didn’t build correctly. What happened? Call me every hour with updates until you figure it out!” For many data professionals, this situation is all too familiar. Analytics, in the modern...
Most projects naively assume that most of the time and resources will be spent in the “black box,” building the machine learning (ML) model, whereas a majority of the project time is actually needed in the green boxes – the ML system.
DataOps improves your ability to orchestrate your data pipelines, automate testing and monitoring, and speed new feature deployment. DataOps recognizes that, in any data project, many different tools play an important role as independent components of the data...
Tests identify data and code errors in the analytics pipelines. Automated orchestration of tests is especially important in heterogeneous technical environments with streaming data. The DataKitchen Platform makes it easy to write tests that check and filter data...
DataOps places great emphasis on productivity. Team velocity can improve significantly when the data organization uses tools and methods that promote component reuse. Architecting analytics as microservices is one way to stimulate code reuse. The challenge is that the...
The cacophony of tools and mission-critical deliverables are the reason behind the high complexity of modern-day data organizations. Data groups include a wide range of roles and functions that are intricately woven together by their “Data”. Teams include data...
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 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
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.