The Five Use Cases in Data Observability: Accuracy in Data Migration (#5) Data migration projects, such as moving from on-premises infrastructure to the cloud, are critical and complex projects that involve transferring data across different systems while...
The Five Use Cases in Data Observability: Fast, Safe Development & Deployment (#4) The integrity and functionality of new code, tools, and configurations during the development and deployment stages are crucial. This blog post delves into the third critical...
The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. This blog...
The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) Ensuring the accuracy and timeliness of data ingestion is a cornerstone for maintaining the integrity of data systems. Data ingestion monitoring, a critical aspect of Data...
The Five Use Cases in Data Observability: Data Quality in New Data Sources (#1) Ensuring their quality and integrity before incorporating new data sources into production is paramount. Data evaluation serves as a safeguard, ensuring that only cleansed and...
Data observability extends beyond simple anomaly checking, offering deep insights into data health, dependencies, and the performance of data-intensive applications. This blog post introduces five critical use cases for data observability, each pivotal in maintaining the integrity and usability of data throughout its journey in any enterprise.
Dive into DataOps and Data Observabiity with DataKitchen’s expansive free training and certification offerings tailored for individual Data Analytics, Science, and Engineering contributors. From grasping the foundational principles through the free DataOps Cookbook, over 30,000 readers strong, to hands-on certification courses in DataOps, Data Observability, and Automation, each pathway illuminates critical skills and insights. Moreover, senior managers can elevate their teams with advanced DataOps Change Management strategies, making every step from theory to certification educational and transformational.
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.
Why open source DataOps Observability and DataOps TestGen? Our decision to share full-featured versions of these products stems from DataKitchen’s long-standing commitment to enhancing productivity for data teams and promoting the use of automated, observed, and trusted tools. It aligns with our company’s philosophy of sharing knowledge and now software to inspire teams to implement DataOps effectively.
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.
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.