Webinar Summary: Introducing Open Source Data Observability

Webinar Summary: Introducing Open Source Data Observability

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 We Open-Sourced Our Data Observability Products

Why We Open-Sourced Our Data Observability Products

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.

Webinar Summary: Agile, DataOps, and Data Team Excellence

Webinar Summary: Agile, DataOps, and Data Team Excellence

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.

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Open Source Data Observability Software

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]

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DataOps Automation: Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change. [Enterprise]

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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.