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Agile Data Engine’s metadata approach to data modeling and workload orchestration means that everyone in the DataOps teams understands how the solution does what it does. So even if the team members change over time, we can be sure that any change to the platform will not disrupt the daily work of our data consumers.
Head of Data Platforms, Kesko
We can develop at an entirely different rate. In practice, the improvement is in multiple percentages. We previously saw development cycle turnarounds in weeks – now they happen in days.
Head of Data and Analytics, DNA (part of Telenor Group)
Agile Data Engine guaranteed a very fast start to the project: in a couple of weeks, the solution was already creating value to the business.
Head of Business Intelligence & Analytics, Matkahuolto
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DataOps and data warehousing in general are ever-evolving. Head over to our blog to stay up-to-date on how you can make yours more resilient and future-proof as possible!
Data quality issues are present in all data warehouses. But how can you tell if the quality of your data is good? The answer: set data quality dimensions.
Learn to talk fluent data with this data glossary, covering data terminology, and specific terms related to analytics, DataOps, data development, and more.
Read on for perspectives on how Agile Data Engine helps business and IT departments improve their DataOps and take action to fix bottlenecks and issues.