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By François Pietquin
With 38% of data teams spending between 20% and 40% of their time fixing data pipelines¹, delivering reliable data to end users can be an expensive activity for data teams. With Kensu’s latest integration with Azure Data Factory, ADF users now benefit from the ability to observe data within their Azure Data Factory pipelines and receive valuable insights into data lineage, schema changes, and data quality metrics.
Kensu extends Data Observability support for Microsoft users with its Azure Data Factory integration
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By Kensu
Kensu announces an integration with Azure Data Factory, the serverless data integration service. With this integration, teams can observe data within their Azure Data Factory pipelines and receive valuable insights into data lineage, schema changes, and performance metrics. As one of the few Data Observability providers available to support customers on-premise, multi-cloud, or hybrid environments, Kensu is broadening access to Data Observability for Microsoft users.
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By François Pietquin
38% of data teams spend between 20% and 40% of their time fixing data pipelines¹. Combating these data failures is a costly and stressful activity for those looking to deliver reliable data to end users. Organizations using Azure Data Factory can now benefit from the integration with Kensu to expedite this process. Their data teams can now observe data within their Azure Data Factory pipelines and receive valuable insights into data lineage, schema changes, and performance metrics.
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By Kensu
Kensu announces the publication of the research "The State of Data Observability" conducted by CDO Magazine with Kensu. The results of the study reveal that 84% of data management leaders place improving data quality and reliability as their highest current priority, with 92% agreeing that data observability would be a core part of their data strategy in the next 1-3 years.
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By Kensu
Kensu announces that it has been named a Challenger and Fast Mover and is projected to be named a Leader within the coming 12 to 18 months in the GigaOm Radar Report for Data Observability. In the report, Kensu falls within the Innovation/Platform Play quadrant, having capabilities to stop data incidents from propagating and resolve them twice as fast.
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By François Pietquin
Kensu is the first solution to bring advanced data observability capabilities to support Matillion, empowering organizations to gain richer insights into their data pipelines and ultimately strengthening trust and data productivity. Matillion ETL is a popular tool for building and orchestrating data integration workflows. It simplifies extracting data from various sources, transforming it according to business requirements, and loading it into a cloud data platform.
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By Jen McGregor
At the beginning of my career as a data analyst, I had to rely on other team members when something went wrong in our data pipeline, often only finding out about it after the event. That experience was one of the driving factors for me to join Kensu. When I spoke with the team for the first time, I had that “lightbulb moment”: data observability is a way of providing help to various data team members, including data analysts, in making their lives more productive and less painful.
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By François Pietquin
It’s a common thread amongst data-driven organizations: data teams face soaring volumes of data with varying complexities, which raise issues regarding data reliability. Efficiently monitoring data pipelines has become paramount to swiftly identifying and addressing potential data incidents, ensuring minimal impact on data practitioners and end users.
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By Kensu
Kensu announces a strategic partnership with Matillion, the leader in data productivity. Kensu is the first solution to bring advanced data observability capabilities to support Matillion Data Productivity Cloud, empowering organizations to gain richer insights into their data pipelines, ultimately strengthening trust in their data.
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By Kensu
Kensu announces support for Databricks Unity Catalog, the unified governance solution for data, analytics, and AI. Kensu's data observability capacities enable seamless tracking of all internal and external data sources' metadata and generating metrics to automate their monitoring, drastically improving visibility and time to resolution.
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By Kensu
Quickly detect, troubleshoot, and prevent the propagation of a wide range of data incidents through Data Observability, a set of best practices that allow data teams to gain greater visibility of data and its usage. If you're a data engineer, ML engineer, or data architect, or if the quality of your work depends on the quality of your data, this book shows how to focus on the practical aspects of introducing Data Observability in your day-to-day work.
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Our low latency data observability solution alerts about data issues, prevents their propagation, and highlights which applications are impacted.
To foster a data-driven culture, automation of data observability at scale is essential. The best way to achieve this is through what is called Data Observability Driven Development [DODD] which implies observable information is produced by the applications.
The method is a paradigm shift that allows data teams and data usage to scale efficiently. DODD is done from within the applications to enable data projects with synchronized observability, continuous validation, and contextual observability.
Data Observability is for everyone:
- For Data Scientists: Remain confident about the models in production by being notified as soon as performance is deviating.
- For Data Engineers: Save time and trouble by easily increasing your visibility and control over data in production.
- For Heads of Data: Increase the productivity of your team by reducing the resources required to maintain existing data applications.
- For Analysts: Increase trust in existing reports by being immediately alerted as soon as data quality is out of range.
Trust what you deliver.