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Trust what you deliver

Our low latency data observability solution alerts about data issues,
prevents their propagation, and highlights which applications are impacted.

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Why Data Observability?

Data Observability automates data monitoring along the pipelines to facilitate data issues troubleshooting and prevention, so data teams can improve their time to value and scale their productivity.

Data Observability also increases communication across teams by providing evidence-based information on data management ecosystems, while improving trust when it comes to put applications, models, and reports in production.

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Increase
scalability

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Speed
time to value

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Improve
collaboration

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Build
confidence

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How Data Observability should be done?

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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.

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Accountability
enabler

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Easier
maintenance

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Document
data usage

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Higher
reliability

What is the platform behind DODD?

Our data and analytics observability platform tracks and measures in real-time your data usage performance across systems, projects, and applications.

Simultaneously, it can enrich your data management ecosystem by sharing lineages, schemas and quality information with data catalogs, glossaries and incident management systems.

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Data usage
analysis

 
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Issues
troubleshooting

 
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"Datastrophe"
prevention

 
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Data Observability is for everyone

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For Data Scientists

Remain confident about the models in production by being notified as soon as performance is deviating.

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For Heads of Data

Increase the productivity of your team by reducing the resources required to maintain existing data applications.

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For Data Engineers

Save time and trouble by easily increasing your visibility and control over data in production.

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For Analysts

Increase trust in existing reports by being  immediately alerted as soon as data quality is out of range.

Discover our latest posts

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Understanding SLOs Role in Data Quality Management

Why SLAs Are Critical to Ensuring Data Reliability.

Why SLAs Are Critical to Ensuring Data Reliability.

Why Data Engineers, Scientists, and Analysts Need Data Observability.

Why Data Engineers, Scientists, and Analysts Need Data Observability.

We've got your back

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Cloud

You can benefit from all our features with a highly secured cloud solution.

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On-premise

We provide an on-premise solution for corporations with specific needs.

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Security

Because security matters to us and to our clients, we are SOC 2 compliant.

Trust what you deliver.

Do you have any questions regarding
data quality management or data observability?