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Stream processing Data governance ML platform Analytical data platform Cloud-native data platform Big data DevOps

The amount, variety and complexity of data in analytical data platforms has grown exponentially over the past several years. The latest advancements in the automation of analytics with reporting, machine learning and artificial intelligence have led to fully automated data pipelines. However, with these advances, the challenge of ensuring that the data used for business intelligence comes from the correct sources and doesn't get corrupted in the process has grown. When data is improperly sourced or corrupted, subsequent business decisions will be faulty.

While other companies focus on organizational process and governance, we concentrate on a technical approach to data governance. In our experience, we have frequently seen organizational controls fail due to a lack of culture, insufficient attention, the demand of overly complex cross-departmental orchestration, an increase in manual efforts and plain human errors. Therefore, we take a practical approach to the problem, and use targeted automation and machine learning to ensure data correctness. 

Practical approach to data governance

Data catalog and glossary

Use case: Find data location by description.

Example: A data analyst needs to discover where a customer address is stored, or find what attributes the customer has.

Solution:

1. Provide a self-service portal to users.

2. Enforce a column and dataset naming convention.

3. Augment columns with searchable descriptions.

Data lineage

Use case: Trace data origins.

Example: A data analyst discovers a broken dataset and needs to find where the data originally came from.

Solution:

1. Provide a self-service portal to users.

2. Implement tooling that collects data modification logs.

3. Ensure that tooling is connected with all data pipeline implementation technologies.

Data quality

Use case: Detect data corruption and prevent bad data from propagation.

Example: A data source format changes unexpectedly, contaminating data in the system and spoiling executive reports.

Solution:

1. Implement statistics and machine learning to detect any data corruption.

2. Alert the support team in case there are issues.

3. Prevent the propagation of corrupted data in real-time.

Self-service data catalog

Self-service data catalog

Easily find any data in the platform and check its current quality status.
Dataset profile

Dataset profile

Provide deep insight for each dataset, such as schema, change log, metrics and more.
Lineage dashboard

Lineage dashboard

Show where the data came from, and what other datasets were generated from it.
Data glossary portal

Data glossary portal

Provide a knowledge base for datasets and a transparent nomenclature for data rules and policies.
Data quality enforcement

Data quality enforcement

Detect data corruption and prevent it from spreading.
Quick alert system

Quick alert system

If there is corruption, the support team is notified quickly.
Enterprise-wide scale

Enterprise-wide scale

Get outside of the data lake and thoroughly cover all source-of-record systems.
Machine learning

Machine learning

Implement anomaly detection and automate dataset metrics analytics with ML techniques.
How it works
Data governance and metadata management
Data governance solution for Hadoop
Big data solution for batch and stream processing
Web interface that supports Hadoop
Data governance and metadata management
Data governance solution for Hadoop
Big data solution for batch and stream processing
Web interface that supports Hadoop
Most of the open source technologies we use provide a solid foundation, but don't cover all requirements of modern data governance. We customize and enhance them for the use cases and environment our clients have.

Schedule a free workshop with one of our architects to learn more

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