Simplify Data & Analytics Architectures to Improve AI and Machine Learning Initiatives

The pace of change is only accelerating, which means the data and analytics requirements today will not be the requirements tomorrow. To be competitive, organizations require a modern data and analytics architecture, that can keep up and perform sophisticated artificial intelligence and machine learning (AI/ML) analyses on the data they collect in real-time.

Maintaining separate systems for each of these requirements makes it difficult to be agile and responsive in today’s fast-moving markets. New data and analytics architectures are available that can support these requirements with a common platform.

  • Automate Data Pipelines - stream real-time data, at scale, from virtually any source - databases, data warehouses, enterprise systems, and more – directly into the Databricks Unified Analytics Platform.
  • Generate Spark Transformations – enable automatic transformations without any manual coding, to deliver analytics-ready data in Databricks Delta Lake.
  • Build Secure Governed Catalogs – Create self-service catalogs for all data, not just in Databricks, but across the enterprise, to provide data consumers an easily accessible data marketplace to find, understand, and utilize data.

The Qlik and Databricks solution drives agility, reduces risk, and allows organizations to realize the potential of their Databricks Unified Data Analytics Platform investment more fully. For more insights on combining Data Warehouses and Data Lakes to improve analysis and insights download the Ventana Research eBook.

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