Leverage existing data and BI assets.
Power value added analytics services with Spark. Information Lake can be implemented as a stand alone data lake or an integration and analytics layer over legacy architecture.
Information Lake provides a layer of governance, harmonized terminology and self-service analytics and data science, at scale, across all data sources. Features include:
In most organizations, the data team is struggling to meet their business customer demands for flexible, friendly and timely access to data. Existing approaches can provide this for narrow slices of data but most lack flexibility as needs change and do not scale to enterprise data.
Users are not just demanding access to data quickly, they want access to data from across the enterprise. They want to choose the data they need, when they need it, and, they want the flexibility to ask questions that they did not necessarily anticipate in advance.
Much corporate value is tied up in unstructured content: reports, notes and documents. Users want to access and analyze this content as if it were structured data and they want access to seamless, integrated analytic capabilities across all data sources, regardless of type.
Corporations can choose to deploy the data lake on-premise, in the cloud or both depending on the data governance and policy requirements.
Most enterprises have invested in data management, governance and security processes and they want to implement these capabilities on the data lake by leveraging their existing investments.