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Cazena Announces Big Data as-a-Service Solutions for Enterprise-ready Data Lakes and Data Marts in the Cloud

cazena_logoCazena announced the general availability of two of its Big Data as-a-Service solutions that simplify big data processing in the cloud, Data Lake as a Service and Data Mart as a Service. Cazena’s Big Data as a Service solutions address the complexity and security concerns that prevent most large enterprises from realizing the cloud¹s cost and agility advantages for handling big data analytics.

Cazena’s Data Mart as a Service is designed for compute-driven workloads, such as ad hoc business intelligence, analytics, and other SQL processing. Cazena’s Data Lake as a Service is optimized for staging, processing or archiving large volumes of data. Used together, Cazena¹s solutions provide a complete data pipeline for an enterprise. Raw data can be staged and processed in a Data Lake as a Service, then subsets extracted and moved it into one or more Data Marts as a Service for specific departments or users.

Cazena’s Big Data as a service securely moves and optimizes big data processing in the cloud in just three clicks using powerful technology, including:

  • Workload Intelligence: Provisions, optimizes and continually manages cloud infrastructure to ensure workload SLAs, by using multiple best-of-breed data technologies (Hadoop, MPP SQL, Spark, etc.)
  • End-to-end Automation: Moves data efficiently and runs analytic workloads in the cloud, with connectors for data sources and enterprise BI/analytics tools
  • Security and Privacy: Integrates an encrypted data cloud into the enterprise using strong security, governance and compliance controls

Cazena’s new solutions have undergone significant beta testing with North American and U.K.-based enterprises across several verticals. Common use cases include:

  • Collecting and staging external data from web, IoT, mobile, social or other cloud sources
  • Providing easy, secure cloud access to datasets for customers or partners
  • Quickly provisioning sandboxes or production-ready environments for analysts and data scientists
  • Augmenting data warehouses with cloud-based disaster recovery or by offloading storage or compute-intensive workloads

 

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