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Big Data as a Managed Service - Cazena

Solutions Gallery > Big Data as a Managed Service - Cazena

Solution overview

Cazena makes it easy to get started with data processing in the cloud in just a few clicks, automating an otherwise long and difficult process. The service bundles high-performance compute, storage, data movers and a variety of tools in one analytic platform. The service supports most enterprise data workloads, from SQL jobs to R, Python and complex data science. Behind the scenes, Cazena's workload intelligence automatically provisions and continuously optimizes the ideal combination of data technologies for each workload. It's simple: Once setup, use the Cazena web interface (or APIs) to securely load data and run analytics -- or connect with any business intelligence or visualization tools. It's all delivered "as a service," freeing teams to focus on analytics instead of managing technology.

Data Lake-as-a-Service

Cazena’s Data Lake-as-a-Service is the easiest way to stage and query raw data from business applications, log files or other sources, as well as a cost-efficient way to store historical data. Data lakes have been gaining prominence recently as the best method to collect and store large volumes, or a wide variety, of data in one repository. They make it simple and efficient to gather disparate data in one place, without requiring restructuring or cleansing before loading. Data lakes are optimized for storage. They are ideal for workloads such as data aggregation from multiple sources, data preparation activities, or data segmentation and subsetting. Based on the specific SLA, Cazena will recommend the appropriate configuration, frequently a Hadoop distribution with optimized cloud infrastructure designed for massive storage.

Data Mart-as-a-Service

Cazena’s Data Mart-as-a-Service can augment existing data warehouses by offloading users or workloads to the cloud at one-fifth the cost of traditional systems. Data marts are production-ready for business analytics supporting a certain department or business process. They have similar highperformance capabilities as data warehouses, but are often used for domain-specific data that has particular access and analytic requirements. Data marts are often (but not always) used to analyze structured data from business applications, and as a production system, frequently have more intensive governance and SLA requirements. Data marts are optimized for compute performance, so they can run alongside a data warehouse and support a variety of analytics and analytic tools. As always, Cazena recommends the appropriate combination of technology and infrastructure to support the specific SLA.


Cazena’s Sandbox-as-a-Service supports data exploration, testing and development environments where enterprises can explore new ideas and hypotheses quickly and inexpensively. It’s a place to test new datasets and relationships in a cost-effective, powerful analytic environment. This has the advantage of ensuring that experimental data exploration doesn’t impact the production data warehouse SLAs. A Cazena Sandbox allows for cloud-scale analytic innovation – yet can still be managed by IT. All kinds of data might be used in a sandbox and companies may have multiple sandboxes for different teams. Sandbox requirements greatly vary, as they may support vastly different types of analytic activities. Based on the needs of a particular organization, Cazena Sandboxes may be optimized for massive storage, high-performance SQL analytics or a right-sized combination based on SLA.

Key highlights

Modernize architecture

About Cazena
Cazena makes it easy to get started with data processing in the cloud in just a few clicks, automating an otherwise long and difficult process. The service bundles high-performance compute, storage, data movers and a variety of tools in one analytic platform. 


Cazena Big Data as a Service Use Case

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