Robust, Scalable Analytic Platform for
Big Data Analytics

Challenges of the Data Pipeline

The traditional data pipeline was not designed for significant data movement, so it relies on data summarizing and sampling, providing only a very narrow view into trends and behaviors. This pipeline limits the benefits of big data analytics.

Today, enterprises want to perform analytics quickly and reliably and have incremental scalability without spending too much for power, cooling and other recurring costs. They also appreciate the flexibility of commodity hardware.

Today’s big data analytics problems require a state-of-the-art analytic platform that can combine data storage, management, and processing in one reliable platform with an underlying architecture built to scale up and down cost-effectively while supporting mission-critical business applications.

The Next-Generation Analytic Platform Architecture

Aster Data nCluster is the leading analytic platform, a massively parallel software solution that embeds MapReduce analytic processing with data stores for big data analytics that incorporate new multi-structured data sources and types. nCluster’s analytics engine delivers a unified SQL-MapReduce® computation layer on top of an architecture distinguished by a hybrid row and column data store for the highest levels of performance. The award-winning Aster Data nCluster analytic platform is available in editions for three different deployment scenarios:

Aster Data nCluster combines three key components in a single massively-parallel processing (MPP) architecture:

  • Massively parallel data storage 
  • Embedded analytic processing
  • Rapid analytics development

With Aster Data nCluster, enterprises can rely on an MPP architecture where applications are embedded in the database engine for in-database analytics. The applications do not have to be re-written to move them to the data. Once moved inside the analytic platfrom, applications can read, write, and process terabytes to petabytes of data at line speed.

Aster Data nCluster has a multi-tiered architecture for independent scaling

Aster Data nCluster is built on a unique, multi-tiered architecture that consists of four separate classes of nodes: Queens, Workers, Loaders, and Backup nodes. The four-tier design encapsulates a clean separation of roles for analytic processing. Each tier can be independently and incrementally scaled in response to the workload characteristics: adding more capacity (Workers), loading bandwidth (Loaders), concurrency (Queens), and backup (Backup) nodes on an as-needed basis.

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Recent attempts to bring analytic logic into databases as user defined functions or stored procedures are a step in the right direction, but inherently limited because most databases aren't optimized for application logic.
Aster Data has tackled this issue by embedding the equivalent of an application server in the database, such that application logic is fully parallelized for maximum speed and scalability with advanced data analytics.

TDWI
Philip Russom, Senior Manager of TDWI Research