Robust, Scalable Architecture, built for Big Data Management

Challenges of the 20-year old Data Pipeline

The traditional data pipeline was not built for significant data movement so it relies on data summarizing and sampling, providing only a very narrow view into trends and behaviors.

Today, enterprises want to analyze all of their data, quickly and reliably, and have incremental scalability, without spending too much for specialized hardware, power, cooling and other monthly recurring costs.

Today’s Big Data problems require state-of-the-art Big Data solutions that can combine Data Warehousing and Data Processing, all on one reliable platform with an underlying architecture built to scale up and down cost-effectively, while supporting mission-critical business applications.

The Next-Generation Approach to Data Warehouse Architecture

Aster Data nCluster, the first Massively Parallel Data-Application Server is available in three product lines, with the award-winning Aster Data nCluster DBMS at the heart of each architecture:

The high-performance Aster Data nCluster database has combined three distinct disciplines in a single massively-parallel processing (MPP) database architecture:

  •  Data Warehousing
  •  Analytic Applications
  •  SQL-MapReduce

With Aster Data nCluster, enterprises can rely on an MPP database architecture where apps are co-located ‘inside’ the database engine. The apps do not have to be re-written to move them to the data. Once moved inside the DBMS, the apps 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.

Top Picks
Data Sheet: Aster Data nCluster 4.0 Data-Application Server
Whitepaper: New MapReduce Whitepaper
Webcast: Bringing Big Data Analytics to the Enterprise - 11/12, with Merv Adrian
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.

Philip Russom, Senior Manager of TDWI Research