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Cloud Computing: Triumph of Analytical Databases

Impressive benchmarks from analytic databases

Analytical Databases: In my last article about Cloud Analytics, we analyzed the various offering on Cloud Analytics. We also noted that some of the Cloud Analytical implementations are based on pure Relational Database extensions and others are built on an unstructured non-relational model or semi-relational model.

While the non-relational databases have existed for a while, their presence in the industry standard benchmarks are limited, and as a result they are not a candidate for enterprise class analytical solutions. However, with the support of various vendors their presence in industry standard benchmark situations are increasing.

Differences of Analytical Databases with Traditional RDBMS Databases
Column Based-Architecture:
Traditional RDBMS stores their data with respect to rows, whereas column-oriented analytical databases stores their content by column. The traditional RDBMS implementation - including Oracle - is to store all data for a given row in the same block. This might be might be optimal for OLTP operations - create an entry, query an entry, change an entry - but is not optimal for many data warehousing and BI query scenarios.

Shared Nothing Architecture: We have been seeing in general and in particular to Cloud, the best scalability and multi-tenancy is achieved when the databases are architected using ‘Shared Nothing' pattern, while some Relational database have implemented ‘Shared Nothing' as a variant (like DB2 Data Partition), most like Oracle RAC have implemented the Shared Everything architecture. New analytical databases all have implemented the Shared Nothing Architecture for unlimited scalability.

In-Memory Processing: Typical analytical databases use in-memory technology that enables maximum data throughput for high performance in accessing data. During read access, the data is processed completely in RAM, eliminating slow access to hard disks. Traditional  Relational Databases while uses the buffer memory still heavily depends on the DISK I/O.

Some of the popular Analytical Databases with the above mentioned properties are :

EXASOL EXASolution is ahigh-performance relational database for time-critical complex analyses, planning and reporting.

The Vertica Analytic Database is the first RDBMS designed specifically for on-demand, infinitely scalable Business Intelligence in virtualized and cloud environments. Optimized for MPP grids.

ParAccel Analytic Database (PADB), the world's fastest, best price/performance platform for empowering data-driven businesses. ParAccel enables organizations to tackle the most complex analytic challenges and glean ultra-fast deep insights from vast volumes of data.

Industry Benchmarks Favor Analytical Databases Over Traditional RDBMS
Cloud platform along with its side benefits like Grid computing have opened the way for unlimited scalability for Analytical applications which were at constraint in traditional data centers. With the massively parallel processing and scalability at stake, the new breed of analytical databases tend to take the advantage in the Cloud Analytics market.

The TPC BenchmarkH (TPC-H) is a decision support benchmark. It consists of a suite of business oriented ad-hoc queries and concurrent data modifications. The queries and the data populating the database have been chosen to have broad industry-wide relevance. This benchmark illustrates decision support systems that examine large volumes of data, execute queries with a high degree of complexity, and give answers to critical business questions.

A recent analysis of the TPC-H benchmark show a clear lead for the Analytical Database EXASOL EXASolution over the traditional RDBMS. The results are shown below and can be seen from the TPC site too.

Database Size Category

Leader In Performance

Nearest Traditional Relational Database

Performance Improvement

100 GB

EXASOL  EXASolution  4.0

MS SQL 2008 R2

15X

300 GB

EXASOL  EXASolution  4.0

MS SQL 2008 R2

18X

1000 GB

EXASOL  EXASolution  4.0

Oracle 11g R2

3.5 X

3000 GB

EXASOL  EXASolution  4.0

Oracle 11g R2

14X

10000 GB

EXASOL  EXASolution  4.0

IBM DB2 9.5

21X

 

The results indeed  show a great  success  for Analytical databases in  DSS and OLAP systems over traditional databases.  We also find the   next top spots are taken up by other analytical databases like ParAccel much higher than other traditional relational databases.

Summary
Benchmarks are there to be broken in future by other vendors, the point is Cloud Platform is  not just a enabler for low cost provisioning of hardware but the High Performance Computing and Grid Computing opens up several possibilities of Advanced Analytics and the associated massive scalability will help the enterprises. Also  we are seeing new possibilities in terms of supporting  platforms. Analytical databases like EXASOL, Vertica, ParAccel  have provided new avenues for enterprises to move Analytics to Cloud.

More Stories By Srinivasan Sundara Rajan

Highly passionate about utilizing Digital Technologies to enable next generation enterprise. Believes in enterprise transformation through the Natives (Cloud Native & Mobile Native).

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