4 Bigdata Patterns and Databases Everyone Should Know

Bigdata has no specific format. It is basically an unstructured data. To make available your big data for your data analytics, you need to store data in certain databases. Else, it is not possible to carry out proper analytics on data.

The data which is available in RDBMS is of structured data. This is what all traditional projects follow to store their data. Due to data is available in multiple patterns you need some different kind of database, which deals with unstructured data.

4 Types of Databases to store bigdata

Façade pattern

HDFS serves as the intermittent Façade for the traditional DW systems.

Lean pattern

HBase is indexed using only one column-family and only one column and unique row-key.

NoSQL pattern

Traditional RDBMS systems are replaced by NoSQL alternatives to facilitate faster access and querying of big data.

Polyglot pattern

Multiple types of storage mechanisms—like RDBMS, file storage, CMS, OODBMS, NoSQL and HDFS—co-exist in an enterprise to solve the big data problem.

Typical storage in an analytics platform…

Let  us take a look on Hadoop analytics platform…

The data which is coming from RDBMS/NOSQL is first comes to HDFS, which is the format understand by Hadoop framework.


In the above image, data is first coming from RDBMS/NOSQL into HDFS. This data will be used by Hadoop platform for data analysis.

Let us see an example of HP-Vertica, which has all the capabilities, to integrate your data into multiple data sources for data analytics. 

EntityVendor tie-ups
Hadoop Software DistributionCloudera, Hortonworks or MAPR distribution
StorageHP Vertica – RAID compliant columnar database
InfrastructureHP Proliant servers
Machine LearningR

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Author: Srini

Experienced software developer. Skills in Development, Coding, Testing and Debugging. Good Data analytic skills (Data Warehousing and BI). Also skills in Mainframe.