Bigdata has no specific format. It is basically unstructured data. To make your big data available for your data analytics, you need to store data in certain databases. Otherwise, it is not possible to carry out proper analytics on the data.
The data that is available in RDBMS is structured data. This is what all traditional projects follow to store their data. Because data is available in multiple patterns, you need a different kind of database that deals with unstructured data.
4 Types of Databases to store big data
Façade pattern
HDFS serves as the intermediate façade for the traditional DW systems.
Lean pattern
HBase is indexed using only one column family, one column, and a 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 at the 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 first comes from RDBMS/NOSQL into HDFS. This data will be used by the 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.
| Entity | Vendor tie-ups |
| Hadoop Software Distribution | Cloudera, Hortonworks or MAPR distribution |
| Storage | HP Vertica – RAID compliant columnar database |
| Infrastructure | HP Proliant servers |
| Analytics/Visualization | SAS |
| Machine Learning | R |
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