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.
| 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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