Why Sqoop is required

let us discuss Why Sqoop is required in projects,

Why Sqoop is required ?

*  In Sqoop we can perform parallel import/export data from RDBMS to HDFS/Hive and vice-versa.

* Using Sqoop we can create a table in Hive similar to the RDBMs table in one shot.

* We can specify a query in Sqoop instead of a table to utilize where clause for importing partial data.

* We can query a RDBMS table using Sqoop eval example performing a row count etc.

* Sqoop has fault tolerance mechanism due to MapReduce framework used in import and export data.

* Using Sqoop we list database and tables of RDBMS.

* Sqoop filled the gap between relational databases and Hadoop system.

* Before Sqoop exist, developers need to write script for import and export, because of Sqoop programmer’s task is reduced.

* Using Sqoop we can compress the huge dataset, so that it saves the memory space.

* Sqoop provides better performance because it has connectors for working with popular relational databases like MySQL, PostgreSQL, Oracle, SQL Server, and DB2.


Where Sqoop is required in projects.

Sqoop tool is required in the following cases,

1. When we are working with structured data, we need to import whole dataset into hadoop for data analysis in that time sqoop is required.

2. Sqoop is used in ELT(Extract Load Transform)

Example: Billing data needs to be run every week, in this case we can run the processing of billing as batch in Hadoop taking the advantage of parallel processing and then send the billing summary data back to RDBMS.

3. It is used as ETL(Extract Transform and Load)

i.e Extract the data sets from RDBMS into Hadoop and use Hadoop as an intermediate parallel processing engine which is part of the overall ETL process, basically Hadoop is becoming the T(Transform of the ETL) the end results then can be copied to the traditional data warehouse.

4. It can be used as Data Archival i.e Data those are not frequently accessed are moved to Hadoop using Sqoop, keeping the RDBMS small and lean.

5. We can use it for Data Consolidation

i.e in organizations data can be widely spread across various RDBMS like oracle Database for ERP, MySQL database for Web data, SQL Server for CRM data and even Legacy systems like mainframe DB2 for Billing data etc. Here Sqoop helps in exporting all these data to the Hadoop data lake.

6.Used in BI Reporting

i.e Sqoop can directly copy the data from the RDBMS and create hive tables on Hadoop, and then we can run SQL statements to query data on it, thus these BI tool can directly connect to Hadoop using the connection string and perform the same activity, the BI tool users are abstracted and gives them the same feel of working on RDBMS even though they are connected to Hadoop.