• The word "Sqoop" arrived from Sql + Hadoop. That is "Sq" from Sql and "oop" from Hadoop.

  • Lets come to the point, so how can we transfer a large volume of data from relational databases or enterprise data warehouse to Hadoop in an accelerated pace using multiple threads or parallelism. In this blog I will be giving you an overview and introduction of Apache scoop. Then we see some basic commands. It will be very useful for beginners.

  • so what is the scoop, well it's an open source command-line interface application which can import bulk data from relational databases like my sequel- sequel server as well as data warehouse systems to HDFS as well as export data from Hadoop file system to relational databases.

  • It gives us a capability either import all the tables of a database or single table into HDFS. It's a Hadoop ecosystem technology which offers schema on read and parallel data transfer and hence transferring data at a faster pace. Due to parallelism capabilities data can be imported and populated to hive tables in the structured form only.

  •  Sqoop supports a data sources which are JDBC compliant but for the data sources which are not a JDBC compliant scooped architecture supports various connectors or plugins thereby providing the capability to connect with several other external data sources, in that case so whenever we submit on command-line interface then scoop internally generates a Map Reduce code to transfer the data. Sqoop makes use of a primary key column to divide the source table data across several map jobs 


The data importing is basically done in two steps:

  • First, it gathers the necessary  metadata for the data which needs to be imported 
  • Sqoop submits map only job to the cluster, the actual data is then transferred using metadata that is captured ,then the corresponding imported data is saved in a directory in HDFS. 

sqoop also provides a capability to the user using which he or she can specify an alternative directory where the file has to be populated. By default it generates comma delimited fields and the records are separated by newline character. One can override this CSV format by specifying the record Terminator character and the field separator in the scope command explicitly.

We can perform both import and export activities. Let us see some symbolic representation , so that it will be remember for us,


Sqoop Import: 


           

Importing data from RDMS to HDFS, HIVE and HBASE are all possible.


Sqoop Export: 



       

Exporting data from HBAS to RDMS alone not possible, As RDMS cannot understand the Unstructured data.

Let us see some basic commands.


1) How to Ensure Sqoop can interact with DB. 


  •       Method 1:  Using List database command.
    sqoop list-databases --connect jdbc:mysql:[hostname] --username [username] --password [password]    

Inside the [ ] brackets , you need to provide the respective values as mentioned. 
 
  •        Method 2: Running a query using sqoop. eval

    sqoop eval --connect jdbc:mysql:[hostname]/[db name] --username [username] --password [password] --query "select * from table"    


 


2) Sqoop Code Gen Command:

    Sqoop Codegen --connect jdbc:mysql:[hostname]/[db name] --username [username] --password [password] --table [table name] --bindir [filename]      


Inside the [ ] brackets , you need to provide the respective values as mentioned.  


Mastering the basic commands of Apache Sqoop is a crucial step towards efficiently managing data transfers between your relational databases and Hadoop ecosystem. As you explore the capabilities of Sqoop, these commands will serve as your foundation for handling diverse data integration scenarios


Let us see some points on Advantages of Sqoop:

    1.Seamless Data Integration:

            Apache Sqoop excels in seamlessly integrating data between Apache Hadoop and relational databases. It provides a straightforward and efficient way to transfer data across these two environments, bridging the gap between big data processing and traditional databases.

    2.  Time and Resource Efficiency:

            Sqoop automates the data transfer process, reducing the need for manual intervention. This automation not only saves time but also minimizes the risk of errors that can occur with manual data handling, making the overall data transfer process more efficient.

    3. Parallel Processing Power:

            One of Sqoop's significant advantages is its support for parallel data transfers. By dividing the workload among multiple mappers, Sqoop enhances performance, allowing for faster and more scalable data imports and exports.

    4. Incremental Data Transfers:

            Sqoop facilitates incremental imports, allowing users to transfer only the data that has been added or modified since the last import. This feature is valuable for scenarios where only the latest changes need to be synchronized between the Hadoop ecosystem and the relational database.

    5. Command-Line Simplicity:

            The command-line interface of Sqoop simplifies the process of initiating data transfers. Users can perform complex data import and export tasks with concise and straightforward commands, making Sqoop accessible to both beginners and experienced data professionals.


Let us also see some of the disadvantages of sqoop:

    1.Dependency on JDBC Drivers:

            Sqoop relies on JDBC drivers for connectivity, and any discrepancy or lack of compatibility in these drivers may cause disruptions. Users need to ensure that their JDBC drivers are up-to-date and compatible with the versions of the databases they intend to connect to, mitigating potential connectivity challenges.

    2. Missing Instrument for Real-Time Symphony:

            Sqoop, designed for batch-oriented data transfers, lacks a real-time instrument in its repertoire. For organizations craving a symphony of real-time data, Sqoop's absence of a real-time conductor may necessitate the incorporation of other tools like Apache Kafka to create a more dynamic and responsive data orchestra.

    3.Complexity in Handling Unstructured Data:

            While Apache Sqoop is adept at navigating the structured waters of relational databases, it encounters challenges when dealing with unstructured or semi-structured data. The tool's forte lies in the ordered rows and columns of traditional databases, leaving users seeking alternative solutions for more intricate, non-tabular data types.


Thus in this blog we saw about the Apache Sqoop explanation with basic commands. We also saw the Advantages and the disadvantages of Apache Sqoop. Hope this blog is helpful. After learning this you can learn about Sqoop import and incremental import commands in this link click_here. 


Thank You !!!