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Big Data Engineering

Big Data Sqoop | Get Started With Big Data Hadoop Sqoop

Navdeep Singh Gill | 17 July 2022

What is Apache Sqoop?

Many of us still wonder what Apache Sqoop is, its architecture, features, uses, and how it is related to big data. In this Sqoop write up, we will talk about everything along with its requirements. Let’s get started! Apache Sqoop is a big data tool for transferring data between Hadoop and relational database servers. It is used to transfer data from RDBMS (relational database management system) like MySQL and Oracle to HDFS (Hadoop Distributed File System). It can also be used to transform data in Hadoop MapReduce and then export it into RDBMS. It is a data collection and ingestion tool used to import and export data between RDBMS and HDFS.


Why do we need it?

Itl is primarily used for bulk data transfer to and from relational databases or mainframes. It can import from entire tables or allow the user to specify predicates to restrict data selection. You can write directly to HDFS as Sequence files or Avro. It can take data directly into Hive or Hbase with proper command line arguments. Finally, you can also export data back out to relational databases using Sqoop in Big Data. A typical workflow with Big Data Hadoop Sqoop is where data is brought in Hive, so intermediate processing and transformation tasks can be done on Apache Hadoop. Once processing is done, data can be exported back to a database. This is one of many ways to perform "data warehouse offloading," where Hadoop is used for ETL purposes.
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What are the key features of it?

  • Parallel Import/Export: Sqoop Big Data Tool uses the YARN framework to import and export data. This provides fault tolerance on top of parallelism
  • Import Results of SQL Query: Big Data Hadoop Sqoop allows us to import the result returned from an SQL query into HDFS
  • Connectors for All Major RDBMS Databases: It provides connectors for multiple Relational Database Management System (RDBMS) databases such as MySQL and MS SQL Server
  • Offers Full and Incremental Load: It can load the whole table or parts of the table by a single command. Hence, it supports the full and incremental load.

Sqoop Import

It imports every single table from RDBMS to HDFS. Each row within a table is treated as a single record in HDFS. All records are then stored as text data in the format of text files or as binary data in Avro and Sequence files. sqoop import architecture

Sqoop Export

Its tool exports files from HDFS back to an RDBMS. All the files given as input to it contain records, which are called rows in the table. Later, those are read and parsed into a set of records and delimited with a user-specified delimiter. sqoop export architecture

Where can we use it?

Relational database systems are used to interact with business applications. One of the biggest and largest sources of it in the modern world is the relational database system. In Hadoop, different processing frameworks like MapReduce, Hive, HBase, Cassandra, Pig, etc. and storage frameworks like HDFS can benefit distributed computing and distributed storage. Data needs to be transferred among database systems and Hadoop Distributed File System (HDFS) to store and analyze it from relational databases. Here, Sqoop in Big Data comes into the picture. It acts like a middle man/ a layer between Hadoop and relational database systems. One can import data and export data between relational database systems and Hadoop and its eco-systems directly using it.
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Sqoop in Big Data provides a command-line interface to the end-users. It can also be accessed through Java APIs. It command submitted by the user is parsed by it and launches Hadoop Map only to import or export data because the Reduce phase is only required when aggregations are required. It just imports and exports the information; it does not do aggregations. It parses the arguments given in the command line and prepares the Map job. A map job launching multiple mappers depends on the number defined by the user in the command line. During Sqoop import, each mapper task is assigned with part of the data to be imported based on the command line. Data is distributed equally by it among the mappers to get high performance. After that, each mapper creates a connection with the database using JDBC and then imports the part of the data given by it, and then it is written into the HDFS or Hive or Apache HBase according to the option provided in the command line.

What is the difference between Apache Flume and Apache SQOOP?

Apache Sqoop and Apache Flume work with different kinds of data sources. Flume functions well in streaming data sources generated continuously in a Hadoop environment, such as log files from multiple servers. On the other hand, Apache Sqoop is designed to work well with any relational database system with JDBC connectivity. Sqoop in it can also import data from NoSQL databases like MongoDB or Cassandra and allow direct data transfer or Hive or HDFS. For transferring data to Hive using the Apache Sqoop tool, a table must be created for which the schema is taken from the original database. In Apache Flume, data loading is events that do not drive event-driven, whereas in Apache Sqoop data load. apache sqoop Flume is a better option when moving bulk streaming data from various sources like JMS or Spooling directory. In contrast, it is an ideal choice. If the data is saved in databases like Teradata, Oracle, MySQL Server, Postgres, or any other JDBC compatible database, it is best to use it. In Apache Flume, data flows from HDFS through multiple channels.

Apache Flume, Big Data Ingestion has agent-based architecture i.e., the code written in the flume is known as an agent that is responsible for fetching data. In contrast, in its architecture is based on the connectors. The connectors in Sqoop know how to connect with the various data sources and fetch data accordingly. apache flume Sqoop and Flume cannot be used to achieve the same tasks as they are developed specifically to serve different purposes. Apache Flume agents are developed to fetch streaming data like tweets from Twitter or log files from the webserver, whereas its connectors are designed to work only with structured data sources and fetch data from them.

It is used for parallel data transfers and data imports as it copies data quickly, whereas Apache Flume is utilized for collecting and aggregating data as it is distributed, the true nature, and highly available backup routes.

Features Apache Sqoop  Apache Flume 
Functionality  Works well for Streaming data sources generated continuously in a Hadoop environment Any relational database system with JDBC connectivity.


Connector-Based Architecture Agent-Based Architecture
Driven Events Not Event Driven  Completely Event-Driven.
Where to use Parallel data transfers and data imports as it copies data quickly Pull Data from different sources to analyze the patterns, perform sentiment analysis etc

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