If you want to learn Apache Sqoop, then you have landed in the right place. The Big Data tool, Apache Sqoop, is used for data transferring between the Hadoop framework and the relational database servers.<\/p>\n
In this Apache Sqoop Tutorial, you will explore the whole concepts related to Apache Sqoop. The article will explain what Apache Sqoop is, why we use Sqoop, how Sqoop works, the prerequisites required for learning Sqoop, Different Sqoop Releases, and many more.<\/p>\n
You will learn how Sqoop came into the picture and what are the various advantages of Apache Sqoop. The article also enlists some of the features and limitations of Apache Sqoop.<\/p>\n
The article also covers Sqoop Import and Sqoop Export tools. We will first begin by learning what Apache Sqoop is. Then, later on, we will explore how it works and what are its various advantages and limitations.<\/p>\n
Let us first start with an introduction to Apache Sqoop.<\/p>\n
<\/p>\n
Apache Sqoop is a tool designed for data transfer between the Hadoop Distributed File System and the relational databases or mainframes.<\/p>\n
We can use Apache Sqoop for importing data from the RDBMS, that is, relational database management systems such as Oracle or MySQL or a mainframe into the HDFS (Hadoop Distributed File System).<\/p>\n
We can use Sqoop for transforming data in Hadoop MapReduce and then exporting it back into the RDBMS.<\/p>\n
Initially, Sqoop was developed and managed by Cloudera. Later on, on 23 July 2011, Sqoop was incubated by Apache Software Foundation. In April 2012, Sqoop was promoted as Apache’s top-level project.<\/p>\n
Apache Sqoop relies on the relational database to describe the schema for data to be imported. It uses the Hadoop MapReduce model for importing and exporting the data. This provides the capability of fault tolerance as well as parallel operation.<\/p>\n
Sqoop can easily integrate with the Hadoop and dump structured data from RDBMS on HDFS, thus complementing the Hadoop\u2019s power.<\/p>\n
For the Hadoop developers, the actual game begins after loading data into the Hadoop Distributed File System (HDFS). They play with this data to gain useful insights that are hidden in the data stored in HDFS.<\/p>\n
For performing such analysis, they need the data in the RDBMS (relational database management systems) to be transferred to the HDFS.<\/p>\n
For transferring the data from RDBMS to HDFS, they have to write MapReduce code for data import and export, which is a very tiresome and tedious task. So, this was where the Apache Sqoop came into the picture.<\/p>\n
The Sqoop introduction has rescued and removed their pain. Apache Sqoop automates the process of data import and export.<\/p>\n
The development of Apache Sqoop makes the developer\u2019s life easy. Sqoop provides the Command Line Interface to the developers for importing and exporting data.<\/p>\n
Developers now have to provide necessary information such as database authentication, source, destination, operations, etc. Sqoop itself takes care of the remaining part.<\/p>\n
Internally, Sqoop converts the Sqoop command into the MapReduce tasks. These MapReduce tasks are then executed over the HDFS.<\/p>\n
Sqoop uses YARN (Yet Another Resource Negotiator) framework for importing and exporting the data. This provides fault tolerance on the top of parallelism.<\/p>\n
RDMS are widely used for interacting with traditional business applications. So, they have become one of the Big Data generating sources.<\/p>\n
For dealing with Big Data, we use a Hadoop framework that stores and processes Big Data by using storage frameworks like HDFS and various processing frameworks such as MapReduce, HBase, Hive, HBase, Pig, Cassandra, Pig, etc. to achieve advantages of distributed storage and computing.<\/p>\n
To store and analyze Big Data generated from RDBMS, we need to transfer this data to Hadoop Distributed File System (HDFS). In such a situation, the Apache Sqoop comes into the picture.<\/p>\n
It acts as a mediator between Hadoop and RDBMS. We can export and import data between the RDBMS and the Hadoop and its eco-system components by directly using sqoop.<\/p>\n
For learning and using Sqoop, you must know the following:<\/p>\n
The Sqoop import tool imports the individual tables from Relational Databases to Hadoop Distributed File System. Each row of a table in RDBMS is treated as a record in the HDFS.<\/p>\n
All these records are stored as text data in the text files or as the binary data in the Avro and Sequence files.<\/p>\n
The Sqoop export tool exports the set of files from the Hadoop Distributed File System back to the Relational Database. The files which are given as an input to the Sqoop contain the records.<\/p>\n
These records are called as rows in a table. Those are read and parsed into the set of records and are delimited with the user-specified delimiter.<\/p>\n
Let us now see the difference between Sqoop, Flume, and HDFS.<\/p>\n
| Flume<\/b><\/td>\n | Sqoop<\/b><\/td>\n | HDFS<\/b><\/td>\n<\/tr>\n| Apache Flume is designed for moving bulkier streaming data into the HDFS.\u00a0<\/span><\/td>\n | Apache Sqoop is designed for importing data from relational databases to HDFS.\u00a0<\/span><\/td>\n | HDFS is the distributed file system used by Apache Hadoop for data storing.\u00a0<\/span><\/td>\n<\/tr>\n | It has an agent-based architecture. In Flume, the code is written (called as ‘agent’) that takes care of the data fetching.\u00a0<\/span><\/td>\n | It has a connector based architecture. A Connector will know how to connect to the data source and how to fetch the data.<\/span><\/td>\n | It has a distributed architecture. The data is distributed across commodity hardware.<\/span><\/td>\n<\/tr>\n | In Flume, the data flows via zero or more channels to the HDFS.\u00a0<\/span><\/td>\n | HDFS is the destination for importing data using Sqoop.<\/span><\/td>\n | HDFS is a final destination for data storage.<\/span><\/td>\n<\/tr>\n | The Apache Flume data load is driven by an event.<\/span><\/td>\n | The Apache Sqoop data load is not event-driven.\u00a0<\/span><\/td>\n | It just stores the data provided by any means.\u00a0<\/span><\/td>\n<\/tr>\n | For loading streaming data like web servers log files or tweets generated on Twitter, we have to use Flume because flume agents were designed for fetching streaming data.<\/span><\/td>\n | For importing data from the structured data sources we have, to use Sqoop only because Sqoop connectors know how to interact with the structured data sources and how to fetch data from them.\u00a0<\/span><\/td>\n | HDFS has built-in shell commands for storing data into it. It cannot import streaming data.\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n | Features of Apache Sqoop<\/h3>\nSome of the salient features of apache Sqoop are:<\/p>\n 1.<\/strong> With Apache Sqoop, we can load the entire table with a single command. Refer to the Sqoop feature article to study Sqoop features in deep.<\/strong><\/p>\n Below are the significant advantages of Sqoop, which are also the reasons for choosing Sqoop technology:<\/p>\n 1.<\/strong> Sqoop allows data transfer with the different structured data stores such as Teradata, Postgres, Oracle, and so on.<\/p>\n 2.<\/strong> Since the data from RDBMS is transferred and stored into the Hadoop, Apache Sqoop allows us to offload the processing done in the ETL (Extract, Load, and Transform) process into the fast, low-cost, and effective Hadoop processes.<\/p>\n 3.<\/strong> Apache Sqoop executes data transfer in parallel, so its execution is quick and cost-effective.<\/p>\n 4.<\/strong> Sqoop helps in integration with the sequential data from the mainframe. This helps reduce high costs in executing specific jobs using mainframe hardware.<\/p>\n Sqoop has some of the limitations also. The limitations of sqoop are:<\/p>\n 1.<\/strong> We cannot pause or resume the Apache Sqoop once it is started. It is an automatic step. If in case it fails, then we have to clear the things and start it again.<\/p>\n 2.<\/strong> The performance of Sqoop Export depends on hardware configuration such as Memory, Hard disk of the RDBMS server.<\/p>\n 3.<\/strong> It is slow because it uses MapReduce in backend processing.<\/p>\n 4.<\/strong> Failures need special handling in the case of partial export or import.<\/p>\n 5.<\/strong> It has a bulkier connector for a few databases.<\/p>\n 6.<\/strong> Sqoop 1 uses a JDBC connection for connecting with RDBMS. This can be less performance and inefficient.<\/p>\n 7.<\/strong> Sqoop 1 does not provide a Graphical User Interface for easy use.<\/p>\n Sqoop 2 has overcome some of the limitations of Sqoop 1.<\/p>\n In short, we can say that Apache Sqoop is a tool for data transfer between Hadoop and RDBMS. Apache Sqoop relies on the relational database to describe the schema for data to be imported.<\/p>\n Apache Sqoop has many features like a full load, incremental load, compression, Kerberos Security Integration, parallel import\/export, support for Accumulo, etc.<\/p>\n For transferring data residing in relational database servers, we use Sqoop. The article has enlisted the difference between Sqoop, Flume, and HDFS. The article had also explained the new features added in Sqoop 2.<\/p>\n I hope after reading this article you clearly understand Sqoop, its working, advantages, and features. Still, if you have any query related to Sqoop, share it with us in the comment section.<\/p>\n","protected":false},"excerpt":{"rendered":" If you want to learn Apache Sqoop, then you have landed in the right place. 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