Apache Sqoop is part of the Hadoop ecosystem. Since a lot of the data had to be transferred from relational database systems onto Hadoop, there was a need for a dedicated tool to do this task fast. This is where Apache Sqoop came into the picture which is now extensively used for transferring data from RDBMS files to the Hadoop ecosystem for MapReduce processing and so on.
When it comes to transferring data, there is a certain set of requirements to be taken care of. It includes the following: Data has to have consistency; it should be prepared for provisioning the downstream pipeline, and the users should ensure the consumption of production system resources; among other things. The MapReduce application is not able to directly access the data that is residing in external relational databases. This method can expose the system to the risk of too much load generation from the cluster nodes.
Understanding Apache Sqoop
||How does Sqoop deliver?
|Fast data analysis
||By deploying ‘schema on read’ by combining structured and unstructured data
|Balancing of load
||By taking care of extra storage and processing
|Parallel data transfer
||By satisfying the need for high-speed parsing of data
||By moving data fast from external sources to Hadoop
The Sqoop tool can help import the structured data from relational databases, NoSQL systems, and even from enterprise data warehouses. This tool makes it easy to import data from external systems to HDFS. This way it is possible to populate the tables in Hive and HBase. You can write Sqoop jobs using Sqoop scripts. Integrating Sqoop with Oozie allows for the scheduling and automating of import and export tasks. The Sqoop architecture is a connector-based architecture that can support plugins, which provides connectivity to new external sources.
What happens underneath the covers when you run Sqoop is very straightforward. The dataset that is transferred is sliced up into different partitions, and a map-only job is launched with individual mappers responsible for transferring each slice of this dataset. Each record of this data is handled in a type-safe manner, as Sqoop uses the database metadata to infer the data types.
How Does Apache Sqoop Make Data Transfer so Easy?
You can consider this blog as a user guide for working with Apache Sqoop. When it comes to importing data with Apache Sqoop, it is done in two steps. First, the database is used to gather the necessary metadata for the data to be imported. In the second step, it is a map-only Hadoop job that Sqoop submits to the cluster. In this job, the actual data is transferred using the metadata that is captured. Based on the table being imported, the data is saved in a directory in HDFS. If needed, Sqoop allows the user to specify an alternative directory where the file has to be populated. The file here contains comma-delimited fields, and the records are separated by new lines. You can override this format in which the data is copied through explicitly, by specifying the record terminator character and the field separator.
- While you are importing data, you can create and load the partition or table.
- For invoking the needed command with metadata, you can populate the Hive metastore.
- You can populate a particular column family with HBase table.
- You can export data from Hadoop into external databases.
This is the process in which individual tables are imported from the relational database to Hadoop Distributed File System (HDFS). For the purpose of transferring, the row in a table is considered as a record in HDFS. The recorded data is stored in the form of text data in text files, or they are stored in Sequence and Avro files as binary data.
It is possible to do Sqoop import to Hive. You can create and load a partition or table with Hive, while importing the data. When it is done manually, the correct type of mapping between the data and details should be done. It is possible to populate the Hive metastore with metadata to invoke the needed command for loading the table and partitioning.
Sqoop also supports incremental import of data where only those rows that are new will be added to the existing database, and if needed the files can even be merged without any issues.
Sqoop export is used for transferring data from HDFS to RDBMS. The input of the Sqoop file would be the records that are considered as rows in the table. This data is scanned and processed into records and used with the user-specified delimiter.
The export of data is done in two distinct steps. The first step includes examining the database for the metadata, and the second step includes the transferring of the data. The Sqoop data is divided into various splits, and then there is the utilization of map tasks so as to push these splits into the database.
Sometimes, there might be a need for Hadoop pipelines to process data into the production system and run the critical business functions. Sqoop can also be deployed for exporting data into external datastores, when necessary.
Understanding Sqoop Connectors
Connectors help transfer the data from any external source to Hadoop. They are also needed to isolate production tables in case of job failure corruption. The tables are populated with the map task and merged with the target table so as to get the data delivered.
You can use specialized connectors in order to connect to the external systems that have optimized import and export. This does not support the native Java Database Connectivity (JDBC). There are plugins that can be used for Sqoop installation and they are part of the Sqoop extension framework.
In the real world, there are a lot of Sqoop examples wherein the connectors are used for different databases like SQL Server, IBM DB2, MySQL, and PostgreSQL. These connectors are efficient is transferring data at high speeds. They include the JDBC connector that is accessed via the JDBC. There are companies that are developing their own connectors to plugin to Sqoop. These connectors range from the enterprise data warehouse to NoSQL datastores.
Working with Apache Sqoop
As per Hadoop Developers, the real work starts when the data is loaded into HDFS. The developers start to derive valuable insights from the data once it is converted from relational databases into the form supported by HDFS. It is possible to write the custom coding or scripts for transferring data to and from Hadoop, but Apache Sqoop is an alternative that lets you do the same tasks in a simple yet efficient manner.
Sqoop lets you automate the process, and depending on the database it is used to describe the schema of the data to be imported. Sqoop works on the MapReduce framework for importing and exporting data, providing a parallel fault-tolerant mechanism. However, here, developers need to understand the database authentication, source, destination, and such other information.
Advantages of Apache Sqoop
Here are some of the advantages of Apache Sqoop that make this important aspect of the Hadoop ecosystem so popular.
- It involves transferring data from a variety of structured sources of data like Oracle, Postgres, etc.
- The data transfer is in parallel, making it fast and cost-effective.
- A lot of processes can be automated, bringing in heightened efficiency.
- It is possible to integrate with Kerberos security authentication.
- You can load data directly from Hive and HBase.
- It is a very robust tool with a huge support community.
- It is regularly updated, thanks to its continuous contribution and development.
Apache Sqoop is a powerful tool in today’s world where you need to transfer data from different sources in different formats onto Hadoop and then move the processed data back to the relational database systems. In this Sqoop documentation, you have seen how Sqoop makes these processes much simpler. The importance of Apache Sqoop will only grow with the advent of its latest versions, thanks to the ever-increasing role of big data in every business vertical in a digitally-driven world.
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