 | |
| Developer(s) | Apache Software Foundation |
|---|---|
| Initial release | 1 June 2009 |
| Final release | 1.4.7 / December 6, 2017 |
| Repository | Sqoop Repository |
| Written in | Java |
| Operating system | Cross-platform |
| Type | Data management |
| License | Apache License 2.0 |
| Website | sqoop |
Sqoop is a command-line interface application for transferring data between relational databases and Hadoop. [1]
The Apache Sqoop project was retired in June 2021 and moved to the Apache Attic. [2]
Sqoop supports incremental loads of a single table or a free form SQL query as well as saved jobs which can be run multiple times to import updates made to a database since the last import. Imports can also be used to populate tables in Hive or HBase. [3] Exports can be used to put data from Hadoop into a relational database. Sqoop got the name from "SQL-to-Hadoop". [4] Sqoop became a top-level Apache project in March 2012. [5]
Informatica provides a Sqoop-based connector from version 10.1. Pentaho provides open-source Sqoop based connector steps, Sqoop Import [6] and Sqoop Export, [7] in their ETL suite Pentaho Data Integration since version 4.5 of the software. [8] Microsoft uses a Sqoop-based connector to help transfer data from Microsoft SQL Server databases to Hadoop. [9] Couchbase, Inc. also provides a Couchbase Server-Hadoop connector by means of Sqoop. [10]
Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use. It has since also found use on clusters of higher-end hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.
HBase is an open-source non-relational distributed database modeled after Google's Bigtable and written in Java. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS or Alluxio, providing Bigtable-like capabilities for Hadoop. That is, it provides a fault-tolerant way of storing large quantities of sparse data.
A database shard, or simply a shard, is a horizontal partition of data in a database or search engine. Each shard is held on a separate database server instance, to spread load.
The following tables compare general and technical information for a number of online analytical processing (OLAP) servers. Please see the individual products articles for further information.
Pentaho is business intelligence (BI) software that provides data integration, OLAP services, reporting, information dashboards, data mining and extract, transform, load (ETL) capabilities. Its headquarters are in Orlando, Florida. Pentaho was acquired by Hitachi Data Systems in 2015 and in 2017 became part of Hitachi Vantara.
Couchbase Server, originally known as Membase, is a source-available, distributed multi-model NoSQL document-oriented database software package optimized for interactive applications. These applications may serve many concurrent users by creating, storing, retrieving, aggregating, manipulating and presenting data. In support of these kinds of application needs, Couchbase Server is designed to provide easy-to-scale key-value, or JSON document access, with low latency and high sustainability throughput. It is designed to be clustered from a single machine to very large-scale deployments spanning many machines.
Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java, Python, JavaScript, Ruby or Groovy and then call directly from the language.
Apache Hive is a data warehouse software project. It is built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data.
Within database management systems, the record columnar file or RCFile is a data placement structure that determines how to store relational tables on computer clusters. It is designed for systems using the MapReduce framework. The RCFile structure includes a data storage format, data compression approach, and optimization techniques for data reading. It is able to meet all the four requirements of data placement: (1) fast data loading, (2) fast query processing, (3) highly efficient storage space utilization, and (4) a strong adaptivity to dynamic data access patterns.
Apache Accumulo is a highly scalable sorted, distributed key-value store based on Google's Bigtable. It is a system built on top of Apache Hadoop, Apache ZooKeeper, and Apache Thrift. Written in Java, Accumulo has cell-level access labels and server-side programming mechanisms. According to DB-Engines ranking, Accumulo is the third most popular NoSQL wide column store behind Apache Cassandra and HBase and the 67th most popular database engine of any type (complete) as of 2018.
Hortonworks was a data software company based in Santa Clara, California that developed and supported open-source software designed to manage big data and associated processing.
Apache Drill is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. Built chiefly by contributions from developers from MapR, Drill is inspired by Google's Dremel system. Drill is an Apache top-level project. Tom Shiran is the founder of the Apache Drill Project. It was designated an Apache Software Foundation top-level project in December 2016.
Apache Impala is an open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop. Impala has been described as the open-source equivalent of Google F1, which inspired its development in 2012.
Apache Phoenix is an open source, massively parallel, relational database engine supporting OLTP for Hadoop using Apache HBase as its backing store. Phoenix provides a JDBC driver that hides the intricacies of the NoSQL store enabling users to create, delete, and alter SQL tables, views, indexes, and sequences; insert and delete rows singly and in bulk; and query data through SQL. Phoenix compiles queries and other statements into native NoSQL store APIs rather than using MapReduce enabling the building of low latency applications on top of NoSQL stores.
Presto is a distributed query engine for big data using the SQL query language. Its architecture allows users to query data sources such as Hadoop, Cassandra, Kafka, AWS S3, Alluxio, MySQL, MongoDB and Teradata, and allows use of multiple data sources within a query. Presto is community-driven open-source software released under the Apache License.
Apache Kylin is an open source distributed analytics engine designed to provide a SQL interface and multi-dimensional analysis (OLAP) on Hadoop and Alluxio supporting extremely large datasets.
Apache Parquet is a free and open-source column-oriented data storage format in the Apache Hadoop ecosystem. It is similar to RCFile and ORC, the other columnar-storage file formats in Hadoop, and is compatible with most of the data processing frameworks around Hadoop. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk.
Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data.
Apache ORC is a free and open-source column-oriented data storage format. It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is used by most of the data processing frameworks Apache Spark, Apache Hive, Apache Flink, and Apache Hadoop.
The Sqoop Import job allows you to import data from a relational database into the Hadoop Distributed File System (HDFS) using Apache Sqoop.
The Sqoop Export job allows you to export data from Hadoop into an RDBMS using Apache Sqoop.
Pentaho's Business Analytics 4.5 is now certified on Cloudera's latest releases, Cloudera Enterprise 4.0 and CDH4. Pentaho also announced that its visual design studio capabilities have been extended to the Sqoop and Oozie components of Hadoop.
