Скачать или смотреть 13 SQOOP - SQL on Hadoop, Introduction and Installation

Sqoop is a tool designed to facilitate the transfer of data between Hadoop (specifically Hadoop Distributed File System, or HDFS) and relational databases. The name "Sqoop" is a portmanteau of "SQL" (Structured Query Language) and "Hadoop." Here's an introduction to Sqoop:

1. Data Integration:

Sqoop is a powerful tool for integrating data between Hadoop and relational databases. It enables you to efficiently import and export data, making it a valuable component in the big data ecosystem.
2. Relational Databases:

Sqoop primarily connects with traditional relational databases like MySQL, PostgreSQL, Oracle, SQL Server, and others.
It can import data from a database into Hadoop for processing or export data from Hadoop back into a database.
3. Key Features:

Parallel Data Transfer: Sqoop can transfer data in parallel, which improves efficiency and reduces transfer times.
Data Compression: It supports data compression to minimize storage space and network bandwidth.
Incremental Loading: Sqoop can be configured for incremental data transfers, ensuring that only new or modified data is moved, which is useful for keeping Hadoop datasets up to date.
Schema Evolution: It handles schema changes during import, adapting to changes in the database schema.
Custom Code Integration: You can incorporate custom code or transformations during the data transfer process.
4. Workflow:

When importing data into Hadoop, Sqoop generates a series of MapReduce jobs to parallelize and distribute the data import.
When exporting data from Hadoop, it utilizes a series of reducers to export data in parallel.
5. Command Line Interface:

Sqoop provides a command-line interface that allows you to specify import and export tasks with a range of options and parameters.
It can also be integrated into scripts and workflows for automation.
6. Integration with Hadoop Ecosystem:

Sqoop is often used alongside other Hadoop ecosystem components like Hive, Pig, and HBase to process and analyze imported data.
7. Use Cases:

Common use cases for Sqoop include migrating data from a relational database to HDFS for big data analysis, and exporting the results of Hadoop processing back to a database for reporting.
It's valuable in scenarios where structured data in databases needs to be combined with unstructured data in Hadoop.
Sqoop simplifies the process of moving data between the world of relational databases and the Hadoop ecosystem, making it an essential tool for organizations working with big data. It's particularly useful for data engineers, data analysts, and data scientists who need to work with both structured and unstructured data in a Hadoop environment.