Hadoop etl
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The document discusses Extract, Transform, Load (ETL) processes and how they are used with Hadoop. ETL involves extracting data from source systems, cleaning and transforming the data, and loading it into a destination database. Traditional ETL tools have limitations for big data analytics. Hadoop provides an alternative ETL architecture using tools like Apache Sqoop and Apache Flume that can handle large volumes of data in a distributed, scalable manner by extracting data from relational databases and loading it into Hadoop for processing.
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Hadoop etl
- 2. What is ETL?
- 3. ETL… • Extract: Extract data from source system and make it accessible for further processing. • Clean Ensures quality of data in warehouse. • Transform: Applies a set of rules on data flowing from source to data. • Load Load in destination database. • Staging
- 4. Benefits & Limitations of Traditional ETL • Benefit • Helps to address issues like Change Management, Slowly changing dimensions, inserts, updates, etc. • Limitations • Early in process someone has to decide what data is important, who can access it, what should be updated. • Original raw data is not stored and can’t be retrieved.
- 5. ETL Bottleneck in Big Data Analytics • Business benefits of analyzing of Big Data can be significant.
- 8. ETL Tools • For Traditional ETL tools like Teradata Warehouse Builder, DataStage are used. • For ETL with Hadoop, Apache Sqoop, Apache Flume are used .
- 9. Apache SQOOP • Sqoop is designed to transfer data between Hadoop and Relational Databases. • You can use Sqoop to import data from a relational database management system (RDBMS) such as MySQL or Oracle into the Hadoop Distributed File System (HDFS), transform the data in Hadoop MapReduce, and then export the data back into an RDBMS.
- 11. What is Sqoop ? • A different paradigm Data Application Data
- 12. • A very scalable different paradigm Data Data Application Data Application Data
- 13. • Where did the transform go ? Application Data TTT TTT TTT TTT
- 14. Any Questions ?
- 15. References • https://www.ibm.com/developerworks/library/bd-hivetool/index.html • https://www.csgsolutions.com/blog/what-is-etl/ • https://software.intel.com/sites/default/files/article/402274/etl-big-data- with-hadoop.pdf