Apache Hadoop Data Warehouse, also known as an Enterprise Data Warehouse (EDW), is a large collective store of data that is used to make such data-driven decisions, thereby becoming one of the centrepiece of an organization’s data infrastructure.
Apache Hadoop Data Warehouse was challenge in initial days when Hadoop was evolving but now with lots of improvement, it is very easy to develop Hadoop data warehouse Architecture. This article will server as a guide to Hadoop data warehouse system design.
Hadoop data warehouse integration is now a days become more popular and many companies are working on the migration tools.
Extract Data From Sources
- Hadoop have the operational source system such as OLTP database systems. Since it is Hadoop ecosystem, you may also introduce the multi-structured data such as machine log data, weblogs, social media feeds including twitter, Facebook, linkedIn etc.
- You can use Sqoop as an ingestion mechanism if you are importing data from the tradition OLTP database systems.
- Use the Sqoop to export data for processing back to OLTP systems is done in Hadoop ecosystem.
- Source data will be ingested directly into HDFS before being transformed and loaded into target systems in designated directories.
- Transformations will occur through one of the processing frameworks supported on Hadoop, such as Spark, MapReduce, Hive, Impala or Pig etc.
Load Data to Target Systems
- Once converted data will be moved into target systems. For instance, It includes hosting the data in Hadoop via a SQL-like interface such as Impala or Hive.
- Generally, you can export data into other data warehouse for further analysis after processing. Use Sqoop export to transfer the processed data back to OLTP data warehouse systems.
- You can use sqoop to build Hadoop data warehouse ETL process with help of Python or shell scripting.
- Generally, you can access the data from the HDFS schema using various analytical tools such as analytics, BI, or visualisation tools.
Apache Hadoop Data Warehouse Advantages
Hadoop can help to overcome some of main challenges that old data warehouse systems are facing now:
- It can process the big volume and complex data. It can complete the ETL processing within the time required for constraints.
- Hadoop can process the big volume and complex data. Its DWS (distributed workload system) can reduces the excessive load on the systems.
- Hadoop is flexible
- It is cheap compared to old data warehouse systems.
Hadoop Data Warehouse Challenges
There are few Hadoop data warehouse challenges:
- If you tap data on Hadoop ecosystem that can gives you an access to potentially valuable data that might otherwise never be available in traditional data warehouse ecosystem.
- Flexibility of Hadoop allows you for evolving the schemas and handling unstructured data and semi-structured, which enables fast turnaround time when changes to downstream reports or schemas happen.
- Using Hadoop as an online archive can free up space and the resources in the data warehouse and avoids the need for expensive scaling of the data warehouse architecture.
Build Hadoop Data Warehouse
You can develop Hadoop Data warehouse in Hive or Impala. Being MPP, Impala gives you best performance compared to Hive.
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