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The above video is the recorded session of the webinar on the topic “Hadoop for Data Warehouse Professionals”, which was conducted on 31st May’14.
All Data Warehousing folks out there, are you aware of Hadoop and the Data warehousing paradigm? Do you realize how important it is to know Big Data and Hadoop, as Data Warehouse professionals? If the answer is ‘Yes’, then you can read this post to endorse your awareness. If ‘No’, then read ahead:
Organizations across all industries are growing extremely fast, resulting in high volume, complex and unstructured data. The huge data generated is limiting the traditional Data Warehouse system, making it tougher for IT and data management professionals to handle the growing scale of data and analytical workload. The flow of data is so much more than what the existing Data Warehousing platforms can absorb and analyze. Looking at the expenses, the cost to scale traditional Data Warehousing technologies are high and insufficient to accommodate today’s huge variety and volume of data. Therefore, the main reason behind organizations adopting Hadoop is that, it is a complete open-source data management system. Not only does it organize, store and process data (whether structured, semi-structured or unstructured), it is cost effective as well.
Hadoop’s role in Data Warehousing is evolving rapidly. Initially, Hadoop was used as a transitory platform for extract, transform, and load (ETL) processing. In this role, Hadoop is used to offload processing and transformations performed in the data warehouse. This replaces an ELT (extract, load, and transform) process that required loading data into the data warehouse as a means to perform complex and large-scale transformations. With Hadoop, data is extracted and loaded into the Hadoop cluster where it can then be transformed, potentially in near-real time, with the results loaded into the data warehouse for further analysis.
Offloading transformation processing to Hadoop frees up considerable capacity in the data warehouse, thereby postponing or avoiding an expensive expansion or upgrade to accommodate the relentless data deluge.
Hadoop has a role to play in the “front end” of performing transformation processing as well as in the “back end” of offloading data from a data warehouse. With virtually unlimited scalability at a per-terabyte cost that is more than 50 times less than traditional data warehouses, Hadoop is quite well-suited for data archiving. Because Hadoop can perform analytics on the archived data, it is necessary to move only the specific result sets to the data warehouse (and not the full, large set of raw data) for further analysis.
Appfluent, a data usage analytics provider calls this the “Active Archive” — an oxymoron that accurately reflects the value-added potential of using Hadoop in today’s data warehousing environment. They have found that for many companies, about 85 percent of their tables go unused, and that in the active tables, up to 50 percent of the columns go unused. The combination of eliminating “dead data” at the ETL stage and relocating “dormant data” to a low-cost Hadoop Active Archive can be considerable, resulting in truly extraordinary savings.
Hadoop’s original MapReduce framework — purpose-built for large-scale parallel processing — is also eminently suitable for data analytics in a data warehouse.
Hadoop effectively makes ETL integral to, and seamless with, data analytics and archival processing. It is this beginning-to-end role in Data Warehousing that has given impetus to what is Hadoop’s ultimate role as an enterprise data management hub in a multi-platform data analytics environment
With the numerous benefits offered by Hadoop, all leading organizations are moving their data management system from the traditional Data Warehousing to Big Data and Hadoop. When considering Data Warehousing as a career, it is better to be updated with the latest trends and products of database management. Hadoop will not replace relational databases or traditional Data Warehouse platforms at the moment, but its superior price/performance ratio will give organizations an option to lower costs while maintaining their existing applications and reporting infrastructure. Saying this, it leaves loads of possibility for Hadoop to take over the duties of a traditional Data Warehouse in the near future.
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