03 March 2016

How Hadoop is best suitable for large legacy data

How Hadoop is Suitable for Data Available on Legacy Systems
#How Hadoop is Suitable for Data Available on Legacy Systems:
How should we be thinking about migrating data from legacy systems?
Treat legacy data as you would any other complex data type. HDFS acts as an active archive, enabling you to cost effectively store data in any form for as long as you like and access it when you wish to explore the data. And with the latest generation of data wrangling and ETL tools, you can transform, enrich, and blend that legacy data with other, newer data types to gain a unique perspective on what’s happening across your business.

What are your thoughts on getting combined insights from the existing data warehouse and Hadoop?
Typically one of the starter use cases for moving relational data off a warehouse and into Hadoop is active archiving. This is the opportunity to take data that might have otherwise gone to archive and keep it available for historical analysis. The clear benefit is being able to analyze data for the types of extended time periods that would not otherwise be cost feasible (or possible) in traditional data warehouses.  An example would be looking at sales, not just in the current economic cycle, but going back 3 – 5 years or more across multiple economic cycles.

You should look at Hadoop as a platform for data transformation and discovery, compute intensive tasks that aren’t a fit for a warehouse. Then consider feeding some of the new data and insights back into the data warehouse to increase its value.

What’s the value of putting Hadoop in Cloud? 

The cloud presents a number of opportunities for Hadoop users. Time to benefit through quicker deployment and eliminating the need to maintain cluster infrastructure Good environment for running proof-of-concepts and experimenting with Hadoop Most Internet of Things data is cloud data. Running Hadoop in the cloud enables you to minimize the movement of that data The elasticity of the cloud enables you to rapidly scale your cluster to address new use cases or add more storage and compute.

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