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Consider these tips for a Successful Journey of Hadoop
You know what’s cool about Hadoop? Well, it’s flexibility. It is the best platform for storing large amounts of data. It works like an active archive and a system for doing ETL. It is a way of exploring and analyzing data. Also, it serves as a solution for cutting back fraud, knowing customers best and boosting product innovation. Well, that a few on its long list.
However, the seemingly innumerable use cases of Hadoop in a business can also put up a challenge when it comes to recognizing your first use case and predicting its success. Lacking a clearly defined use case to apply Hadoop against is probably the biggest barrier to enterprise acceptance.
So from where it should be started? And what do data-driven enterprises need to effectively deploy Hadoop across the enterprise?
The discussion with experts was rich and deep with different topics being popped around including the ways to identify an initial use case and fortify collaboration between IT and business user to why open-source is important for customers. The time was also spent on security, agility and governance. It means a lot of ground was covered.
We went through a number of topics like this one on a latest panel style webinar with experts. Here is a synopsis
Migrating data from legacy systems:
Treat legacy data just like any other complex data type. HDFS works as an active archive that enables you to cost efficiently store data in any format for as long as you want and access it when required. And with the recent generation of data wrangling & ETL tools, you can change, blend and enrich that legacy data with other data, newer types to have a unique perspective on what is happening across your organization.
Combining insights from existing data warehouse and Hadoop
Generally one of the starter use cases for shifting relational data off a warehouse & into Hadoop solution is active archiving. This is the right thing to take data that might have otherwise gone to archive & keep it accessible for historical analysis. The clear advantage is being able to analyse data for extended time periods which would otherwise be cost practical in traditional data warehouses.
One should take Hadoop as a platform for data transformation as well as discovery, calculate iintesive data that are not suitable for a warehouse. Then think of feeding some of the new data & insights back into the data warehouse to boost its value.
What’s the importance of putting Hadoop in Cloud?
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