Four steps to big data project success Posted on : Sep 24 - 2017

Big data has the potential to solve big problems and create transformational business benefits. While a whole ecosystem of tools have sprung up around Hadoop to handle and analyse data, many are specialised to just one part of a larger process.

When organisations can effectively leverage Hadoop, the potential IT and business benefits can be particularly large. But as with any technology that is just beginning to mature, barriers to entry are high and successfully implementing Hadoop for value-added analytics can be challenging.

In order to make the most of Hadoop, organisations need to step back and take an end-to-end view of their analytic data pipelines.

1: Ensure a flexible and scalable approach to data ingestion

The first step in an enterprise data pipeline involves the source systems and raw data that will ultimately be ingested, blended and analysed. The most important big data insights tend to come from combinations of diverse data that may initially be isolated in silos across the organisation.

Therefore a key need in Hadoop data and analytics projects is the ability to tap into a variety of different data sources, types and formats.

Businesses need to prepare not only for the data they want to integrate with Hadoop today, but also data that will need to be handled for potential additional use cases in the future. A vital part of this is planning to reduce manual effort, and establishing a dynamic and reusable data ingestion workflow.

2: Drive data processing and blending at scale

Once enterprises are able to successfully pull a variety of data into Hadoop in a flexible and scalable fashion, the next step involves processing, transforming and blending that data at scale on the Hadoop cluster. View More