19 January 2018
Having established itself as a key part of corporate Big Data programs, Hadoop continues to grow in importance. Unsurprisingly, Hadoop and Big Data skills are in high demand as organisations try to extract greater value from their disparate data sets to drive digital transformation.
The Future is automated
The Hadoop skills shortage is forcing data scientists to develop new techniques to work with large data sets and to deliver meaningful insights. As they wait for the next generation of analysts to complete Hadoop training, many are investigating how Machine Learning (ML) techniques can help reduce their personal workloads.
On a more basic level, Apache Oozie can be deployed to create workflow schedules, adding another layer of automation for processing Big Data. In this way, resource intensive computing can be carried out when it will have the least impact on other operational processes.
Those organisations committed to digital transformation and data-driven business processes are already making gains using machine learning and automation.
Advanced Hadoop deployments
Database clustering has been employed for many years to improve data availability and to increase the resilience of the corporate computing environment. Similarly, technical staff involved with Big Data deployments will need training in how to build a fault-tolerant Hadoop server cluster.
Industry-standard Hadoop deployments isolate single points of failure to reduce the risk of outages, but they can also be configured to support manual failover. Again, data scientists and engineers will need training to understand when manual intervention is required, and how it is performed.
The falling cost of storage means that businesses are reluctant to dispose of data, accelerating the growth of the corporate data store. A high-performing Hadoop deployment uses MapReduce to distribute queries across the data cluster to improve performance for instance. The system will also need regular maintenance and debugging using the built-in JVM metrics system.
Opening Big Data to other users
Big Data insights should be available to the whole business – but not every user will have the skills and knowledge required to query Hadoop directly. To overcome this shortfall, many organisations are choosing to extend their Hadoop environment using tools like Hive to simplify querying, making it possible for developers with standard SQL training to begin using data and insights from Hadoop clusters.
In the era of data driven-decision making and digital transformation however, it makes sense to provide all in-house developers with Big Data courses to ensure they understand basic principles, and how the information can be better used by your business. Perhaps more importantly still, providing SQL-like access to Hadoop data stores will help overcome skills shortages until more developers can be trained in Hadoop, MapReduce and Big Data.
When applied correctly, Big Data has the potential to transform both internal and external business operations. But in order to better serve customers, organisations must first ensure they have a properly trained and qualified workforce – or their Hadoop deployment will never realise its full potential.