With big data analytics, many use cases must be run in real-time for live analysis and this drives IT to change datacentre policies and learn new tools.
The rise in volume of big data is huge, inexorable, and is coming from everywhere, every second of the day. Just about every online service, from social networking sites to video streaming services, use big data and analytics to tailor and improve their services.
All industries, from oil and gas, to medicine and education, are tapping the power of big data and analytics to learn more about their operations, improve services and boost their business performance.
“Companies often encounter roadblocks when implementing big data projects”
Moreover, the continued growth in the number of connected devices, from smartphones and laptops, to IoT devices that live at the Edge, all feed demand growth of big data and analytics.
Companies often encounter roadblocks when implementing big data projects. These can include budget constraints, lack of IT expertise and risk of platform lock-in.
“Processing big data workloads is different than processing typical enterprise application workloads”
Budget constraints and cost are the top reasons why many companies are shying away from deploying big data, according to a study performed by Deloitte. It can be hard to justify investing in new IT infrastructure to process large amounts of data, especially if the business does not yet have an immediate business case.
Processing big data workloads is different than processing typical enterprise application workloads, which means companies may find some skill and knowledge gaps when they embark upon big data projects. Indeed, big data workloads are processed in parallel, instead of sequentially. IT typically prioritises business critical workloads and schedules lower priority jobs in batches at night or when there is excess capacity.
With big data analytics, many use cases must be run in real-time for live analysis and reaction. This forces IT to change datacentre policies and learn new tools to create, manage and monitor these new workloads. There’s also the issue of performance lock-in: companies need to choose the right type of infrastructure to run their applications and data.
“With big data analytics, many use cases must be run in real-time for live analysis and reaction”
Data Analytics offers insights into changes, new issues and situations, and their impact on everything around us, especially in cases like the pandemic. It will help organisations change the way they do things in preparation to deal and adapt to the new conditions successfully.
Organisations in the public and private sector – from government departments and major utilities, to healthcare providers, retailers and financial institutions – increasingly rely on their ability to gather and analyse data effectively. Today, with advances in cloud and AI, organisations can adopt data analytics more easily and thus gain immense value from data.
For example, healthcare providers can spot trends in infection and recovery rates, which can help with establishing more effective policies and procedures at the government level, while retailers can analyse data regarding footfall and buying habits to cater to customers more effectively and increase sales.
In the current scenario, data analytics can benefit most organisations by helping them gain a more complete understanding of the current situation. This in turn gives greater resilience – and the resiliency of services such as electricity and water, networks, finance, healthcare and security, and the distribution of government services, will be testament to solid insights supported by Data Analytics, good decision-making and technology.
As enterprises embrace a multi-cloud strategy and data analytics, cloud service provider business has the opportunity to play a key role in helping simplify the complexity of today’s distributed, cloud-based world. To take advantage of that opportunity, cloud providers need to be able to focus more on high-value cloud services, and less on the complexities of managing infrastructure and custom tools.
“IT must change datacentre policies and learn new tools to create, manage and monitor these new workloads”
VMware is delivering innovations on the Cloud Provider Platform to help Cloud providers improve time to market, decrease cost of operations, and deliver differentiated services—from DRaaS to a developer-ready cloud. As a result of our partnership, access solutions are delivered as a service by AWS VMware’s preferred public cloud partner for all vSphere-based workloads, Azure, Google Cloud, IBM Cloud, Oracle Cloud and over 170 other VMware Cloud Verified partners around the world.
VMware vSphere Big Data Extensions, or BDE, is a feature within vSphere to support Big Data and Apache Hadoop workloads, which enable huge amounts of data to be processed in parallel. BDE provides an integrated set of management tools to help enterprises deploy, run and manage Hadoop on a common virtual infrastructure.
“Roadblock include budget constraints, lack of IT expertise and risk of platform lock-in”
VMware has built BDE to support all major Hadoop distributions including Apache Hadoop, Cloudera, Pivotal, Hortonworks and MapR. Associated Apache Hadoop projects such as Pig, Hive and HBase are also supported. Customers can easily upload supported distributions of their choice and configure Big Data Extensions to deploy their preferred distributions and versions.
Big Data Extensions provides differing levels of feature support depending on the Hadoop distribution and version you configure for use.