How is Big Data Transforming Managed Infrastructure Services?

Traditional IT environments are not capable of meeting the challenges posed by big data, including – legal, ethical & regulatory, pertaining to security, cost, availability and the most important i.e. of inadequacy of IT infrastructure and database systems. Also, big data far exceeds in quantity that traditional units of storage management (LUNs, volumes, aggregates, etc.) can manage.

Furthermore, it is sourced from ‘n’ number of sources in a variety of formats and so organizations, across all industries, have to manage data that is captured at more detailed levels along with keeping historic information.

Overall, Big Data management is a challenging job. And so the new age IT environment needs to be quick, efficient and robust to store, manage and analyze big data. What organizations really need at this point of time are managed infrastructure services, especially designed to overcome, if not all, at least some of the aforementioned challenges.

1.    Agile & Robust Infrastructure
The amount of data supporting mission-critical applications continues to multiply from gigabytes to petabytes and even more. So to manage data centers, network and desktop support operations and deliver the business value and better responding to changes and conditions that affect the business, an agile & robust architecture needs to be built prioritizing data storage and management.

2.    Technical Expertise
Focus should be on all the key stages of data infrastructure, including – Deployment and configuration, Monitoring, Diagnostics and Reporting & multitude of technology areas: Installation and Configuration of Hadoop clusters, Application Migration on Hadoop and disaster recovery.

3.    Integration & Scalability
“Big” data infrastructures certainly needs to be able to scale and integrate easily to support big data platforms and applications. To allow big data storage systems to expand file counts into the billions without suffering the overhead problems that traditional file systems encounter, object-based storage systems need to be adopted. This should also scale up geographically, enabling large infrastructures to be spread across multiple locations.

4.    Security
To tackle new security considerations that may surface when big data analytic cross-reference data that may not have co-mingled in the past.

5.    Accessibility
To allow multiple users on multiple hosts to access files from many different back-end storage systems in multiple locations, it is recommended that storage infrastructures are used as these include global file systems that help address this issue.
Some of the infrastructural approaches for storing, processing and analyzing big data, include:

Hadoop
Hadoop is an ecosystem of different products and its key components include: HDFS, YARN, Map Reduce, Spark. It is both cost- and time-effective because it’s open source, free and can run off cheap commodity hardware. Additionally, it processes multiple ‘parts’ of the data set concurrently, making it a comparatively effective tool for in-depth analysis.

NoSQL
NoSQL databases are adept at processing dynamic, semi-structured data with low latency, making them better tailored to a Big Data environment.

Massively Parallel Processing (MPP) Technology
MPP technology process humongous data in parallel, for instance, hundreds of different parts of the same programme.

Cloud
Cloud solutions have minimal up-front costs and deliver faster insights.

Google offers Cloud computing products such as BigQuery, specifically designed for the processing and management of Big Data. Similarly, Amazon Web Services has a wide range, included EMR for Hadoop, RDS for MySQL and DynamoDB for NoSQL.

Competitive Advantage

Managed Infrastructure Service solutions provides a road map to take your infrastructure from basic to a dynamic and utility based one. It helps turn your enterprise into a truly adaptive enterprise. However, the key is to understand the impact that these technology designs can have on your analytic needs and determine an appropriate approach. Following are the top competitive advantages:

•    Improved user accountability, excellent business transparency, controlled IT resource consumption, and better regulatory compliance.
•    Enhanced productivity through rapidly provisioned, shared services
•    Reduced IT overheads

Big Data Technology Trends in 2015

Until 2013, it was a topic of debate whether “Big Data Technology” was a niche technology best suited for Internet companies or had the potential to go mainstream.  Things began to change and just in a span of 2 years, industry arrived at a consensus – a clear one. So in 2015, we will see the impact of big data across almost every industry sector. Here’re big data technologies trends for the year 2015:

Big data analytic shall address all security concerns

While earlier it was possible, now corporations collect vast quantities of data that is getting increasingly tedious to handle. This data is so highly complex and voluminous that it has quickly outgrown the capabilities of traditional security software. Not just its managing is difficult, but also it is nearly impossible to analyze it, given the fact that most security platforms used currently don’t talk to each other. Moreover, the processing huge volumes and varieties of data is both cost and time-consuming.

In such times, CISOs have started to look at big data analytics as a powerful complementary solution to traditional security software. It offers some clearly distinct advantages as it enables the security infrastructure to identify and fix “low and slow” advanced-persistent-threat outbreaks that might otherwise be indiscernible when protected by security solutions that don’t connect to or relate with each other. This phenomenon is sure to catch up quickly and expand to other sectors.

Fake technologies will no longer be able to sustain

In 2015, buyers are sure to come to a clear understanding that even when these come from experienced vendors, not everything they try to sell in the name of big data technology could help them. For their own good and that of enterprises, they have to choose solutions that are distinct from traditional business intelligence and data warehouse technologies so that it handles (effectively) the volume or variety of data they have to manage. 2015 shall be the year of technology replacement wherein big data analytics will be emerging as a fundamentally powerful next-generation analytics and the preferred choice of buyers.

Big data shall help IoT reach its full potential

There is a huge amount of data i.e. generated by the household and industrial devices, and wearable et al. and it comprise today’s Internet of things (IoT). As yet, hundreds of device makers building the IoT have not come together. So this data, despite of growing exponentially, is lying unused with the device makers.

However, if big data technology is put to use, it can bring this data together so that its true value is realized. It will also involve understanding how devices and services are working, communication flow that could constitute security threats, and intertwining interdependent & dependent failure modes.

This is the first vital first step that should be followed by a variety of analytical approaches. In 2015, device makers will have to turn to big data analytics to help the emerging Internet of things market reach its full potential— or they will be left out if they don’t.

Companies, both big & small, will be moving towards big data analytics

A few years ago, big data tools were available only to corporate biggies that could invest in proprietary and largely inflexible infrastructure. From 2015, companies of all sizes will increasingly adopt it to identify and quickly adapt to opportunities and challenges, if they want to remain competitive, as they rethink the way their employees collaborate and use data. It is expected as companies will be increasingly adopting a data centric approach to set strategy, encourage collaboration among colleagues, and interact with customers.

Big Data vendor consolidation will happen
In 2015, some of these small players in the area of big data, with their niche products, will not be able to meet their customers’ overall needs. So it is expected that many startups will either fail or become acquisition targets as larger vendors who could offer end-to-end solutions would eye them.

Conclusion

These are a number of proof points that go beyond elusive claims to prove that in 2015 the full potential of big data analytics will be unleashed empowering business analysts with new capabilities. So this year shall mark the end of testing and early-stage roll outs and the rise of a ripe big data landscape where we’ll see enterprises making bigger strategic bets and asking for end-to-end solutions built to handle humongous amounts of incongruent data without calling for data scientists. It’s shakeout time.