7 ways to get started with Big Data in the Cloud

Over the past 5 years we’ve seen an amazing amount of growth of major disruptive technologies in mobile, information, collaboration, and cloud. Each area has spawned new and amazing capabilities that have transformed our lives, the way we communicate, how we consume our news and access information, and the way we work.

The trade-off of this massive growth in technology in recent years has meant heightened customer demands, with the expectation that products and services will be tracked and delivered faster than ever. While all of this poses considerable integration and infrastructural challenges, the most innovative organizations today will be those that stay on top of the game and align cloud services with other major technologies like Big Data in order to enhance the delivery of their goods and services.

 

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Cloud and Big Data integration is a big topic in industry right now and it’s only going to get bigger. New research shows that much of the cloud’s growth is driven by the use of Big Data. In fact, revenues for the top 50 public cloud providers rose a whopping 47 percent in the fourth quarter of 2013, to $6.2 billion.

The takeaway here is clear – businesses need to shift their already existing Big Data strategy to the cloud to ensure they keep up with the market competition and maximize their ROI. In what follows, we outline 7 strategic ways that business leaders can indeed begin adopting Big Data in the cloud today.

1. Analytics-as-a-Service

Given the growth and popularity of Big Data in recent years, notable markets have opened up to help businesses leverage their data and move huge volumes in and out of the cloud. Analytics as a Service (AaaS) has emerged as a new cloud model for helping provide businesses with faster, scalable ways to integrate, analyze, transform, and visualize various types of structured, semi-structured, and unstructured data in real time. Platforms like Aspera and Qubole offer solutions to the Cloud I/O gridlock. Google’s Cloud Dataflow also provides a native like environment for large-scale data processing on scenarios like ETL, analytics, real-time computation, and process orchestrations.

2. Get onboard with Big Data security analytics

2014 was a major wake-up call in the cyber-security industry. Nothing is safe anymore and individuals and businesses need to do everything possible to keep attentive and secure their assets. Not to mention, we all know that organizations today are bombarded with massive amounts of information. A trending area that has risen to meet this growing need in recent years is known as Big Data security analytics. Big Data security analytics is really about filtering the massive number of events across an increasingly wide variety of data sources (traditional log and audit files, videos, images, social media, email, and sensors, ect). What’s more is that this high-scale processing must take place in a real-time, robust, and secure cloud infrastructure. Big Data and cloud integration is therefore becoming seen as an essential business strategy for meeting the security, technological, and management challenges posed by today’s increasingly brazen and sophisticated forms of cyber-crime.

 

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3. Don’t be afraid to try out Hadoop

The Big Data explosion in recent years has created a vast number of new technologies in the area of data processing, storage, and management. One of the biggest names to appear on the scene is Hadoop, which in simplest terms is a Big Data storage system that takes in large amounts of data from servers and breaks it into smaller, manageable chunks. Hadoop was originally designed for on-premises experimentation and was known for being hard to scale. But with the overall growth of the cloud, Hadoop has adapted well to the new business model. Vendors like Cloudera and Datameer are making Hadoop scalable and much easier to integrate into existing IT infrastructures. Also, more and more cloud services have emerged to make Hadoop data accessible through the well-familiar SQL framework. For example, SQL-on-Hadoop is now becoming a standard protocol for Hadoop-as-a-service.

4. Scale up your Internet of Things strategy

The intersection of Big Data and Internet of Things is raising plenty of IT challenges in data storage, integration, and analytics that only an effective cloud strategy can fully resolve. Big Data is the crux and core of new services generated by the Internet of Things, and much of the innovation in this space will be overseen by third party providers that develop the software channels for customer access to the long trail of devices and sensors connected to the internet. Cloud based service models built on top of these channels will generate Big Data-on-demand to customers and business alike. This new era of Big Data & IoT will spawn all kinds of revenue streams that make data more intelligible, accessible, and actionable from a business standpoint. Now is the time for organizations to start adopting the Internet of Things as a business model for Big Data in the cloud.

5. Adopt NoSQL

NoSQL databases have grown rapidly in popularity over the last several years, and have become known for providing an easily scalable solution for managing Big Data. Ease of deployment and management make NoSQL a good match for today’s virtualized cloud environments; this means less need to worry about hardware and more time to focus on software, strategy, and performance. The top NoSQL databases on the market, such as MongoDB, MarkLogic, Couchbase, CloudDB, and Amazon’s Dynamo DB, have all emerged with robust, scalable, cloud-based solutions that allow for rapid processing of real-time Big Data applications – all for a very affordable cost.

 

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6. Look past MDM to Unified Endpoint Management

Researchers predict that by 2020 over 30 billion objects will wirelessly be connected to the internet. With the massive levels of information processing we’re already seeing, it doesn’t take rocket science to imagine the kinds of data that will become available from these sources . . . and the kinds of resources needed to capture and process and share all of this data. Businesses need to align their Big Data strategy to the realities of Internet of Things. The new framework here is known as Unified Endpoint Management (UEM), which means that instead of device oversight, IT will focus on the ‘endpoint’. This will require businesses to shift away from exclusive mobile device management to a business cloud model that integrates wearables, smartphones and tablets, and IoT-enabled sensors and objects together into a unified, holistic management framework.

7. Leverage your IT/machine-generated data

Most companies focus on the “front-end” data, or the kind of data that will influence customer actions and behaviors in relation to their brands, products, and services. But there’s another side to the data game that you really need to pay attention to if you want to maximize your business efficiency and revenue, and it’s not front-facing at all. This concerns the huge amount of information that gets generated behind the scenes, especially in the form of message queues, sensor data, GPS data, and IT log data (application logs, point of sale logs, server logs, virtual machine logs, web proxy logs, ect). Splunk is a company, for instance, that has redefined the whole value proposition of machine data in recent years by showing how what it calls operational intelligence can dramatically increase revenue, efficiency, and overall ROI.

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