Defining Big Data Analytics for the Cloud
Big data is the catch-all term used to describe gathering, analyzing, and storing massive amounts of digital information to improve operations. Big data analytics is the process of evaluating that digital information into useful business intelligence.
Utilizing this data, companies can provide actionable information that can be used in real-time to improve business operations, optimize applications for the cloud, and more. In today’s data-driven landscape, organizations need to understand how to leverage big data analytics in the cloud, how it impacts leading industries, and other core data integration concepts.
Big Data Analytics and Business Intelligence
They say the world used to run on oil, more recently it ran on data, and today it runs on fast data. The ability to quickly, reliably process and act upon the information produced by the myriad interactions taking place in a network environment is often the difference between obstacles and opportunities.
With the right big data analytics approach and supportive tools, the business intelligence produced presents time and cost-saving insights into:
- Network traffic patterns
- Customer behavior and sales optimization
- Security events, both external and your environment
- Compliance information
- Errors and anomalies impacting performance data
- And more
Collecting and utilizing this data takes planning and support, but the value it presents organizations far outweigh the alternative risk -- missing critical information and opportunity.
Big Data Analytics and the Cloud
As we think of it today, the term cloud actually has two uses: one is as a dynamic, geographically dispersed storage system for massive amounts of data, and the other is the medium through which data is computed.
Analyzing both streams of cloud data and blending that information into business intelligence takes a comprehensive solutions approach, one customized to the unique demands of different industries.
Big Data Analytics in Your Industry
Any industry that depends on the efficient use and management of data has already been transformed by big data analytics. Here are just a few examples:
Big Data Analytics for Healthcare
Technology drives healthcare breakthroughs, and analysis of cloud data is streamlining the way our health histories are accessed by caregivers.
Additionally, by ingesting cloud data from countless sources—and the Internet of Things (IoT)—big data analytics can help spot illness outbreaks, isolate risk factors, and proactively improve and protect the health of a growing global population.
Big Data Analytics for Financial Services
Every day, billions of dollars move back and forth across global markets, and each transaction requires precision, speed, and maximum security. Analysts constantly mine petabytes of data for patterns and changes to establish predictions that will combat money laundering, and more, to decide the fate of fortunes and impact the global economy.
Big Data Analytics for Transportation
Trains, planes, automobiles … today, almost all of them are part of the IoT, gathering and producing big data through the cloud at a historically unprecedented rate. Through big data analytics, engineers can now automatically predict arrival time, update delay schedules, and even help prevent traffic jams by sharing data with travelers.
Big Data Analytics for Marketing
How is it that browsing for an item on Amazon.com results in the same item appearing routinely in a user’s Facebook feed? By applying data earned via real-time customer click path analysis, produced in routine search and shopping activity.
Algorithms crunch huge amounts of data, cross-referencing demographics provided through social media, web surfing patterns gathered in browsers, and site visits recorded in DNS and other logs. The analyzed data is then used to produce highly personalized advertisements to customers already identified as interested, greatly increasing the likelihood of a return visit and sale.
In these and other ways, big data analytics in the cloud has forever changed the way products are marketed and sold.
Big Data Analytics for Technology
Though it may seem redundant to say big data tech is changing technology, it is doing so in very immediate and physical ways.
Breakthroughs in multi-processing and the power to store, process, and move vast amounts of data is slowly phasing out physical network infrastructures like server banks, switches, load balancers, and more. Big data analytics now plays a central role in building, securing, and optimizing the virtual layers that will affect applications and traffic, providing both increased training challenges and limitless opportunities to secure and scale network capabilities.
Organizations’ growing ability to leverage business big data analytics will evolve as quickly as technology itself, and almost every industry will need to adapt or fall behind in the race for modern market share.
Big Data Tools and Software
Because of the overwhelming volume, velocity, and variety of modern data, successful management of that data depends on the, right process to be established, and right people to be enabled with right technologies for gathering and analyzing information.
One of the first and most popular big data ecosystem is Apache Hadoop, an open-source software solution designed for working with big data.
The tools in Hadoop help distribute the processing load required to work with massive data sets across a few—or a few hundred thousand—separate computing nodes. Instead of moving a mountain of data to a tiny processing site, Hadoop does the reverse, vastly speeding the rate at which information sets can be processed.
As with most technologies, the growth of the cloud is driving evolutions in Hadoop. Popular cloud platforms have built-in management for big data frameworks like Hadoop. Some of these include:
- Amazon EMR — Amazon leverages Hadoop and other open source technologies to lower operational costs in the cloud.
- HDinsight — Microsoft Azure utilizes HDInsight to power ETL, data warehousing, machine learning, and more on its managed cloud.
Some companies even provide serverless, cloud-native data platform-as-a-service options for leveraging Apache Spark and other open source solutions. Google DataProc and Cloudera Altus offer easy and affordable options for managing and scaling Hadoop instances, greatly simplifying machine learning and analytics.
Another essential, open-source tool for analytics is Apache Spark, a super-speed, in-memory engine for large scale data processing. Spark can be used with other ubiquitous technologies like NoSQL databases, which are non-relational, open-sourced, and greatly scalable when in cloud. The platforms, and technologies like them, help decision makers gather, process, and store big data and refine it into business big data analytics.
Big Data Analytics vs Data Science
Big data analytics evaluates data that has already been produced and turns it into business intelligence that can be used in near real-time. Is this a different practice than data science?
Yes. Data science, as the name implies, follows scientific methods to chart trends and anomalies, using information to extrapolate likely future arcs. If analysts report the who, what, when, and other Ws of today’s data, scientists use it to forecast what will happen tomorrow.
Both disciplines rely on the tools and technologies that enable big data analytics, and both process up to petabytes of information to produce intelligence.
Is Your Business Ready for Big Data Analytics?
From inanimate objects like household appliances, to public transit services, to the way we shop for luxury items, big data is rapidly changing modern life and business. Business big data analytics in the cloud can be an organization’s most powerful tool for improving operations and gaining the intelligence advantage in any industry.
Modern industry giants like Air France-KLM, Groupon, and many more are using Talend’s comprehensive, open source-based solutions to master their data challenges. Learn more about big data, or download a free trial platform that puts the power of big data integration in your hands without all the coding.
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