Functions that can benefit from Hadoop

Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits.

With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless. One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable. And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.

Since last decade, companies have been struggling to figure out how to deal with all of the new data that is streaming in all around them. From smartphones to production line sensors, everything is generating data. Apache Hadoop has become the go-to solution for storing and processing big data, and it provides a distinct competitive advantage for organizations across industries in several key functional areas, including security and risk management, marketing optimization, operational intelligence, the enterprise data hub, and the Internet of Things. Let’s take a closer look at these core functions that benefit from Hadoop enterprise solutions.

Data Velocity, Data Security and Risk Management
As Data fraud, velocity and professional security breaches are becoming more frequent and typical and old fashioned security solutions just not up to the challenge of reliably protecting your company assets. Hadoop, can help your organization analyze large amounts and different types of data in real time, secure the speed of threat analysis, and improve ability to assess risk by using great machine learning models.

As an example, Solutionary, a leading Managed Security Service Provider (MSSP), took their security solutions to the next level by implementing the Cisco UCS Common Platform Architecture (CPA) for Big Data with MapR. This joint solution made it possible for Solutionary to perform real-time analysis on big data in order to help protect and defend against sophisticated, and organized adversaries.

Operational Intelligence
If we want to remain competitive, we should look forward for ways to benefit our organization’s productivity and profitability. But even when it seems that our operations have been properly analyzed and optimized, we still can make subtle changes in operating environments in order to realize even greater improvement. By taking a wide variety of granular measurements from sensors, you can track patterns in operations to find new ways to optimize your organization. Cisco provides infrastructure and analytics to support Hadoop distributions such as MapR, and this type of joint solution can give you the ability to evaluate data at the speed your business demands.

Sales and Business Marketing Optimization
Current growth of social media channels which customers are using can make it seem like you’re drowning in data in an effort to better understand your customers. Hadoop can be used to cost-effectively integrate and analyze your disparate data in order to gain richer customer insights, develop personalized real-time customer relationships, and increase revenue.

Specific uses cases in sales and business marketing optimization include data analysis, audio/video advertising optimization, recommendation engine and targeting, social media analysis, and round the clock customer view. As an example many data analytics’ and business intelligence company, provides consumer insights, retail measurement, product libraries, analytics reporting, and consulting services for retail and manufacturing clients

Data Enterprise Hub
Hadoop can be used as a cost-effective data enterprise hub (DEH) to store, transform, cleanse, filter, analyze and gain new value from all kinds of data. Building a successful DEH starts with selecting the right technology in three key areas: infrastructure, a foundational system to drive DEH applications, and the data processing platform. For example, the Cisco Unified Computing System (Cisco UCS) Integrated Infrastructure for Big Data can be used to reliably run your EDH. This solution delivers a highly scalable platform that is proven for enterprise applications, and can be deployed with a Hadoop distribution such as MapR, which is especially well-suited to take advantage of the compute and I/O bandwidth of Cisco UCS.Specific use cases in the EDH area include collecting raw data in a data lake, data refining, big data exploration, data warehouse optimization, and mainframe optimization.

Internet of Things
By 2020, Cisco estimates that there will be about 11 billion connected things shipped annually, and a total of 50 billion things in use. By using an enterprise-grade Hadoop architecture that can access and process the data that comes from these devices, you can create enormous opportunities for your business.

The four main areas of uses cases for the Internet of Things include personal IoT (smartphones, fitness devices), group IoT (family in a smart house, tourist group), community IoT (smart cities and roads), and industrial IoT (smart factories, retailer supply chains). The partnership between Cisco and MapR provides you with an infrastructure that can readily handle the massive amounts of real-time big data from the Internet of Things.

Data Warehouse Optimization and Analytics
Enterprise data warehouses are being re-architected to accommodate increasing data volumes as well as new types of data such as clickstream, logs and social among others. Data warehousing and analytics teams can use the Quick Start Solution built on MapR to reduce overall system cost by performing transformations on Hadoop and get granular and richer analytics across the combined Hadoop and data warehouse solution.