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Big Data: A new competitive advantage

Big Data: Customer Insights and Profitability

Consumers Reap Benefits: Big data is not just companies and organizations that stand to gain from the value that can create. Consumers can also reap highly significant benefits. The use of Big Data is becoming a crucial way for leading companies to outperform their peers. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value. Big Data will help to create new growth opportunities and entirely new categories of companies, such as those that aggregate and analyze industry data. Many of these will be companies that sit in the middle of large information flows where data about products and services, buyers and suppliers, consumer preferences and intent can be captured and analyzed. Forward-thinking leaders across sectors should begin aggressively to build their organizations’ Big Data capabilities.

  • Big Data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services.
  • Sophisticated analytics can substantially improve decision-making, minimize risks, and unearth valuable insights that would otherwise remain hidden.
  • Big Data can be used to develop the next generation of products and services.

Employing big data technologies not to replace existing BI & DW architectures, but to augment them. Our solution enables business customers and enterprise analysts to easily visualize, explore, and report on data across multiple dimensions without depending on Technical experts. Call us today.

Big Data: Moving Computation to Storage

Hadoop is a highly scalable analytics platform for processing large volumes of structured and unstructured data. By large scale, we mean multiple petabytes of data spread across hundreds or thousands of physical storage servers or nodes.

Hadoop was designed to move compute closer to data and storage and to make use of massive scale-out capabilities and execute the distributed processing with the minimum latency possible. This is achieved by executing Map processing on the node that stores the data, a concept known as data locality.

Our solution supports the widest spectrum of big data sources, taking advantage of the specific and unique capabilities of each Hadoop Framework tools and technology.

Our solution enables business customers and enterprise analysts to easily visualize, explore, and report on data across multiple dimensions without depending on Technical experts.

Big Data - Hadoop Eco-System:
Hadoop - Map Reduce Design Patterns

Design patterns: Reusable solutions to problems. It makes the intent of code easier to understand and be able to reuse the code.

Pattern categories: Summarization, Filtering, Data organization, Joins, Meta patterns and Input & output.

Hadoop Ingestion and Testing Challenges

Data ingestion : Apache Flume is a reliable service for efficiently transferring large quantities of data into HDFS. An important consideration when designing a Flume flow is the type of channel to use. There are two types: file channel and memory channel.

The file channel stores all events on disk so if the OS crashes or reboots, events that were not successfully transferred to the next node will not be lost. The memory channel buffers events in memory, so it is faster but less reliable should a failure occur.

Map Reduce Unit testing with MR jobs

MRUnit is a tool that was developed by Cloudera and released back to the Apache Hadoop project. It can be used to unit-test map and reduce functions.The local job runner lets you run Hadoop on a local machine, in one JVM, making MR jobs a little easier to debug in the case of a job failing.

Integration testing - Running MR jobs on a QA cluster

QA cluster composed of at least a few machines.By running the MR jobs on a QA cluster, Testing team will be testing all aspects of both MR job and its integration with Hadoop.

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According to analyst firm Gartner, 75% of businesses are wasting 14% of revenue due to poor data quality. With the rise of the Web, and mobile and social computing, the volume of data generated daily around the world has exploded. As the number of communications and sensing devices being deployed annually accelerates to create the encompassing “Internet of Things,” the volumes of data continue to rise exponentially.

Big Data is often characterized as involving the so-called “Three Vs”: Volume, Velocity and Variety. Our Hadoop solution and services enables business customers and enterprise analysts to easily visualize, explore and report on data across multiple dimensions without depending on technical experts.

Itechmatics can deliver customer insight strategies, predictive frameworks and opportunity machine learning models.

The Big Data platform is a major contributing factor to the majority of marketing investment decisions in modern enterprise companies and creates new sales opportunities based on numerous customer insights--including customer online behavior and social media activities. Any business can now harness the tremendous power of Big Data to increase revenue growth as well as profitability.

Hadoop Monitoring

The Basics: Nagios, Ganglia, Ambari/Cloudera Manager, Hue.

Admins need to understand the principles behind Hadoop and learn about their tool set: fsck, dfsadmin, etc.

• Monitor the hardware usage for your work load – Disk I/O, network I/O, CPU and memory usage.

– Use this information when expanding cluster capacity.

• Monitor the usage with Hadoop metrics.

- JVM metrics: GC times, memory used, thread Status.

– RPC metrics: especially latency to track slowdowns.

– HDFS metrics: Used storage, # of files & blocks, cluster load, file system operations.

– Job Metrics: Slot utilization and Job status.

- Tweak configurations during upgrades & maintenance windows on an ongoing basis.

- Establish regular performance tests – Use Oozie to run standard test like TeraSort, TestDFSIO, HiBench.

Hadoop Security Architecture

As Hadoop enters the IT mainstream and starts getting used in a major way in production environments, the same security concerns that apply to IT systems such as databases will be applicable to Hadoop as well.

Three main aspects of securing information — aspects that apply to Hadoop as they would to any other IT system:

Perimeter management

Access control