Swimming in a lake of Data Processing Challenges: Does the Hadoop data lake make sense?

Data Processing & Analysis has come a long way since inception. If it has streamlined business operations – it also has some challenges that warrant immediate attention. If it has enhanced efficiencies – it also has some aspects which need to be attended on day to day basis. If it has offered enterprises with better customer engagement – it also is the epicenter to some of those complications; which may wash away all the benefits and bright side of Data Processing.

Even with some of those best Customized Data Processing Applications, you as an enterprise are bound to face apprehensions when it comes to final results if the app is not thoroughly in tune with tomorrow.

Data Processing And Analysis

In modern organizations information is managed in isolated silos for niche processes such as maintaining data quality, data integration, information governance, metadata management, (master) data management, content management, B2B data exchange, data base administration and information lifecycle management to name a few.

In the past decade the diversity and volume of digital information has grown at a great speed. Majority of this data is unstructured, and grows six times faster than the structured data. This unstructured data cannot be directly used as information for the above mentioned processes. The unstructured data needs to be converted into structured information that can be efficiently used to drive business growth.

Businesses face several challenges when it comes to efficient data processing and management.

Enlisted are A Few of the Data Processing Challenges That an Organization May Encounter; That Need Immediate Attention:

  • Processing of complex information.
  • Conversion of huge volumes of unstructured data into structured data.
  • Hard to express queries using SQL.
  • Highly recursive algorithms.
  • Requirement of parallelizable algorithms like genome sequencing or geo spatial analysis.
  • Retention of data (huge volumes of data – that require too many cores).
  • Machine learning.
  • Requirement of custom coding to handle job scheduling.
  • Easy adaption and tolerance in fault scenarios.
  • One of the major challenges that an organization might face is that the investments made in order to make the data available in real time might not actually return the benefits as desired, and investment might surpass the amount of benefits.

These are several software tools available that can convert terabytes or even petabytes of unstructured data into structured data. Hadoop is one of most used tool that allows enterprises adopt and easily manage big data challenges. A survey was conducted to analyze the popularity of Apache Hadoop across enterprises as a great data process platform.

The Survey Results Drive The Point Home, As To Why The Platform Is So Popular And Widely Used For Data Processing:

According to the survey it was established that more than 54% of businesses (out of those that were surveyed) are already using or are considering the proposal to use Hadoop, in order to fulfill their data processing requirements for the eternally mounting data volumes.

  • More than 82% of existing Hadoop users reported that the platform enables faster analysis and utilization of computing resources in a better manner. They also said that it enables creation of new products and services and is cost friendly as compared to other platforms.
  • It was also found that nearly 87% users are experimenting and evolving the ways in which they carryout data analytics and manage large volumes of data.
  • 94% users claim that, Apache Hadoop, a decade old data processing platform has enabled them adopt big data by allowing them to manage mounting data volumes easily, a task that was considered un-achievable a decade ago. 88% say they can now analyze data in great detail while 82% say that it is now possible to retain as much data as desired.
  • For 63% of Hadoop users, it is now easier to process unstructured data like events data and logs.
  • More than two thirds, that is nearly 66% of Hadoop users, feel that now it is easier for them to perform advanced data processing & analysis.

Moving on to Hadoop can help manage all these challenges effectively and make the most of big data. So does that mean that Hadoop will solve all your data processing problems?

Hadoop is not a magical tool that will just transform everything, however it will definitely provide a complementary approach to the conventional data warehousing and when implemented well will also help organizations address the most difficult challenges pertaining to data processing and management in the best possible manner.

Terms like data processing, information extraction, big data, data analytics etc. have become so popular today, and are so relevant to the evolving nature of organizations. All these trending terms direct organizations towards one thing, i.e. towards making the available data understandable.

The processed data henceforth provides an insight regarding the business and helps the involved professionals take informed and strategic decisions that drive business growth.

About Author:

Ritesh Sanghani, Director at Hi-Tech BPO leading outsourcing services company serving global businesses for the past 15 years. Ritesh has worked with several international clients and has executed BPO projects of varying scales and complexities.

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