Digital footprints are inevitable in today’s
world. Data is generated almost everywhere including bio-medical data in life
sciences, geospatial data in logistics, sensory data from IoT devices, and public
data in social networking sites. Big Data is a blanket term for datasets that
are so large in volume, move in and out with a velocity that makes it difficult
to analyze, and are collected from varying sources as structured, unstructured
and semi structured data. Thus, it is not adequate to acquire, store and process
them using traditional data processing software.

Nevertheless, organizations want to explore
these massive datasets and extract value from them as this acumen will help
facilitate their productivity, identify customer behaviour and opinions, and promote
best practices. This is where a Big Data Platform comes into picture and helps
to collect, store, manage and analyze big data. Good characteristics is crucial
for a platform as it enables efficient storage and processing of huge datasets
and outputs analyzed data for consumption via business intelligence and data
visualization tools, and thus promotes exploration of datasets.

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Ideally, a productive big data
platform addresses the 3V’s of big data– Volume, Velocity and Variety and
enables integration with information architecture including databases, data
warehouses and BI applications to unveil purposeful information; the platform offers
scalability such that the solution is scalable even up to petabytes. The
solution is extensible to hold newer technologies and fault-tolerant to avoid
failures, especially in real-time analytics.

Apache Hadoop, an open-source
framework almost synonymous with big data, allows large-scale distributed storage
and computing of enormous datasets across clusters of commodity computers in a
cost-effective manner. Hadoop’s clustered computing delivers higher storage
& computational speed compared to classical data processing systems and is
scalable by adding machines to the network. It provides Batch Processing in
which the entire datasets being processed are usually bounded, persistent and
large. MapReduce, the parallel processing engine for Hadoop, divides the
datasets into chunks and distributes them across the available nodes in which
the computation power of the nodes is utilized for the processing of these data
subsets. Reshuffling is done based on intermediate results and the results of individual
nodes are assembled to get the final result. The Hadoop Distributed File System
(HDFS) empowers handling of any type of data by providing reliable data storage
across all the nodes in a cluster. It allows the storage of unstructured/semi
structured data across the cluster, facilitates processing of data at the
petabyte scale, stores intermediary processing results, and also persists the
finally computed results.

While batch processing is taken care
of by Hadoop, Stream Processing is event-based and real time i.e., individual
data items are computed continuously even as new data comes in. Apache Spark is
a lightning-fast framework that supports advanced analytics such as SQL
queries, machine learning, streaming data and graph algorithms, which makes it competent
for real time stream processing. Spark’s in-memory cluster computing offers a
computation speed that is much faster than that of MapReduce and helps in
building analytical datasets. It is also a better fit for machine learning than
Hadoop due to its dedicated libraries – MLib, speed and stream processing
abilities. Since Spark doesn’t have its own distributed file system like
Hadoop’s HDFS, it is often installed on top of Hadoop making use of the data
stored in HDFS along with its stream processing capabilities.

However, it has become increasingly
difficult for organizations to meet the requirements on infrastructure for big
data. In order to meet this demand, vendors provide Hadoop-as-a-Service (HDaaS)
on a pay-per-use basis. Organizations have the choice of choosing a commercial
distribution among in-house and cloud-based distributions. A cloud-only
platform acts as a substitute for in-house Hadoop and empowers organizations to
store, manage, analyze and visualize big data on the cloud without having to procure,
maintain and scale any infrastructure. It is up to an organization to decide
which services it requires based upon its individual needs. For instance, an organization
that prefers to gain business insights from its structured and unstructured
data would benefit by installing Spark on top of Hadoop’s storage. Some of the
leading HDaaS market players include Amazon Elastic Map Reduce, Cloudera, Hortonworks,
MapR, Google and Microsoft amongst others.

The overall
purpose of storing and managing a myriad of huge datasets is to explore them and
apply them to Business Intelligence. Various techniques like optimization,
pattern recognition, statistical analysis, machine learning and data visualization
need to be performed in order to present the final output to decision makers. Firstly,
data needs to be cleaned to eliminate incomplete, incorrect and duplicate
records. Data Cleansing tools such as OpenRefine, Trifacta Wrangler help in
achieving this. After cleaning, the data will be ready for data mining through
which useful trends and patterns could be identified. Popular choices for data mining
include Sisense and RapidMiner, which will help in transforming patterns into
effective information. Using these obtained patterns, data analysis can be
implemented to find out answers for desired questions that concern with the
development of business. The Hadoop ecosystem contains data analysis tools like
Hive, Pig and HBase. The final step is to expose these insights to business
executives via Data Visualization. Visualization techniques include line
graphs, network diagrams, word clouds and correlation matrices. It is a vital
phase since any misinterpretation of data could easily lead to faulty decision
making, which could adversely affect the business. Tableau and Qlik Sense are among
the well-known visualization tools in the market to showcase comprehensible
data by illustrating key relationships and unknown correlations for smarter
decisions.

Modern big
data platforms are well-equipped to provide the necessary solutions using which
businesses can leverage their operations. An online bus ticketing agency in
India – Redbus, uses Google BigQuery for analyzing its booking and inventory
data. Google’s cloud platform helps RedBus gain valuable customer insights such
as bus booking patterns without having to maintain an on-premise
infrastructure. Even government forces such as the Indian Army, border security
force and police departments across India have benefited from data analytics. Big
data has the potential to bring tremendous opportunities and big data platforms
help in achieving that goal.