Don’t want emerging
trends in big data analytics to get the better of you?Either stay ahead of it
or with it!
Ripples are surfacing on the big data lakes and the cloud is
moving swiftly. These are not some climatic changes but the dawn of big data
technology. You better make beeline for them much like other data engineers and
business brands. These trends in big data analytics will light up your big data
solution.
NoSQL (Not Only Structured Query
Language) isn’t going anywhere
This big data technology doing the rounds in big data
solution has arrived and is here to stay. You better have a grab of it because
it works with SQL as well as SQL- like languages.
Schema- less data representation is its USP. Traditional
database vendors are giving way to NoSQL companies like DataStax, MongoDB,
MarkLogic and Redis Labs.
Apache Spark on the roll
Emerging out of the ecosystem of Hadoop is Apache Spark. It
has become a platform for the big data analytics due to its dramatic data
processing speed. Even reputable firms like Goldman Sachs has Apache Spark as
their lingua franca.
Hadoop comes of age
More and more enterprises are planning to move to or
continue with Hadoop. More investment will go inthe security of enterprises’
system adopting Hadoop such as Apache Sentry project which aims to enforce
role- based authorization of stored data and metadata on the Hadoop
cluster.
Coming together of IoT, big data,
and cloud
Leading enterprises in the cloud and big data solution the
likes of which are Microsoft, Google, and Amazon Web Services are already
investing in the IoT (Internet of Things) services for seamless data flow into
cloud- based analytics.
Deep learning
Deep learning is a set of techniques on machine learning
that works on neural networking. It enables computers to segregate items of
interest in humungous binary and unstructured data. It can easily deduce
relationships without the help of specific models and programming instructions.
For example, colors, objects, and shapes in any video can be
interpreted and appearance of a cat in various images. A deep learning
algorithm even recognized Texas and California to be located in the USA on
examining data on Wikipedia.
Big data lakes
Forget traditional database theory to design any data set
before data entering. Welcome, big data lake (Also called enterprise data hub
and enterprise data lake) that will deposit all data sources into one Hadoop
repository and not beforehand. Work is on for better monitoring access control,
data security, encryption, and tracing data lineage.
Faster big data
Traditional concepts of BI (Business Intelligence) are in
for a haul. Enterprises will be demanding fast data mining capabilities that
will see the adoption of many big data solution such as Cloudera Impala, Actian
Vector, AtScale, and Jethro Data.
Automated data preparation
Data scientists and engineers are continuously looking out
for any big data
technology that can efficiently reduce the time and complexity of data
preparation. Multitudes of data formats and types calls for self- servicing
data preparation.
Lack of talent and knowledge in any big data technology
isn’t an excuse anymore. All the dimensions of big data solution are evolving
and so is talent. The urgent need of the hour is an open ecosystem for data
scientists and data analysts to explore and experiment in the domain of big data
analytics.
This article is very well-written especially the kind of reasearch put in BIg Data and all is truely helpful in carrer prospective. Good Work.
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