Industry executives and experts share their predictions for 2019. Read them in this 11th annual VMblog.com series exclusive.
Contributed by Abhimanyu Sundar, Assistant Manager-Marketing, Indium Software
Top 7 Big Data Technologies to Watch out for in 2019
Big data today is the X factor for all businesses. It is the
market that is growing tremendously and does not look to be slowing down in the
least. By 2020, IDC claims that big data revenue will reach $205 billion. It
isn't just about the revenue, by 2020 there are going to be 440,000 big data
jobs with only 300,000 professionals to grab them.
With the big data industry growing at this rate, so are the
corresponding technologies and tools. Tools have been getting stable releases
over 2018 after their initial launch. These tools went big during the latter
half of 2018 and this trend will continue through 2019. Let's have a look at 7
big data technologies that will help drive better big data solutions with time.
Apache Spark
The most famous and most widely used Apache
project which possesses incredibly fast big data processing speeds. Apache
Spark achieves real-time data streaming with its in-built capabilities. It also
has built-in capabilities for SQL, Machine Learning, Graph Processing and a lot
more.
The key reason for its popularity is that it is optimized to
run in-memory. It can also enable interactive streaming analytics. What this
does is, unlike batch processing this allows you to analyze huge amounts of historic
data with live data in order to help you make real-time decisions. Examples of
this are, fraud analytics, predictive analytics, sentiment
analytics and more.
TensorFlow
Tensorflow is an open source library that allows you to achieve advanced
analytics
at scale. It is predominantly for machine intelligence. New machine learning
models can be experimented with, system level optimizations can be done along
with large scale distributed training and inference.
The reason for Tensorflow's popularity is that prior to it,
there wasn't a single library that could accommodate the length and breadth of
machine learning and possess such huge gleaning insights from it. Tensorflow is
also very well documented and is very readable.
Apache Beam
A project model which combined the name for
big data processes, batch and streaming is what led to Apache's name. This is
one model which can be put to use for both cases.
For simpler understanding, Beam equals Batch
and stream.
Under the Beam model, a data pipeline is required
to be designed only once and after that you are required to choose from
multiple processing frameworks. The flexibility in place with Beam is that when
you want to choose another processing engine or when batch or streaming data
needs to be processes it does not require redesign.
Beam allows you greater agility and
flexibility. The data pipelines can be reused and the right processing engines
can be selected for varied use cases.
Apache Airflow
When we talk about Apache Airflow, with the aim of smart
scheduling and automation of Beam pipelines for process optimization and
project organization, it became the most suited technology.
There are quite a few benefits and features of Airflow. A couple
of features that stand out are that the pipelines are dynamic as their
configuration is via code and the metrics are enabled with visualized graphics
DAG and Task Instances.
Additionally, in case of a failure Airflow can rerun a DAG
instance.
Apache Cassandra
Apache Cassandra has failed node replacement enabled. It can do
this without having to shut anything down replication of data across multiple
nodes happends automatically. A huge advantage of Cassandra is that it is
scalable and is a nimble multi-master database.
The major feature that stands out is that it is a NoSQL
database. The design is such that it has no master slave structure and hence
all nodes will be peers and fault tolerant. With that being said, it is clear
that it is different from a traditional RDBMS and few other NoSQL databases.
Cassandra is easy to scale out for additional computing power
even though it has no application downtime.
Apache Carbon Data
Carbon Data allows for extremely fast analytics to be performed
on big
data platforms
like hadoop and spark as it is an indexed columnar data format. Primarily,
Carbon Data solves the issue of querying analysis for varied use cases. It can
handle multiple querying needs like big scan and small scan, OLAP vs detailed
query and many more.
As the data format is carbon data is so unified, the queries run
extremely fast. This allows us to access through just a single copy of data
with the required computing power.
Docker and Kubernetes
Docker and Kubernetes are container and automated container
management technologies respectively. Both of these technologies specialize in
fast deployment of applications.
Your entire architecture becomes a whole lot more flexible
and portable with the use of technologies like containers. This will allow
increased efficiency in continuous deployment, thereby giving your DevOps
process the edge..
Big data is the becoming an industry of its own and it is the
best place to invest irrespective of which sector you are in. It is helping
industries from finance all the way to healthcare. Adopt the best big data
technology suited for you, make business decisions faster than ever and see
your business evolve.
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About the Author
Abhimanyu has pursued
Visual Communications in his undergrad and has an MBA in Marketing. He loves
playing tennis and is an absolute sports fanatic. He writes articles on big
data and analytics and loves a good discussion on these topics. He is a public speaker
who loves hosting events and partying.