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Indium Software 2019 Predictions: Top 7 Big Data Technologies to Watch out for in 2019

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.

Published Friday, February 01, 2019 9:02 AM by David Marshall
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