Basic Big Data analytics and spa
By: Ganesh Singh
Tags: Big Data Analytics, Spark

What can Big Data analytics do? (What is Big Data Analytics and Why is it Important? ) or (How it can be used and why? )

It’s not the amount of data that is important. What matters the most is how well businesses manage the data and what they do with that data. To find valuable information businesses are doing Big data analytics and make crucial decisions based on information which is changing their business. Giving edge against their competitive.

If you are still wondering why to incorporate Big Data analytics in your business; here are a few reasons why:

Big Data can determine user behaviour Big Data is capable of conducting predictive analysis Big Data helps in deriving insights to frame business strategies Big Data can conduct processes so as to extract value from data sets

Keeping all the above-mentioned factors in mind, organisations and businesses are looking forward to Big Data analytics. Also, by incorporating Big Data, they can come up with new initiatives and reformed strategies which have the capability to transform any business.

In addition to that, the applications of Big Data are not just limited to software or application development. Big Data analytics used in every sector.

Since the amount of Big Data keeps on increasing exponentially, it is not easy to analyse it. But, proper management and study of that data can help you to make an informed decision for your business.

Big data analytics is booming all round us. Big giant companies google, Facebook are paying big amount of money their big data analytics. Using big data analytics they are figuring out important information.

Here we are going to learn Spark big data analytics tool and it’s very important to learn that if you are planning to switch your career in Big data. Coming further we will have discussion why it’s important and in trending.

What Sets Spark Apart?(Why is Spark )

There are many reasons to choose Spark, but the following are key:

Simplicity:

Spark’s capabilities are accessible via a set of rich APIs,

all designed specifically for interacting quickly and easily with data at scale.

These APIs are well-documented and structured in a way that makes it straightforward for data scientists and application developers to quickly put Spark to work.

Extract, transform, and load (ETL) processes are often used to pull data from different systems, clean and standardize it, and then load it into a separate system for analysis.

Spark (and Hadoop) are increasingly being used to reduce the cost and time required for this ETL process.

What Is Apache Spark?

Spark is a general-purpose distributed data processing engine that is suitable for use in a wide range of circumstances.

(Distributed processing is a setup in which multiple individual central processing units (CPU) work on the same programs, functions or systems to provide more capability for a computer or other device.)

On top of the Spark core data processing engine, there are libraries for SQL, machine learning, graph computation, and stream processing, which can be used together in an application. Based on what kind of functionality you want to use

Programming languages supported by Spark include: Java, Python, Scala, and R.

What Does Spark Do?

Spark is capable of handling several petabytes of data at a time, distributed across a cluster of thousands of cooperating physical or virtual servers.

It has an extensive set of developer libraries and APIs and supports languages such as Java, Python, R, and Scala;

Batch processing — its flexibility makes it well-suited for a range of use cases. Spark is often used with distributed data stores such as MapR XD, Hadoop’s HDFS, and Amazon’s S3, with popular NoSQL databases such as MapR Database, Apache HBase, Apache Cassandra, and MongoDB, and with distributed messaging stores such as MapR Event Store and Apache Kafka.

Stream processing: From log files to sensor data, application developers are increasingly having to cope with “streams” of data.

Machine learning: As data volumes grow, machine learning approaches become more feasible and increasingly accurate. Software can be trained to identify and act upon triggers within well-understood data sets before applying the same solutions to new and unknown data.