What is big data analytics?

So, do you know definition of big data analytics? you don't know right, today, I will explain about big data analytics for you understand by way easily. Let's do it now.

The fist, Big data defined

In general the term refers to sets of data that are so large in volume and so complex that traditional data processing software products are not capable of capturing, managing, and processing the data within a reasonable amount of time.
A clear big data definition can be difficult to pin down because big data can cover a multitude of use cases. These big data sets can include structured, unstructured, and semistructured data, each of which can be mined for insights.

The concept of big data comes with a set of related components that enable organizations to put the data to practical use and solve a number of business problems. These include the IT infrastructure needed to support big data technologies, the analytics applied to the data; the big data platforms needed for projects, related skill sets, and the actual use cases that make sense for big data.

So... What is big data analytics?

Data analytics involves examining data sets to gain insights or draw conclusions about what they contain, such as trends and predictions about future activity. Moreover, what really delivers value from all the big data organizations are gathering is the analytics applied to the data.

By applying analytics to big data, companies can see benefits such as increased sales, improved customer service, greater efficiency, and an overall boost in competitiveness.
Data analytics can include exploratory data analysis (to identify patterns and relationships in data) and confirmatory data analysis (applying statistical techniques to find out whether an assumption about a particular data set is true.

IT infrastructure to support big data

At a high level, these include storage systems and servers designed for big data, data management and integration software, business intelligence and data analytics software, and big data applications.

For the concept of big data to work, organizations need to have the infrastructure in place to gather and house the data, provide access to it, and secure the information while it’s in storage and in transit.

Big data technologies

There several technologies specific to big data that your IT infrastructure should support.

Top 1: Hadoop ecosystem

The Hadoop software library is a framework that enables the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop is one of the technologies most closely associated with big data. The Apache Hadoop project develops open source software for scalable, distributed computing.

Top 2: Apache Spark

Part of the Hadoop ecosystem, Apache Spark is an open source cluster-computing framework that serves as an engine for processing big data within Hadoop. Spark has become one of the key big data distributed processing frameworks, and can be deployed in a variety of ways.

Top 3: Data lakes

Data lakes are designed to make it easier for users to access vast amounts of data when the need arises. Data lakes are storage repositories that hold extremely large volumes of raw data in its native format until the data is needed by business users.