Key Technologies for Big Data Analytics

For most businesses today, data management has shifted from an important competency to a critical differentiator and determines industry winners and has-beens.

Government bodies and Fortune 1000 companies benefit from the innovations of web developers. These organizations are reevaluating existing strategies and defining new initiatives to transform their businesses using “big data”. These trends indicate that big data is not a single technology or initiative. It is, rather, a revolution across many areas of technology and business.

Big data plays an important role in many different industries across the world. To make the most out of it, you can consider ensure your employees get trained in big data. When they learn about proper management of big data, your business will become productive and will improve in efficiency.

Big Data refers to a collection of data sets, so massive and complex that it becomes difficult to process with traditional applications/tools. Data scienceis an interdisciplinary field that tells us about where the information has come from, what it represents and how business can turn it into a valuable resource.

Now, let’s examine key technologies that you can use to promote your business.

NoSQL databases

Earlier, databases stored structured data in tables with rows and columns. These databases are also called SQL databases and relational databases. Nevertheless, a need for databases that can store data in any format arose. NoSQL databases are database technologies that store unstructured data.

Document: As implied by its name, it stores information in a document. The exact definition of a document depends on the database.
Graph: Databases that store information in nodes are called Graph databases, which can further be connected to other nodes. Graph databases make connecting databases and highly complex queries easier and faster.
Columnar database: This database is most similar to relational databases. This database is not row-oriented to store data, but stores data in a column-oriented manner. As compared to traditional databases, it performs operations faster.
Key document Value database: The simplified version of the more robust document database is key document value database. Here, each entry is a simple key-value pair. It is simple and easy to implement.

Knowledge discovery tools

Businesses can mine big data—structured and unstructured—stored in multiple sources with the help of knowledge discovery tools. The sources can be different file systems, DBMS (database management systems), APIs (application programming interfaces) or similar platforms. Search and knowledge discovery tools allow businesses to isolate and use the information to their benefit.

In-memory data fabric

In-Memory data fabrics explain the natural evolution of in-memory computing. Data fabrics have a broad approach to in-memory computing, integrating the whole set of in-memory computing use cases into a collection of clear, independent components. A data grid is one of the components that data fabrics provide. In addition to the data grid functionality, the in-memory data fabric includes a CEP (complex event processing) streaming, an in-memory file system, a compute grid and more.

One of the important benefits of an in-memory data fabric is that all of the in-memory components can be used independently while being integrated with each other.

In Apache Ignite, for example, a compute grid can load-balance and schedule computations within a cluster, but if used together with a data grid, the compute grid also routes all the computations responsible for data processing to the cluster members responsible for data caching.

The same applies to streaming and CEP – when working with streamed data, all the processing takes place on the cluster members responsible for caching that data also.

The most common features of in-memory data fabrics are:

Data Grid
Compute Grid
Service Grid
Streaming & CEP
Distributed File System
In-Memory Database
An Apache Incubator project, Apache Ignite, is the only in-memory data fabric that is available in the open source space.

Data Integration

One of the operational challenges for many businesses dealing with big data is to process terabytes or petabytes of data in a useful way for customer deliverables. With data integration tools, companies can streamline data across various big data solutions such as Apache Hive, Amazon EMR, Apache Pig, Hadoop, Apache Spark, MongoDB, Couchbase, and MapReduce.

Spark

Apache Spark is an open-source processing engine. It is built around speed, user ease, and great analytics. Mainly, it is a parallel data processing framework that can even work with Apache Hadoop, leading to its faster development. It also leads to an ease of streaming along with interactive analysis on your data.

Hadoop

Hadoop is an open-source software framework that stores data and runs applications on clusters of commodity hardware. It provides massive space to store any type of data (structured or unstructured) and has enormous processing power with the ability to handle limitless concurrent tasks virtually. Possessing talent that is well equipped with big data Hadoop training gives any organization, a good head start.

You need to understand the terms used in the definition to understand Hadoop.

Open-source software- Open source is any program where the source code is available for ungated use and even users can modify it to suit their requirements. Usually, an open source project is developed as a public collaboration and is available for free. As Hadoop is an open-source platform, everyone has access to it.

Framework-It includes everything from programs to connections required to develop and run software applications.

Massive storage-The Hadoop framework divides big data into entities, which are stored on clusters of commodity hardware.

Processing power- Hadoop processes bulk data concurrently using various low-cost computers to provide faster results

Data Quality

Data is the most important element of big data processing. Data quality software uses parallel processing to conduct cleansing and enrichment of large data sets to ensure reliable and consistent outputs from big data processing.

Big Data improves operational efficiency, and businesses are able to make informed decisions based on the latest information, and it has become the mainstream norm.

There are several ways to make use of big data to improve your business. It is important for professionals and businesses to remember that while big data is one field, its scope is huge! Make the best use of big data and, who knows? Maybe your company could just become the next Google or Facebook.