A decade ago, data around the world began to grow rapidly, opening up opportunities to improve customer experience. At the moment, the concept of Big Data has already become firmly established all over the world.
The lion’s share of this data is collected over the Internet, and the rest is collected using network-capable devices. Another important growth factor is the growing number of online virtual offices.
Companies are interested in both hiring Big Data experts and people who are well versed in analytics tools.
Team leaders are looking for employees with competent skills and demonstrating talent and cognitive abilities that would be a valuable asset to fulfill the company’s niche responsibilities. A lot of what was valuable before has lost its value and vice versa. In any case, let’s take a closer look at what Big Data is.
We are non-stop generating gigantic amounts of data through social media, public transport and online shopping. Their volumes captivate the spirit. We upload 95 million images and videos, 340 million tweets and 1 billion documents every day. In total, we produce 2.5 quintillion bytes per day, do any of you remember how many zeros are? That is why they are called Big Data.
Although data has penetrated almost every niche and serves as one of the main driving forces behind the success of modern companies, the term Big Data has not been used very long ago.
By the way, Google Trends has been demonstrating user interest in this phrase since 2011. Today this term is in active rotation, being one of the most commonly used in the corporate environment.
This term has no clear boundaries and definitions, someone thinks that Big Data starts with 100 GB (500 GB, 1 TB, whatever), someone with data that cannot be processed in Excel or with data that cannot be processed on one computer , and someone counts any data among them.
Therefore, there is an alternative opinion that Big Data does not exist, it is a fictional character that marketers use to force companies to spend money.
So what is this concept? Essentially, Big Data is a series of approaches, tools, and techniques used to process structured and unstructured data of enormous volumes and considerable diversity to produce human-perceived results that prove to be effective in an environment of continuous growth.
Big Data is an alternative to traditional database management systems and Business Intelligence solutions.
Thus, big data does not refer to a specific amount of data, or even to the data itself. Instead, the term refers to data processing techniques that enable distributed information processing.
These methods can be applied to both huge datasets (for example, the content of all pages on the Internet) and small ones (for example, the content of this article).
Big data is essential to global business as more data leads to more accurate analytics, which in turn enables better decision making, improved operational efficiency and lower costs.
Table of Contents
Three big whales of Big Data
When we talk about big data, we cannot fail to mention three key properties: volume, speed and variety. These three vectors allow us to understand how big data compares favorably with old-school data management.
Volume
The amount of data should be sufficient. You will have to process huge amounts of unstructured data with low density. And the size of the data is the most important metric when determining the possible recoverable value, since the more data, the more accurately you can get the result from it. Clicks streams, syslogs, and streaming systems usually generate large amounts of data.
Diversity
Long gone are the days when data was collected from one place and returned in a single format. Data today comes in all shapes and sizes, including video, text, sound, graphics, and even gouging on paper. Thus, big data provides opportunities to leverage new and existing data and develop new ways to collect data in the future.
Speed
The usual speed means how quickly data gets to us from various systems for further interaction with them. Some data may appear in real time, and some comes in batches. Since most platforms process incoming data at different speeds, it is important not to speed up the decision-making process without having all the information you need.
The best tools for working with Big Data
Big Data Analytics software is widely used to efficiently process data and achieve a competitive advantage in the market. These software analytics tools help you track current market changes, customer needs and a variety of other valuable information. Let’s take a look at the most popular analytics tools of 2021.
Apache Hadoop
Apache Hadoop is at the top of our list. Big data will be difficult to handle without Hadoop, and data scientists are well aware of this. Hadoop is not only a completely open and free big data storage system, but also a companion set of utilities, libraries, frameworks, and distributions for development.
This foundational technology for storing and processing big data is a top-level project of the Apache Software Foundation.
Hadoop has four parts:
- HDFS is a distributed file system designed to run on standard hardware.
- MapReduce is a distributed computing model presented by Google that is used for parallel computing.
- YARN is a cluster management technology.
- Libraries – for the work of other modules with HDFS
X-plenty
This cloud-based scalable platform ranks among the leaders in its niche with ETL solutions and data pipeline tools. X-plenty handles both structured and unstructured data and integrates with a variety of sources including Amazon Redshift, SQL data warehouses, NoSQL databases, and cloud storage services. Main advantages:
- easy data conversion;
- REST API;
- flexibility in use;
- excellent security;
- various data sources;
- customer-oriented approach.
Spark
Today, this powerful open source analytics tool is a staple in the arsenal of companies including Amazon, eBay, and Yahoo. Apache Spark is a technology for working with big data through distributed in-memory computing, which increases processing speed. It is based on Hadoop and is essentially an evolution of the MapReduce concept using other types of computation, including interactive queries and streaming.
Spark is built for a wide variety of workloads such as batch applications, iterative algorithms, interactive queries, and streaming. This makes it ideal for both hobbyist use and professional processing of large amounts of data.
Cassandra
If you are familiar with NoSQL databases, you have probably come across Cassandra. It is a free open source NoSQL database and stores values as key-value pairs. This tool is the perfect choice when you need scalability and high availability without sacrificing performance.
Due to its architectural features, Apache Cassandra has the following advantages:
- scalability and reliability due to the absence of a central server;
- flexible data scheme;
- high throughput, especially for write operations;
- native SQL-like query language;
- customizable consistency and replication support;
- automatic conflict resolution.
Talend
Talend is a free and open source ETL analytics program that simplifies and streamlines big data integration. ETL makes it easy to transform raw data into information that can be used for practical business intelligence (BI). Talend software boasts features such as cloud, big data, enterprise application integration, data quality, and master data management. It also contains a single repository for storing and reusing metadata and checking data quality.
Features:
- faster development and deployment;
- less costs and free download;
- modern solution;
- single platform;
- huge dedicated community.
There are a wide variety of big data tools that can help you store, analyze, report, and do much more with your data. This software turns scarce bits of data into a powerful fuel that drives global business processes and facilitates knowledge-based decision making.
Outcomes
Once upon a time, the use of big data revolutionized information technology. Companies today are leveraging valuable data and adopting big data tools to outperform their competitors. In a competitive marketplace, established companies and newcomers alike use data-driven strategies to capture a signal, track a fire, and make a profit.
Big data enables organizations to identify new opportunities and create new types of companies that can combine and analyze industry data. In this way, clean, up-to-date and visual data provides useful information about products, optimizes business operations and brings significant economic benefits.
