Big data is hard to quantify because “big” is relative. In the past, a standard DVD drive that holds 4.7 gigabytes (GB) was considered big storage. Today, storage specifications are in terabytes (TB), which is 1000s of GB, or petabytes (PB), equal to 1000s of TB. Billions of people connected to computer networks are exchanging exabytes (EB) or thousands of petabytes of data annually, and it’s still increasing. Big data is a new kind of data where traditional hardware, architecture, software, methods, and processes are not effective or applicable.
What is big data software?
Big data is characterized by its volume, velocity, and variety. Businesses acknowledge that there is value in data. But to take advantage of that value inside big data, companies must have the capacity to gather huge amounts of structured and mostly unstructured data being generated at a fast rate. Big data tools are designed to collect, store, search, process, and display big data using specially designed computer hardware, servers, or computer architecture. While business intelligence (BI) software usually applies analytical tools to describe what is happening, big data software applies analytics to suggest why things are happening.
Benefits of big data software
Big data software can be applied to a wide range of business activities. Information derived from big data can help companies anticipate customer demand or predict equipment failures. The information within can help improve customer interaction or discover security vulnerabilities. Big data software enables users to:
- Integrate different data sources and applications to create a unified stream of data that can be stored, cleaned, and prepared for further use.
- Manage the storage of big data in various environments, whether on-premise or on the cloud.
- Analyze using big data analytics tools to get information and insight fast from huge volumes of data with modern features such as visual analysis and data exploration.
Best Big Data Analytics Tools 2021
Big data analytics software is being used across industries, institutions, and governments. Businesses are investing in the top big data tool to help them acquire and retain customers, tailor their products to targeted markets, innovate existing products, and identify opportunities as well as potential risks. Here is a list of the best big data software in no particular order. Understanding your particular business requirement, knowing how big data tools can solve your problem, and matching your requirement with the right solution are the best steps to take.
Wolfram Mathematica is software for technical computing that can process computational requirements of big data. It has thousands of built-in functions that cover areas such as big data, neural networks, machine learning, image processing, geometry, data science, and visualizations. Powerful algorithms can be built using its Wolfram language. Other features include automations, natural language input, predictive suggestions, notebook interface, and more. Also available are thousands of examples, access to real-world data, and cloud integration.
RStudio is an open source software for data science teams. Several products are available using its integrated development environment (IDE) that allows developers to create applications for big data. Enterprise-ready modular products enable teams to adopt the R language or Python to write code-friendly applications designed for handling big data. The open source platform includes core productivity tools, packages, protocols, and file formats that are not dependent on a single vendor.
Databricks is a unified data analytics platform for big data engineering and collaboration. It provides a workspace where teams can collaborate on big data for the whole machine learning (ML) lifecycle. Features include collaborative notebooks, preconfigured ML environments, a central repository, and ML workflow. Data science teams can quickly access and explore data, find and share insights, and build models collaboratively.
Cloudera is an enterprise data platform offering several products designed for big data. It delivers self-service big data analytics tools across different cloud environments that is secure and compliant. Services and features include a data hub, data flow and streaming, data engineering, data warehouse, operational database, and machine learning. Some of the use cases are for healthcare analytics, IoT-enabled predictive maintenance, and real-time compliance monitoring.
Qlik is a provider of data analytics and data integration solutions. It provides applications to turn raw big data into useful information and insights that can be quickly acted upon. It provides the platform that can simplify the many steps needed to transform raw big data into well-documented and analytics-ready information. Features include many out-of-the-box connectors, easy-to-use visual interface, and central repository for reusable data.
MongoDB is a general purpose, document-based, distributed non-relational database that works well with big data. It is inherently designed to handle unstructured inputs which makes most of big data. It stores data in documents rather than traditional row and columns. Other features include a powerful query language, two types of relationships, support for joins in queries, and more. It allows developers to build applications that can process and analyze billions of data points in real time.
Tableau is a BI and analytics software that is easy to use. It has many built-in visual tools and best practices that allow users to quickly explore big data rather than spend time learning how to use the software. It is an integrated platform that can be deployed in the cloud, on-premises, or directly integrate with many popular applications such as CRM systems. It also has integrated AI and ML capabilities, governance, data management, and visual and collaboration tools.
The Apache Software Foundation is an open source community of developers that have provided several big data tools. In its project list, the big data category enumerates about 50 software projects that includes well known applications such as CouchDB, a NoSQL document database, Flink, a system for expressive, declarative, fast, and efficient data analysis, and Storm, for distributed real-time processing. Big data software such as Hadoop, Cassandra, Hive, and Spark are also projects from the Apache Software Foundation.
SAP HANA is an in-memory database with built-in advanced analytics. It uses a single column-oriented database that can handle any data type for transactional and analytical processing. Some of the benefits of its in-memory database is real-time data access and support for multiple data types and models. It is ideal for data scientists as well as developers, system admins, security architects, data admins, and project managers.
IBM’s SPSS platform include many big data tools such as advanced statistical analysis, library of ML algorithms, text analysis, open source extensions, big data integration, and seamless deployment. It is also easy to use, flexible, and scalable for users of all skill levels, so organizations can find new opportunities, improve efficiency, or minimize risk. The platform includes several products such as Statistics and Modeler to give users the option for different approaches.