Data has since long helped in gaining meaningful insights. Take the example of students’ performance in a class. Collecting data regarding how much every student has scored in different exams subject-wise can help gain insights like which student stood first in the class, the number of students who passed the final exams, students who achieved above 90% marks, and so on. This manual calculation becomes complex when you have thousands of students. Similarly, when we are dealing with big data, we need to put in extra effort to gain valuable conclusions from it.
Big data analytics basically refers to the process of uncovering meaningful insights from raw data that can be structured or unstructured. Identifying hidden patterns, correlations, and trends can help make decisions, design better products, offer enhanced customer service, prevent fraud, and more. You may be witnessing the use of big data analytics in your daily life. For instance, you can see the daily COVID-19 cases in each country, the positivity rate, what can be predicted from the current trends, etc. This is all possible because of big data analytics. As we mentioned earlier, big data cannot be analyzed by traditional data management systems. We need powerful tools to process them and identify patterns.
So, in this article, we are going to make you familiar with the top big data analytics tools used by companies for various purposes.
Top Big Data Analytics Tools
Let’s dive into some of the top tools that help organizations harness big data analytics’ potential successfully.
Apache Hadoop
Apache Hadoop is, by far, the most common big data analytics tool you will come across. It is a framework that allows for distributed processing of large data sets across clusters of computers through simple programming models. A highly scalable Java-based tool, Hadoop offers cross-platform support and has the ability to handle any variation of data needed. Rather than relying on hardware to deliver high availability, the framework is designed to identify and handle failures at the application layer. So, it delivers a highly-available service on top of a cluster of computers.
MongoDB
MongoDB, as described on its official website, is a document database that offers the scalability and flexibility that users want for querying and indexing. Its document model is easy to learn and use for developers and offers all the capabilities needed to meet the most complex requirements at scale. The data is stored in JSON-like documents, which means fields can vary from document to document and the data structure can be changed over time. Features like indexing, ad hoc querying, and real-time aggregation offer powerful ways to access and analyze the data.
Apache Spark
Apache Spark is a multi-language engine used for executing data science, data engineering, and machine learning on clusters or single-node machines. With Spark, users can unify the processing of their data in batches and real-time streaming, with a preferred language like SQL, Python, Java, Scala, or R. One of its top features include executing fast and distributed ANSI SQL queries for dashboarding and ad-hoc reporting, and running faster than most data warehouses. Moreover, users can perform Exploratory Data Analysis (EDA) on petabyte-scale data without having to resort to downsampling.
Xplenty
If you are a part of an organization that wishes to integrate, process, and prepare data for analytics on the cloud, then Xplenty is a suitable tool. Its scalable platform offers a coding and jargon-free environment through which companies can harness the power of big data without investing in hardware, software, or related personnel. The tool is trusted by a number of renowned firms like Samsung, Sabre, Caterpillar, PwC, Accenture, and IKEA. It combines a drag-and-drop interface and personalized user support that helps design sophisticated ETL (extract, transform, load) pipelines.
Talend
Talend helps organizations deliver healthy data and change the way they make important business decisions. The Talend Data Fabric combines data integration, integrity, and governance in a unified platform that unlocks the true power of data. It improves customer satisfaction through better insights and saves money by enhancing operational efficiency. It has collaborated with AWS, Snowflake, Azure, and other technologies to ensure seamless performance across the data infrastructure. Moreover, Talend was named a Leader in Gartner’s 2021 Magic Quadrant for Data Integration Tools.
Splunk
You can trust a tool that is trusted by 92 of the Fortune 100 companies, including Intel, Hyatt, Yelp, Comcast, and Coca-Cola. Splunk solutions offer everything you need to make your digital initiatives successful. The tool helps answer questions across the business, DevOps, IT, and security functions with state-of-the-art investigative capabilities, seamless collaboration, and intuitive visualizations. Splunk is able to ingest any text-based data without the need to organize it first, and do so from anywhere, whether the data is stored on-premise, cloud, IoT device, or CRM.
Bottom Line
Professionals skilled in big data analytics are in high demand these days. Companies are reaping the various benefits associated with data analytics and hence want to hire individuals that can enhance the performance of their big data initiatives. Knowledge of any of the above tools will help you stay ahead of your peers and grab the attention of the hiring managers.
