Here are the top 10 big data technologies that you need to know in the year 2023
Today, we live in the digital age full of data technologies, when businesses generate and manage massive volumes of data regularly. The term “Big Data” refers to this vast accumulation of organized and unstructured data, which is growing at an exponential rate due to greater digitization. Traditional data processing software, however, is incapable of handling and extracting usable information from Big Data due to its enormous bulk and complexity. As we move ahead to 2023, the good news is that there are a variety of dependable big data solutions to pick from. In this article, we have explained the top 10 big data technologies that you need to know in 2023.
What is Big Data Technology?
Big data technologies refer to the high volume, and variety of information assets that necessitate creative data processing methods and are cost-effective, for better insight and decision-making rather than traditional data processing methods. Big Data Technologies are software utilities that are primarily intended to analyze, process, and extract information from big datasets with exceedingly complex structures than traditional data processing technology
Top Big Data Technologies
Many big data technologies have recently impacted the market and IT industries. They can be classified into four broad categories, which are as follows:
1. Apache Hadoop
The Apache Software Foundation created Apache Hadoop, an open-source, Java-based framework for storing and analyzing large amounts of data. In essence, it provides a distributed storage infrastructure and uses the MapReduce programming methodology to process large amounts of data.
2. MongoDB
MongoDB is a cross-platform, open-source document-oriented database designed to store and process huge volumes of data while maintaining high performance, availability, and scalability. MongoDB is classified as a NoSQL database because it does not store or retrieve data in the form of tables.
3. RainStor
RainStor is a database management system developed by the RainStor corporation that handles and analyses massive data. De-duplication is a technique used to simplify the storage of vast volumes of data for reference. It eliminates duplicate files due to its ability to organize and store vast volumes of information for reference.
4. Cassandra
Cassandra is a distributed open-source NoSQL database that allows for in-depth analysis of several sets of real-time data. It allows for high scalability and availability without sacrificing performance. CQL is used to interface with the database.
5. Presto
Facebook created Presto, an open-source SQL query engine that allows for interactive query analysis on massive amounts of data. This distributed search engine allows for quick analytics searches on data sources ranging in size from gigabytes to petabytes. This technology allows you to query data exactly where it is, without having to move it to a separate analytics system.
6. RapidMiner
RapidMiner is a powerful open-source predictive analytics data mining application. It’s a strong data science platform that enables data scientists and big data analysts to quickly analyze their data. It supports model deployment and model operation in addition to data mining. You will have access to all of the data preparation tools and machine learning you require to have an effect on your company operations with this solution.
7. ElasticSearch
Elasticsearch, which is based on Apache Lucene, is a distributed, open-source, analytics, modern search engine that allows you to index, search, and analyze data of all types. Log analytics, operational intelligence, security intelligence, full-text search, and business analytics are some of its most common use cases.
8. Kafka
Apache Kafka is a popular open-source event store and streaming technology written in Java and Scala by the Apache Software Foundation. Thousands of organizations rely on the platform for streaming analytics, high-performance data pipelines, data integration, and mission-critical applications.
9. Splunk
Splunk is a scalable, sophisticated software platform that finds, analyzes, and visualizes machine-generated data from websites, applications, sensors, and devices, among other sources, to offer metrics, diagnose problems, and gain insight into corporate processes. Splunk captures, indexes, and correlates real-time data into a searchable repository that can be used to generate Reports, Alerts, Graphs, Dashboards, and Visualizations.
10. KNIME
KNIME, commonly referred to as Konstanz Information Miner, is a platform for data analytics, integration, and reporting that is free and open-source. KNIME is not only intuitive and open, but it also actively absorbs new ideas and advancements to make data interpretation and constructing data science processes and reusable components as simple and accessible as feasible.
