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Jan 24, 2024 · Here are 18 popular open source tools and technologies for managing and analyzing big data, listed in alphabetical order with a summary of their key features and capabilities. 1. Airflow. Airflow is a workflow management platform for scheduling and running complex data pipelines in big data systems.
- APACHE Hadoop. It’s a Java-based open-source platform that is being used to store and process big data. It is built on a cluster system that allows the system to process data efficiently and let the data run parallel.
- Cassandra. APACHE Cassandra is an open-source NoSQL distributed database that is used to fetch large amounts of data. It’s one of the most popular tools for data analytics and has been praised by many tech companies due to its high scalability and availability without compromising speed and performance.
- Qubole. It’s an open-source big data tool that helps in fetching data in a value of chain using ad-hoc analysis in machine learning. Qubole is a data lake platform that offers end-to-end service with reduced time and effort which are required in moving data pipelines.
- Xplenty. It is a data analytic tool for building a data pipeline by using minimal codes in it. It offers a wide range of solutions for sales, marketing, and support.
- Hadoop. Apache developed the open-source Java-based Hadoop framework for managing big data. It offers a distributed storage infrastructure for processing large and complex data sets, handling hardware failures, and processing batch information using simple programming models.
- Spark. The Apache Spark open source analytics engine is a top choice for large-scale analytics, with 80 percent of Fortune 500 companies using it for scalable computing and high-performance data processing.
- MongoDB. Recognized as one of the leading big data technologies, MongoDB is a NoSQL database that helps create more meaningful and reactive customer experiences using AI/ML models.
- R Language. R is a free software environment that offers highly extensible statistical and graphical techniques for effective data handling and storage.
May 1, 2024 · Learn about the four types of big data technologies (storage, mining, analytics, and visualization) and the tools (Apache Hadoop, MongoDB, Rapidminer, Presto, Apache Spark, Splunk, Tableau, and Looker) that can help you manage and analyze big data. Coursera also offers courses on big data analysis based on Hadoop, Spark, and Storm.
Learn about the top tools for processing, analyzing, and visualizing big data, such as Hadoop, Spark, and HBase. Compare their features, pros, and cons, and find the best fit for your needs.
- Hadoop Apache Spark Apache HBase Apache Cassandra MongoDB Elasticsearch Apache Flink Splunk Tableau QlikView KNIME RapidMiner Apache Zeppelin SAS A...
- #1 Data Governance Framework #2 Data Quality Assurance #3 Data Security Measures #4 Regular Backups #5 Data Classification and Categorization #6 Da...
- Keep Data Organized Make Sure Data Is Accurate Ensure Data Stays Fast Lock the Data Safe Mix Data Together Store Old Data Know More About Data Be R...
- Define Objectives Choose the Right Tools Understand Data Sources Profile Data Structure Assess Data Quality Identify Anomalies Document Your Findin...
- Get to Know Your Data Clean and Verify Data Organize Data Combine Data Sources Summarize Data Add More Context Keep Track of Changes Document Every...
Jun 23, 2023 · Learn about the best Big Data tools and software for 2024, including Hadoop, Spark, BigQuery, Snowflake, Tableau, PowerBI, and Databricks. Compare their features, pros, cons, and pricing to find the best fit for your needs.
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Jun 13, 2024 · 1) Zoho Analytics. Zoho Analytics is a self-service business intelligence and analytics platform. It allows users to create insightful dashboards and visually analyze any data in minutes. It features an AI powered assistant that enables users to ask questions and get intelligent answers in the form of meaningful reports. #1 Top Pick. Zoho Analytics
