1. Hadoop

Hadoop is a distributed system infrastructure developed by the Apache Foundation. Users are able to develop distributed programs without knowing the underlying details of the distribution. The power of clusters is fully utilized for high-speed computing and storage.Hadoop is a software architecture that enables distributed processing of a lot of data.Hadoop performs data processing in a robust, efficient, and scalable way.

2. Apache Hive

Hive is an open-source data warehouse infrastructure built on Hadoop, through Hive can be very simple to carry out the ETL of the data, structured processing of data, and Hadoop on the large data file query and processing. data files on Hadoop for querying and processing. Hive provides a simple SQL-like query language HiveQL, which provides a convenient way to query data for users who understand the SQL language.

3. Apache Spark

Apache Spark is a new member of the Hadoop open source ecosystem. It supplies a faster query engine than Hive due to the fact that it relies on its own data processing structure instead of relying on Hadoop's HDFS service. Together, it is also used for things like stream processing, real-time querying, and machine learning.

4. Keen IO

Keen IO is a robust mobile app analyzer. Developers can track whatever information they want about their app with as little as one line of code. The developer then just needs to do some Dashboard or query work.

5. Ambari

Apache Ambari is a web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters. , MapReduce, Hive, Pig, Hbase, Zookeper, Sqoop, and Hcatalog.

6. Flume

Flume is a highly available, highly reliable, distributed mass logging system for collection, aggregation and transmission provided by Cloudera, Flume supports customization of various types of data senders in the logging system for the purpose of collecting data. Flume supports the customization of various types of data senders in the logging system for collecting data; together, Flume provides the ability to briefly process the data and write to various data recipients (which can be customized).

7. MapReduce

MapReduce is a programming model used for large-scale datasets (greater than 1TB) of parallel computing. The concepts "Map" and "Reduce", which are at the forefront of their thinking, are borrowed from functional programming languages, along with features borrowed from vector programming languages. It greatly facilitates programmers to run their programs on distributed systems without being able to program in distributed parallel.

About what are the common big data development tools, Green Vine has shared with you here. If you have a strong interest in big data engineering, I hope this article can help you. If you still want to learn more about data analysts, big data engineers tips and materials, you can click on other articles on this site to learn.