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What this article shares with you is about the open source software that is easy to use in the field of big data. The editor thinks it is very practical, so I share it with you to learn. I hope you can get something after reading this article. Let's take a look at it with the editor.

I believe we all understand the importance of open source software. There are too many open source software that are easy to use. The following is mainly aimed at the field of big data, recommend ten kinds of easy-to-use open source software, namely HBase, Hadoop distributed file system, Hive, Spark, Redis, SpagoBI, Lucene, HDP, CDH, HPCC.

1 、 HBase

HBase is designed for very large tables with billions of rows and millions of columns, a distributed database that provides random real-time read / write access to big data. It is similar to Google's Bigtable, but based on Hadoop and Hadoop distributed File system (HDFS).

2. Hadoop distributed File system (HDFS)

HDFS is an Hadoop-oriented file system, but it can also be used as a standalone distributed file system. Based on Java, it is fault-tolerant, highly scalable and highly configurable. Supported operating systems: Windows, Linux, and OS X.

3 、 Hive

Apache Hive is a data warehouse for the Hadoop ecosystem. It allows users to query and manage big data using HiveQL, a language similar to SQL.

4 、 Spark

As an alternative to MapReduce, Spark is a data processing engine. It claims that it is up to 100 times faster than MapReduce when used in memory and up to 10 times faster than MapReduce when used on disk. It can be used with Hadoop and Apache Mesos, or independently. Supported operating systems: Windows, Linux, and OS X.

5 、 Redis

Redis is now sponsored by Pivotal, a key caching and storage system. Provide paid support. Note: although the project does not officially support Windows, Microsoft has a Windows derivative on GitHub. Supported operating system: Linux.

6 、 SpagoBI

Spago, known as the "open source leader" by market analysts, provides business intelligence, middleware and quality assurance software, as well as a Java EE application development framework. The software is free and open source, but it also provides paid support, consulting, training and other services.

7 、 Lucene

Java-based Lucene can perform full-text search very quickly. According to the official website, it can retrieve more data than 150GB every hour on modern hardware, and it contains powerful and efficient search algorithms. The development work is sponsored by the Apache Software Foundation.

8 、 HDP

HDP is an Apache Hadoop distribution. Hortonworks Data Platform (HDP) is used to create a secure enterprise data lake that provides enterprises with the analytical information they need to achieve fast, real-time business insight. HDP is an open source ecosystem of big data.

9 、 CDH

CDH is an Apache Hadoop distribution. CDH is similar except that it is not as open and open source as HDP. CDH documents are well organized and organized, and can be used as an important resource for understanding and learning big data and Apache Hadoop.

10 、 HPCC

In the big data market dominated by Apache Hadoop & Spark, HPCC is very precious and deserves more attention. HPCC includes the following core components: Thor (the Data Refinery Cluster); Roxie (Rapid Online XML Inquiry Engine, the Query Cluster); and ECL (Enterprise Control Language) ECL IDE;ESP (Enterprise Services Platform). HPCC has many success stories in security, identity and risk management.

These are the open source software that is easy to use in big data's field, and the editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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