Shulou(Shulou.com)06/03 Report--

Analysis Software commonly used in big data's Development

The emergence of big data research provides new and effective ideas and means for enterprises, research institutions, and the government to make decisions. In order to do a good job in big data's management and analysis, the use of some big data development tools is essential. The following are the tools commonly used in the development process of big data:

1. Apache Hive

Hive is an open source data warehouse infrastructure based on Hadoop. Through Hive, we can easily ETL the data, structure the data, and query and process big data files on Hadoop. Hive provides a simple SQL-like query language-HiveQL, which provides convenience for users who are familiar with the SQL language to query data.

2. Apache Spark

Apache Spark is a new member of the Hadoop open source ecosystem. It provides a faster query engine than Hive because it relies on its own data processing framework rather than Hadoop's HDFS services. At the same time, it is also used for event flow processing, real-time query and machine learning.

3. Jaspersoft BI Suite

The Jaspersoft package is open source software that generates reports from database columns. Industry leaders have found that Jaspersoft software is state-of-the-art, and many companies have used it to convert SQL tables into pdf, so that everyone can review it at meetings. In addition, JasperReports provides a connection configuration unit to replace HBase.

4. Keen IO

Keen IO is a powerful mobile application analysis tool. Developers only need to be as simple as one line of code to track any information they want about their application. Developers only need to do some Dashboard or query work.

5. Mortar Data

Mortar Data is a Hadoop development platform designed for developers. It replaces MapReduce with a combination of Pig and Python so that developers can simply write Hadoop Pipeline.

6. Placed Analytics

Using scripting languages and API, PlacedAnalytics can provide detailed user behavior analysis for mobile and web applications. Including, the user uses time and geographic location information. These can help developers' applications to better attract advertisers, but also help developers to improve their applications.

7. Ingres Corp

It has more than 10,000 customers and is expanding. It extends through Vectorwise and ParAccel. These developments led to the creation of Actian Vector and Actian Matrix, respectively. It has Apache,Cloudera,Hortonworks and other distributions to choose from.

8. Talend Open Studio

Talend is a unified platform that makes data management and application more simple and convenient by providing a unified lifecycle management environment across enterprise boundaries. This design can help enterprises build a flexible, high-performance enterprise architecture, which makes it possible to integrate and enable distributed applications with 100% open source services.

9. Cloudera

Cloudera is working to provide support for open source Hadoop, which can be used as a target data warehouse, an efficient data platform, or an ETL source for existing data warehouses. Enterprise size can be used as the basis for integrating Hadoop with traditional data warehouses. Cloudera is committed to becoming the "focus" of data management.

10. Pentaho Business Analytics

Pentaho's tools connect to NoSQL databases and have many built-in modules that you can drag and drop onto an image and connect them together.

The skilled use of tools can get twice the result with half the effort. These are just some commonly used tools in the process of data development, which need to be skillfully mastered by big data developers. Of course, big data will also need to use some other tools in the development process, which requires big data developers to have the ability to discover and solve problems, as well as to develop the habit of being good at accumulation! If you want to learn from big data developer, you can get in touch with the old boy education!

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