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

What are the common big data analysis software used for big data development?

The emergence of big data research provides new effective ideas and means for enterprises, research institutions and government decision-making. To do a good job in the management and analysis of big data, the use of some big data development tools is essential. The following are commonly used tools in the process of big data development:

1. Apache Hive

Hive is an open source data warehouse infrastructure built on Hadoop. Hive can easily ETL data, structure data, query and process large data files on Hadoop. Hive provides a simple SQL-like query language-HiveQL, which provides convenience for users familiar with 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 relying on Hadoop's HDFS service. It is also used in event stream processing, real-time queries, and machine learning.

3. Jaspersoft BI Suite

Jaspersoft package is an open source software for generating reports from database columns. Industry leaders have found Jaspersoft software to be top-notch, and many businesses have used it to convert SQL tables to pdf, which allows everyone to review them at conferences. In addition, JasperReports provides a connection configuration unit to replace HBase.

4. Keen IO

Keen IO is a powerful mobile app analytics tool. Developers can track whatever information they want about their app down to a single line of code. The developer then just needs to do some Dashboard or query work.

5. Mortar Data

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

6. Placed Analytics

Using scripting languages and APIs, PlacedAnalytics provides detailed user behavior analysis for mobile and web apps. include information about that time and geographic location of the user. These can help developers make their apps more attractive to advertisers, and they can also help developers improve their apps.

7. Ingres Corp

It has over 10,000 customers and is growing. It is extended by 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 easier by providing a unified, lifecycle management environment across enterprise boundaries. This design helps organizations build flexible, high-performance enterprise architectures where distributed applications that integrate and enable 100 percent open source services become possible.

9. Cloudera

Cloudera is working hard to provide support for open source Hadoop, which can serve as a target data warehouse, an efficient data platform, or an ETL source for an existing data warehouse. Enterprise scale can be used as a basis for integrating Hadoop with traditional data warehouses. Cloudera strives to be the "center of gravity" of data management.

10. Pentaho Business Analytics

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

The skilled use of tools can achieve twice the result with half the effort. The above are only some commonly used tools in the data development process. For big data developers, it is necessary to master them skillfully. Of course, some other tools will be needed in the big data development process. This requires big data developers to have the ability to discover and solve problems, and to develop the habit of being good at accumulation! If you want to learn big data development, you can contact the old boy education!

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