What are the big data development tools?
The emergence of big data research has provided new and effective ideas and means for enterprises, research institutions, and government decision-making. In order 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 It is a commonly used tool in the process of big data development:
1. Apache Hive
Hive is an open source data warehouse infrastructure built on Hadoop. Through Hive, data ETL can be easily performed, data can be structured and processed, and big data files on Hadoop can be queried and processed. Hive provides a simple SQL-like query language-HiveQL, which provides convenience for users who are familiar with SQL language to query data.
2. Apache Spark
Apache Spark is a new addition to the Hadoop open source ecosystem. It provides a faster query engine than Hive because it relies on its own data processing framework instead of relying on Hadoop's HDFS service. At the same time, it is also used in event stream processing, real-time query and machine learning.
3. Jaspersoft BI Manuscript
The 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, making it available for everyone to review at meetings. Additionally, JasperReports provides a connection hive as an alternative to HBase.
4. Keen IO
Keen IO is a powerful mobile application analysis tool. Developers can track any information they want about their application with as little as one line of code. The developer only needs to do some Dashboard or query work next.
5. Mortar Data
Mortar Data is a Hadoop development platform for developers. It replaces MapReduce with a combination of Pig and Python so that developers can easily write Hadoop pipelines.
6. Placed Analytics
Using scripting languages and APIs, PlacedAnalytics can provide detailed user behavior analysis for mobile and web applications. Including, user usage time and geographic location information. These can help developers' applications better attract advertisers, and can also help developers improve their own applications.
7. Ingres Corp
It has more than 10,000 customers and is growing. It implements extensions through Vectorwise and ParAccel. These developments led to the creation of the 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 and more convenient by providing a unified environment for lifecycle management across enterprise boundaries. This design can help enterprises to build a flexible, high-performance enterprise architecture. Under the sub-architecture, it becomes possible to integrate and enable distributed applications with 100% open source services.
9. Cloudera
Cloudera is working hard 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 scale can be used as a basis for integrating Hadoop with traditional data warehouses. Cloudera strives to be the "center of gravity" for data management.
10. Pentaho Business Analytics
Pentaho's tool can connect to NoSQL databases, has a lot of built-in modules, drag and drop them onto a picture, and connect them.
The skilled use of tools can achieve twice the result with half the effort. The above are just some of the tools commonly used in the data development process. For big data developers, they need to be proficient. Of course, in the process of big data development, some other tools will also be needed. Tools, which requires big data developers to have the ability to discover and solve problems, as well as develop the habit of being good at accumulation! If you want to learn big data development, you can contact Old Boy Education!