The database and data warehouse are one of the essential software components in the enterprise. Traditional database systems are not efficient to handle large volumes of users and they are mainly designed to operate on a single server. As applications have evolved to serve large volumes of users, and as application development practices become agile, you surely need advanced database products.
1.Jaspersoft BI Suite
It is one of the open source leaders for producing reports from database columns. It adds a software layer to connect its report generating software to the places where big data gets stored. Once you get the data from these sources, Jasper soft’s server will boil it down to interactive tables and graphs.
2.Pentaho Business Analytics
It is a software platform that began as a report generating engine. It branch’s big data by making it easier to absorb information from the new sources. It also provides software for drawing HDFS file data and HBase data from Hadoop clusters. It has a bunch of built-in modules that you can drag and drop onto a picture, then connect them.
3.Talend Open Studio
It offers an Eclipse-based IDE for stringing together data processing jobs with Hadoop. Its tools are designed to help with data integration, data quality, and data management, all with subroutines tuned to these jobs. Talend Studio allows you to build up your jobs by dragging and dropping little icons onto a canvas.
It’s not exactly a report-generating tool or a collection of AI routines, although it accomplishes much of that along the way. It creates an index of your data as if your data were a book or a block of text. The index helps correlate the data in these and several other common server-side scenarios.
It is a fantastic tool for predictive analysis. It’s powerful, easy to use and has a great open source community behind it. It can even integrate your own specialized algorithms into RapidMiner through their APIs.
It is a distributed real-time computation system that allows you to process unbounded streams of data reliably. It does for real-time processing what Hadoop does for batch processing.
It is an SQL query engine for Big Data exploration. It has been designed from the ground up to support high-performance analysis on your semi-structured and rapidly evolving data coming from modern Big Data applications.
It is the right choice when you need scalability and high availability without compromising performance. It is suitable for applications that can’t afford to lose data. It’s used by large, active datasets, including Netflix, Twitter, Urban Airship, Constant Contact, Cisco and Digg.
It is a distributed column-oriented database built on top of the Hadoop file system. It is an open-source project and is horizontally scalable. It includes linear and modular scalability, strictly consistent reads and writes, automatic failover support and much more. It leverages the fault tolerance provided by the Hadoop File System (HDFS).
It is a highly scalable native graph database that leverages data relationships as first-class entities, helping enterprises build intelligent applications to meet today’s evolving data challenges. It boasts performance improvements up to 1000x or more versus relational databases.
It combines the flexibility of document databases with the power of graph databases while supporting features such as ACID transactions, fast indexes. It has a strong security profiling system based on users and roles and supports
It is an open source distributed, fault-tolerant graph database for managing wide but shallow network graphs. It differs from other graph databases. Since it is still in the process of being packaged for outside of Twitter use, the code is still very rough and hence there is no stable release available yet. It was designed to store social graphs .It offers horizontal scaling and very fast reads and writes.