Pivotal Big Data Suite: Full-Featured Data Analysis
Pivotal Big Data Suite from Data Chef is a full-featured data analysis, reporting and graphing platform for managing real-time data from the world’s largest and most valuable data sets. Pivotal Big Data Suite provides insight for decision-makers in all areas of business from marketing to supply chain management. Whether it’s about extracting value, reducing risk, optimizing operations or gathering more intelligence about customers Pivotal Big Data Suite delivers the tools and the frameworks required to bring that information to everyone.
New updates to Pivotal Big Data Suite
Pivotal Big Data Suite includes new updates to Pivotal Big Data Suite, adding exciting new features to the Pivotal HD Enterprise-grade Apache Hadoop platform. Pivotal HD is one of the most widely used open-source frameworks for large-scale, distributed systems.
Apache Hadoop is a collection of frameworks and libraries that enable users to run applications on large clustered hardware without the need for complex programming languages or high-end servers. By combining the power of Map-Reduce and Oozies, Hadoop lets developers leverage large-scale data processing via a framework that is highly flexible and efficient even under heavy workloads. With Pivotal Big Data Suite developers can easily create, manage and analyze real-time data sets on the go even when on the road.
The Pivotal Big Data Suite was designed by Pivotal Software and Hortonworks to bring together the functionality of several different open source frameworks into one flexible and performant product. Pivotal Big Data offers developers a full range of analytical and data processing capabilities that span several different areas. These capabilities make the Pivotal Big Data Suite ideal for applications that require large amounts of data for analysis, exploration and reporting.
Rapid & Accurately Process
The suite also allows developers to rapidly and accurately process massive amounts of information, which is vital for running a wide variety of businesses and tasks. Developers looking to invest in the Hadoop framework can benefit from the flexible and scalable capabilities that the Pivotal Big Data Suite provides.
The Hadoop software stack is composed of many different components, including the core tooling, schedulers, and data products. Developers working with Hadoop can install the Big Data suite along with any of the tens of thousands of add-ons that are available for use with the framework.
Most popular add-ons include the Distributed Ledger Manager (DLM), Spatialaverageduler, and Cucchini Collections. The software features capabilities such as parallelism, batching, data compression, streaming, and device support. In addition, the suite includes capabilities to monitor and track performance over multiple nodes and to optimize the Hadoop network.
The Pivotal Big Data Suite: An Open Source
The Pivotal Big Data Suite was created as an open-source project under the BSD license. The BSD license is designed to grant the freedom to innovate and experiment on the open-source platform. This enables a range of developers to develop new applications and to make them compatible with the Hadoop framework and other related technologies. As a result, it is possible to run a comprehensive range of analytics applications on the BSD open-source platform.
In addition to being able to compile and run a wide range of analytics workloads, the suite of tools provided by the Pivotal Big Data suite makes it easy to build a more complicated system. For example, it is possible to create a streaming Analytics application and to utilize the streaming capabilities in the Hadoop framework. Further, the developers of the BSD licensed software can build advanced clusters based on Mesos or EMC grids. In fact, it is possible to build a cluster of hundreds of nodes on top of one another using Mesos.
The second component of the pivotal big suite is the Hadoop Distributed Management platform. The Hadoop Distributed Management platform allows for the large-scale deployment of data-intensive applications and makes the construction of highly efficient systems much easier. The third component is the Apache Hadoop collector, which is responsible for managing the Java virtual machine. The fourth component is the Apache Hadoop Distributed Warehousing, which is again a key component of the suite. The last component is the apache Map Reduce System, which is responsible for handling the Map-Reduce Compression operation.
The Pivotal Big Data Suite of tools is very useful for building large data warehouses and for managing the associated workload. The tools integrate well with the open-source HDFS and have been designed to scale up easily when the data warehouse begins to handle large volumes of data. However, the authors do recommend that users consider migrating their data warehouse over to Hadoop sooner rather than later.