For organizations today, the most important asset is ‘Data’. And in order to be successful and be ahead in the race of getting customers and market shares, it is important for them to not just collect this data but also analyze it to use for business decisions and innovations.
But how does an organization achieve all this and with ease. Well the answer is ‘Big data Platform’. Big data platforms help store, manage and analyse big data to achieve required business outcomes.
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But with so many big data platforms being available today, which is the right one for your business, which platform meets your needs is a question. So we compiled a list of ‘ 10 Leading Big Data Platforms’ and what they have to offer you. While there are dozens of big data platforms that have propped up over the last few years, we created this list based on their relative popularity right now, collected through mediums like Google insights.
Cloudera Enterprise is a high performance low cost platform for conducting analytics on data. Cloudera Enterprise has the only native Hadoop Search engine and this platform provides its users with active data optimization feature. Cloudera manager includes advanced features like intelligent configuration defaults, customized monitoring, and robust troubleshooting which allow easy administration of Hadoop in any environment. With predictive maintenance included in its Support Data Hub, Cloudera Enterprise keeps you up and running.
Hewlett Packard Enterprise’s big data platform Vertica is one of the most advanced SQL database analytics portfolio to address today’s demanding big data analytics initiatives. It allows companies to manage and analyse massive volumes of structured and semi-structured data quickly and reliably with no limits. Vertica allows organizations to leverage on analytics by providing support for all leading BI and visualization tools, open source technologies like Hadoop and R, and built-in analytical functions.
Hortonworks Data Platform (HDP)
HDP is a secure and enterprise-ready open source Apache Hadoop distribution. It is based on a centralized architecture YARN. This big data platform takes care of the entire data need i.e. it addresses data-at-rest, powers real-time customer applications and delivers robust analytics to help organizations in decision making. YARN and Hadoop Distributed File System (HDFS) are the two pillars of HDP.
IBM Big data Platform
IBM’s big data platform caters to the full spectrum of big data business challenges allowing its users to have an integrated experience. This platform is a blend of traditional technologies and modern new technologies. It combines the traditional technologies which are best suited for structured, repeatable tasks along with new technologies which provide speed and flexibility and are ideal for adhoc data exploration, discovery and unstructured analysis. Hadoop-based analytics, stream computing, data warehousing, and information integration & governance are four core capabilities of IBM Big data Platform.
Kognitio Analytical platform
The Kognitio Analytical Platform is best suited to run complex analytics on big data. It is designed to cater to organization with more users having more complex queries and requiring a shorter response time. This analytics platform provides ultra-fast high-concurrency SQL for Hadoop platforms and also for existing data warehouse implementations. It seamlessly interoperates with the user’s existing BI, analytics and visualisation applications, and Hadoop big data storage.
MapR Converged Data Platform
MapR Converged Data Platform is one single platform for big data applications. Organizations using MapR’s platform do not require to move data to specialized silos for processing as data can be processed in place. MapR’s Platform is based on the concept of Polyglot Persistence which allows to leverage multiple data types and formats directly. MapR, a converged data platform integrates the power of Hadoop and Spark with global event streaming, real-time database capabilities, and enterprise storage, thereby enabling its users to experience the enormous power of data.
Pivotal Big Data Suite
Pivotal Big Data Suite aims at being a database technology which helps organizations accelerate their digital transformation. Pivotal Big Data Suite is based on open source technologies. This big data platform aims to provide a wide and comprehensive base for modern data architectures. Pivotal Big Data Suite can not only be deployed on-premise but also in public clouds. It contains all the elements necessary for batch and streaming analytics architectures.
Qubole Data Service
Qubole Data Service (QDS) aims at making the platform more accessible to carry out big data analytics for data stored in the cloud accounts of AWS, Google and Microsoft. QDS is an integrated interface which is used to perform ad hoc analysis, predictive analysis, machine learning, streaming for data driven organizations. Also QDS’s workbench feature allows users who do not have knowledge of writing SQL query to query using SmartQuery interface. QDS Data Engines are fully automated and optimized for the cloud.
SAP’s big data platform HANA is designed for next-generation applications and analytics. It is an in-memory platform which focuses on running the analytics applications in a smarter way, making business processes faster and creating simpler data infrastructures. It acts as the foundation for all data needs by allowing to integrate all types of data and using advanced analytical processing for deeper insights.
Teradata Integrated Big Data Platform
The Teradata Integrated Big Data Platform lets its user offload the data and workloads from Teradata IDW to a low-cost system of Teradata. All this can be done without disturbing the execution of Teradata analytics that the user wishes to carry out. This feature of Teradata Integrated Big Data Platform helps you avoid the complexity of adding Hadoop for low-cost storage. Also this integrated big data platform boasts of having global hot spare drives, an optional hot standby node, and redundant power supplies for enhanced system availability.