Big data processing and distribution systems offer a way to collect, distribute, store, and manage massive, unstructured data sets in real time. These solutions provide a simple way to process and distribute data amongst parallel computing clusters in an organized fashion. Built for scale, these products are created to run on hundreds or thousands of machines simultaneously, each providing local computation and storage capabilities. Big data processing and distribution systems provide a level of simplicity to the common business problem of data collection at a massive scale and are most often used by companies that need to organize an exorbitant amount of data. Many of these products offer a distribution that runs on top of the open-source big data clustering tool Hadoop.
Companies commonly have a dedicated administrator for managing big data clusters. The role requires in-depth knowledge of database administration, data extraction, and writing host system scripting languages. Administrator responsibilities often include implementation of data storage, performance upkeep, maintenance, security, and pulling the data sets. Businesses often use big data analytics tools to then prepare, manipulate, and model the data collected by these systems.
To qualify for inclusion in the Big Data Processing and Distribution category, a product must:
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BigQuery is Google's fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don't need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. BigQuery is a powerful Big Data analytics platform used by all types of organizations, from startups to Fortune 500 companies.
Cloudera, based in Palo Alto, California, U.S, offers Cloudera Enterprise, a platform that includes Cloudera Analytic DB (for BI & SQL workloads based on Apache Impala), Cloudera Data Science & Engineering (for data processing and machine learning based on Apache Spark and Cloudera Data Science Workbench), and Cloudera Operational DB (for real-time data serving based on Apache HBase and Apache Kudu). Through their SDX (shared data experience) technologies, the platform provides unified security, governance, and metadata management across these workloads as well as across deployment environments. Cloudera’s platform is available on-premises; across the major cloud environments (including native object store support for S3 and ADLS); and as a managed service under the Cloudera Altus brand.
Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed. And with its serverless approach to resource provisioning and management, you have access to virtually limitless capacity to solve your biggest data processing challenges, while paying only for what you use.
MapR delivers on the promise of Hadoop with a proven, enterprise-grade platform that supports many mission-critical and real-time production uses. MapR brings unprecedented dependability, ease-of-use, and world-record speed to Hadoop, NoSQL, database and streaming applications in one unified Big Data platform.
Azure Time Series Insights is a fully managed analytics, storage, and visualization service for managing IoT-scale time-series data in the cloud. It provides massively scalable time-series data storage and enables you to explore and analyze billions of events streaming in from all over the world in seconds.
Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way. Operations that used to take hours or days take seconds or minutes instead, and you pay only for the resources you use (with per-second billing). Cloud Dataproc also easily integrates with other Google Cloud Platform (GCP) services, giving you a powerful and complete platform for data processing, analytics and machine learning.
XenonStack is a software company that specializes in product development and providing DevOps, big data integration, real time analytics and data science solutions.
FICO Decision Management Platform Streaming provides a fully integrated solution for any data -- Big Data or otherwise -- to rapidly generate powerful insights and precise decisioning from the most diverse range of sources. The Platform can import, normalize and synthesize data from any source to quickly analyze the best data to generate decisions, enabling organizations to respond to signals in the data in real-time
Combines open source Hadoop and Spark to cost-effectively analyze and manage big data Combines Hadoop and Spark Integrates Hadoop and Spark for fast processing of any type of data at scale. Improves ROI Provides data management and analytical tools to enhance Hadoop capabilities. Helps improve your ROI, whether in the cloud or on-premises. Scalable and adaptible Helps integrate Hadoop as part of a hybrid architecture that supports multiple data types and technologies. Provides the scalability and adaptability you need for big data analytics. Open source support Built on IBM Open Platform, which provides complete open source distribution of Apache ecosystem components. Enhances your ecosystem Provides deployment options and an extended portfolio of capabilities to help you make the most of Hadoop.
Infoworks addresses the end-to-end challenges you face with end-to-end data engineering solutions that are more than just a pretty user interface. We automate most of the work for you, which is why our Fortune 500 customers are in production in a matter of days.
Market Locator, powered by Instarea software, allows data rich industries to monetize a highly valuable asset – their big data. A telco can thus create a new revenue stream by providing its anonymized and aggregated big data in the form of a self service location intelligence / population analytics and mobile marketing for their B2B customers. Tested and proven on several markets with world-class telcos such as Slovak Telekom (Deutsche Telekom Group), Orange or O2. Delivered either on a partnership basis or license fee model. Get the most out of your data!
The Syncfusion Big Data Platform is the first and the only complete Hadoop distribution designed for Windows. Its users can develop on Windows using familiar tools, and deploy on Windows. Syncfusion has taken the advantages of the Hadoop environment – from easy querying across structured and unstructured data to cost-effective storage of any amount of data using commodity hardware with linear scalability- and made them available on Windows. With extremely minimal prerequisites and no manual configuration, the platform provides an easy-to-use environment for working with popular big data tools such as Pig and Hive. The industry-tested Syncfusion Big Data Platform gives users complete access to the power of the Hadoop environment - and the backing of an experienced team providing the samples and support that will get them up and running quickly.
Upsolver is a Streaming Data Preparation Platform. It removes the complexity from big and streaming data preparation projects and shortens their implementation time from weeks/months to several hours, literally. Powered by a cutting-edge Volcano technology, it queries Amazon S3 in less than a millisecond and stores 10x more data in RAM - allowing you to meet any scale and performance needs without complex data engineering work. Upsolver is packaged as a Public or Private Cloud.
ViZiX Big Data IoT Platform allows you to seamlessly Collect, Store, Analyze, Report and Act on wireless sensors data streams in real time. ViZix main features include: • A web-based interface, user configurable at ALL levels • Support for Big Data Fractal Multi-Tenancy™ to support hierarchical, multi-element Implementations • Designed for seamless Integration with existing enterprise systems (ERP, SCM, WMS, …) • Secure (SSL/TLS) • Complex Event Processing: trigger custom events and/or alerts when complex conditions occur among event streams • And more...