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Top 10 Big Data Companies with the Best Quality - The analytics industry is still in growth mode, and leaders emerge when an industry consolidates. Secondly, the big names got in the market early in a big way. That's also unprecedented, because established vendors have traditionally been notoriously slow to embrace a new technology. But already, IBM, Microsoft, SAP, HP, and Oracle are in the game. So, which tools and platforms should you choose? Here are 20 of the top companies to consider in the Big Data world.
Oracle has its Big Data Appliance that combines an Intel server with a number of Oracle software products. They include Oracle NoSQL Database, Apache Hadoop, Oracle Data Integrator with Application Adapter for Hadoop, Oracle Loader for Hadoop, Oracle R Enterprise tool, which uses the R programming language and software environment for statistical computing and publication-quality graphics, Oracle Linux and Oracle Java Hotspot Virtual Machine.
Calling itself the leader in self-service data analytics, Alteryx's software is meant for the business user and not the data scientist. It allows them to blend data from multiple and potentially disparate sources, analyze it and share it so that actions can be taken. Queries can be made from anything from a history of sales transactions to social media activity.
3) Splice Machine
Splice Machine bills itself as the provider of the only Hadoop relationship database management system (RDBMS). It can act as a general-purpose database that can replace Oracle, MySQL or SQL Server databases for various workloads on Hadoop. The latest version, 2.0, added Spark, which does all analytics in memory instead of on disk. Version 2.0 also added the ability to route work to one of two processing engines either OLTP or OLAP.
Pentaho is a suite of open source-based tools for business analytics that has expanded to cover Big Data. The suite offers data integration, OLAP services, reporting, a dashboard, data mining and ETL capabilities.
Pentaho for Big Data is a data integration tool based specifically designed for executing ETL jobs in and out of Big Data environments such as Apache Hadoop or Hadoop distributions on Amazon, Cloudera, EMC Greenplum, MapR, and Hortonworks. It also supports NoSQL data sources such as MongoDB and HBase. The company was acquired by Hitachi Data Systems in 2015 but continues to operate as a separate subsidiary.
SiSense sells its Prism to the largest enterprises and some SMBs alike because of its small ElastiCube product, a high-performance analytical database tuned specifically for real-time analytics. ElastiCubes are super-fast data stores that are specifically designed for extensive querying. They are positioned as a cheaper alternative to HP's Vertica systems.
Thoughtworks incorporates Agile software development principals into building Big Data applications through its Agile Analytics product. Agile Analytics helps companies build applications for data warehousing and business intelligence using the fast paced Agile process for quick and continuous delivery of newer applications to extract insight from data.
7) HP Enterprise
HP Enterprise has built up a considerable portfolio of Big Data products in a very short time. Its main product is the Vertica Analytics Platform, designed to manage large, fast-growing volumes of structured data and provide very fast query performance on Hadoop and SQL Analytics for petabyte scalability.
HPE IDOL software provides a single environment for structured, semi-structured and unstructured data. It supports hybrid analytics leveraging statistical techniques and Natural Language Processing (NLP).
HPE has a number of hardware products, including HPE Moonshot, the ultra-converged workload servers, the HPE Apollo 4000 purpose-built server for Big Data, analytics and object storage. HPE ConvergedSystem is designed for SAP HANA workloads and HPE 3PAR StoreServ 20000 stores analyzed data, addressing existing workload demands and future growth.
8) Amazon Web Services
Amazon has a number of enterprise Big Data platforms, including the Hadoop-based Elastic MapReduce, Kinesis Firehose for streaming massive amounts of data into AWS, Kinesis Analytics to analyze the data, DynamoDB big data database, NoSQL and HBase, and the Redshift massively parallel data warehouse. All of these services work within its greater Amazon Web Services offerings.
Microsoft's Big Data strategy is fairly broad and has grown fast. It has a partnership with Hortonworks and offers the HDInsights tool based for analyzing structured and unstructured data on Hortonworks Data Platform. Microsoft also offers the iTrend platform for dynamic reporting of campaigns, brands and individual products. SQL Server 2016 comes with a connector to Hadoop for Big Data processing, and Microsoft recently acquired Revolution Analytics, which made the only Big Data analytics platform written in R, a programming language for building Big Data apps without requiring the skills of a data scientist.
Google continues to expand on its Big Data analytics offerings, starting with BigQuery, a cloud-based analytics platform for quickly analyzing very large datasets. BigQuery is serverless, so there is no infrastructure to manage and you don't need a database administrator, it uses a pay-as-you-go model.
Google also offers Dataflow, a real time data processing service, Dataproc, a Hadoop/Spark-based service, Pub/Sub to connect your services to Google messaging, and Genomics, which is focused on genomic sciences.