Large-volume data cloud Analysis market

The combination of cloud computing and massive data analysis is destined to be used together. The cloud computing model basically allows users to leverage the infrastructure and related expertise provided by the service provider without having to build it in-house.

Thankfully, massive data has been organically combined with cloud computing. Experts predict that future investment in this area will gradually increase. Several companies have merged cloud computing with massive data and launched a unique service.

Quantivo

Quantivo just announced its entry into the market in Tuesday and launched a cloud-based data analysis platform. The platform is able to bring together enterprise data from multiple sources and transform improved data, which can then be used by users with Quantivo-specific interfaces. Quantivo that its technology needs to be predicted through the "intelligently auto-compiling lists of patterns" during the collection of customer datasets.

101data

101data actually has more than 10 years experience in processing large data areas. Before people talk and calculate, 101data has provided a variety of specific services for the actual use of the volume data, including data warehousing and business intelligence advanced analysis. Customers can interact with services using familiar tools. For example spreadsheets, customers can easily find what they want. Tim Negris, senior vice president of 101data, says the collection of massive data and the storage and use of massive amounts of data are really two different things. Doing anything beforehand requires a lot of (preparing data) work is one of the challenges facing Oracle and most database vendors. We are trying to eliminate this problem and hand the data directly to analysts.

Opera FX

Opera FX is an interesting company. Although the annual income of 100 million dollars, but few people know the company. Although the company is little known, its services are quite attractive to customers. Customers upload their data to the opera platform, then analyze the data and finally analyze the customer's data according to the "tags" associated with the customer dataset. Opera is not satisfied with providing ordinary data analysis to customers, Opera's business focuses on the specific needs of each customer and the use of professional tools in various industries to help each customer meet their unique data analysis needs.

IBM

IBM offers a wide range of options for cloud-based data analysis based on cloud services, but the current IBM strategy seems to be largely around Hadoop. IBM launched its SmartCloud cloud computing platform in April. and promised to improve the Hadoop workload. IBM offers Infosphere biginsights based on Hadoop (IBM Infosphere biginsights is software and services for analyzing and virtualizing massive amounts of data, which is supported by Apache Hadoop.) Basic Edition and Enterprise Edition. Infosphere Biginsights, a cloud product previously tested and developed by IBM, is now being replaced by SmartCloud.

Amazon Web Services

AWS does not actually provide analytics services, and AWS mainly has a large scale parallel processing framework and computational power. Amazon Elastic MapReduce creates a data-processing workflow that is executed by Hadoop software under the Amazon EC2 architecture. It automatically starts and configures a certain number of Amazon EC2 instances automatically according to customer requirements. It then produces a Hadoop implementation based on the MapReduce programming model, which reads a large number of user input data from Amazon S3 and scales them to the generated Amazon EC2 instance for parallel processing. Like AWS Services, Amazon elastic MapReduce customers only need to pay for the parts they use.

HPCC Bae

Hadoop relies on two core components to store and process massive data--hadoop Distributed file systems and Hadoop Mapreduce. Cloudant CEO Mike Miller believes MapReduce is still relatively complex in writing parallel processing workflows, HPCC to improve the situation through ECL (Enterprise control Language). HPCC provides two kinds of data processing and service modes--thor data Refinery Cluster and Roxy Rapid data IBuySpy. Escalante said it was so named because it could solve difficult problems like Thor (the Nordic mythology of Sre, war and Agriculture), Thor mainly used to analyze and index large amounts of Hadoop data. Roxy is more like a traditional relational database or data warehouse, and can even handle the service of Web front-end. While there is no in-depth discussion of the details of the HPCC storage component, Escalante says HPCC is based on a distributed file system and can support a variety of off-node storage architectures and local SSD. The HPCC system has been widely used in finance and other important industries. (Li/compiling)

Original link: Gigaom

(Responsible editor: Liu Fen)