Cloud computing era: When Big Data meets agility

At the O ' Reilly Media conference in New York this September there were two big calls for big Data technology: Enterprise-class and agile. We know that enterprise-class business intelligence products include Oracle Hyperion, SAP BusinessObjects, and IBM Cogonos, while Agile products have qlikview, tableau, and Tibco Spotfire.

If it turns out that big data has to buy enterprise-class products, that means big data can cost a lot. But this is not absolute, with large data agile technology, companies of all sizes can control costs and benefit from large data. It is essential to minimize costs and maximize the understanding of large datasets, once the data has been converted to usable, have insight into the business, then aggregate the problems in various ways, and take advantage of the advantages of enterprise technology to solve the problem.

Enterprise VS Agile Bi

First let's look at what's going on in the bi world. The purpose of Enterprise BI design is to meet the requirements of many islands of information in large enterprises. The typical scenario is as follows: The information isolated from the data through the process of ETL is purged and normalized, into the Data Warehouse, become an available version. Then, through the reporting and analysis techniques, the data are sliced, diced, and delivered to thousands of people. Enterprise BI is a complex process that works together through a variety of applications to meet the needs of tens of thousands of people in the enterprise. The problem with enterprise BI is that its configuration takes a long time, and all large, complex systems are difficult to configure and change.

Agile BI can solve many of the problems faced by enterprise BI. Agile Bi can sort, filter, and statistic data in a highly interactive manner for end users without the guidance of BI experts. With agile BI technology, enterprises can enjoy the value of data more widely.

Enterprise VS Agile Large Data

Now is the time for large data technology, Emcgreenplum, Teradata Aster data, etc. are enterprise-class representatives, and 1010data, pervasive datarush, etc. are agile representatives. Both types of vendors are aware of the gap between enterprise-level and agile in the bi area and are working to address this problem. Enterprise-wide data providers know they need agile, and agile big data vendors know they need to deliver high-quality enterprise-class solutions.

Enterprise-wide data providers offer some flexibility solutions. Greenplum has launched a product called Chorus to provide a collaborative environment that supports data analysis and lookup processes. Aster provides a series of SQL extensions that allow Hadoop-type queries to use syntax similar to SQL. In implementing agility, the expansion of these products is a great step forward, but the question of the high price tag for greenplum and Aster enterprise-class technology is more interesting: can you make up for the lack of enterprise-class technology with agile large data technology and get more return with less investment?

Agile large data solves enterprise platform challenges

Looking back at the three agile big Data technologies we mentioned, the first question is: Why are they called agile?

The answer is really simple, they can give users access to extraordinary data insights and cut prices. Follows：

With only a little training, users can use Splunk to query, filter, and display data

1010data provides users with large data processing interface in spreadsheet form

Pervasive Datarush process data in parallel and efficiently with a graphical interface

A case of a large agile data in bulk processing

David Inbar, chief executive of the pervasive's Market development strategy Office, did an experimental demo at a low price, dealing with large enough data that could be called large data.

The Datarush Toolkit eliminates the complexity of programmer concurrent programming. Even the tech geek, in contrast to writing a single-threaded program, is a rocket science. Datarush provides a programming model for parallel programming to replace difficult multicore function encodings. For example, you can write a workflow application based on a component model that automatically translates into a parallel application that the workflow allows.

Large data systems such as Hadoop have horizontal scalability. Although Datarush can assume such roles and can be installed on thousands of computers, the difference is that it is typically installed on a single computer to discover the potential of multicore systems.

Datarush's most distinctive feature is that it does not require you to know how many cores the computer has, and when writing a Datarush application, it automatically perceives the number of cores on the computer and maximizes the use for parallel processing.

Assemble agile components to build enterprise-class systems

Agile technology has a great advantage in creating intelligent business systems for large data, but it still has a long way to go. Agile bi can reduce costs, and it will prove its value in the face of big data challenges.

(Responsible editor: Lu Guang)