Today, many companies are trying to mine the vast amount of data they own, including structured, unstructured, semi-structured and binary data, to explore the further use of data.

Most companies estimate that they have analyzed only 12 percent of the available data, leaving 88 percent underutilized. Massive data silos and lack of analytical capability are the main reasons for this situation. Another difficulty is determining whether data is valuable. Especially in the era of big data, in order to avoid data loss you have to collect and store data. Data that may not seem relevant to the business, such as mobile GPS data, may also prove useful in the future.

As a result, many companies are pinning their hopes on Hadoop to solve the following problems:

Collect and store all data related to the company’s business functions. Support advanced analytics, including business intelligence, for advanced visualization and predictive analysis of data in a modern manner. Share data quickly with those who need it. Consolidate multiple data silos
To solve complex questions that no one has ever asked before, or even unknown. Hadoop support

The solution

The rapid and efficient expansion of scale enables the rapid processing of ever-increasing capacity, speed and diverse data.

Now that the Hadoop purchase cycle is on the rise, there are more and more vendors in the space. Although Hadoop is an open source project from Apache that can be downloaded for free by anyone, most consumers prefer vendor packages. In addition to packaging all Hadoop components and making them usable (compatible versions), vendors typically provide enterprise-level support and extensions: Apache Hadoop (HDFS) as the core component of the solution, with additional implementations to enhance Hadoop capabilities, and adding differentiating capabilities to make their solution more attractive.

In the big data Hadoop solution review, vendors include Amazon Web Services, Cloudera, Hortonworks, IBM, MapR Technologies, Huawei and Kuaikuai search. These vendors are all based on the Apache open source project and then add packaging, support, integration features and innovations of their own to compensate for Hadoop’s shortcomings in the enterprise. All vendors implement these features, though in slightly different ways — as evidenced by vendor scores and vendor profiles.

Dakuai Big Data Platform (DKH) is a one-stop search engine-level and general computing platform for big data designed by Dakuai Search in order to get through the channel between the big data ecosystem and traditional non-big data companies. By using DKH, traditional companies can easily overcome the technological gap of big data and achieve the performance of big data platform at the level of search engine.

DKH effectively integrates all components of the entire HADOOP ecosystem, and is deeply optimized and recompiled into a complete and higher performance general computing platform for big data, realizing the organic coordination of all components. Therefore, COMPARED with open source big data platform, DKH has up to 5 times (maximum) performance improvement in computing performance.

DKH simplifies the complex big data cluster configuration to three types of nodes (master node, management node and computing node) through the unique middleware technology of Dakuai, which greatly simplifies the management operation and maintenance of the cluster and enhances the high availability, high maintainability and high stability of the cluster.



DKH, although highly integrated, still retains all the advantages of the open source system and is 100% compatible with the open source system. Big data applications developed based on the open source platform can run efficiently on DKH without any changes, and the performance can be improved by up to 5 times.

Traditional business methods

In this approach, a business would have a computer to store and process big data. For storage, the programmer will do it with the help of the database vendor of his choice, such as Oracle, IBM, etc., and the user will interactively use the application to obtain and process the data storage and analysis.

limitations

This approach is perfect for large data applications that can be stored by a standard database server, or down to the limitations of the processor that processes the data. However, when it comes to handling large amounts of scalable data, this is a busy task that can only be processed through a single database bottleneck.

Google’s solution

Google solved this problem using an algorithm called MapReduce. The algorithm divides tasks into smaller pieces, distributes them to multiple computers, and collects and synthesizes results from these machines to form a result data set.

Hadoop

Using a solution provided by Google, DougCutting and his team developed an open source project called HADOOP.

The MapReduce algorithm used by Hadoop runs where the data is being processed in parallel with other applications. In summary, Hadoop is used to develop applications that can perform complete statistical analysis of big data.