Today, many companies are trying to mine the large amount of data they have, including structured, unstructured, semi-structured and binary data, etc., to explore the in-depth use of the data.
Most companies estimate that they only analyze 12% of the data they have, leaving the remaining 88% underutilized. A large number of data silos and lack of analytical capabilities are the main reasons for this situation. Another challenge is how to judge whether the data is valuable. Especially in the era of big data, in order to avoid data loss you must collect and store this data. Some seemingly irrelevant data, such as cell phone GPS data, may also be of great use in the future.
Therefore, a large number of companies are pinning their hopes on using Hadoop to solve the following problems:
Collect and store all data related to the company's business functions. Supports advanced analytics capabilities, including business intelligence, for advanced visualization and predictive analysis of data in modern ways. Quickly share data with those who need it. Consolidate multiple data silos to answer complex questions that have never been asked before, or even unknown. Hadoop enables rapid and efficient scaling of solutions, enabling rapid processing of ever-increasing volumes, speeds, and diverse data.
The buying cycle for Hadoop is now on the upswing, thus spawning more and more vendors in the space. Although Hadoop is an open-source project from Apache that anyone can download for free, most consumers prefer the vendor's packaged solution. In addition to packaging all Hadoop components and ensuring their normal use (compatible versions), manufacturers generally provide enterprise-level support and extensions: Apache Hadoop (HDFS) is used as the core component of the solution, with additional implementations to enhance Hadoop functions , and adding differentiating features to make the solution more attractive.
In the evaluation of big data Hadoop solutions, vendors include Amazon Web Services, Cloudera, Hortonworks, IBM, MapR Technology, Huawei and DakuaiSearch . These vendors are all based on the Apache open source project, and then add features such as packaging, support, integration, and their own innovations to make up for the shortcomings of Hadoop in the enterprise. All vendors implement these features, albeit in slightly different ways—as evidenced by each vendor's evaluation scores and vendor profiles.
Da Kuai Big Data Platform (DKH) is a one-stop search engine-level, big data general computing platform designed by Da Kuai Search to open up the channel between the big data ecosystem and traditional non-big data companies. By using DKH, traditional companies can easily cross the technical gap of big data and realize the performance of big data platform at the level of search engines.
DKH effectively integrates all the components of the entire HADOOP ecosystem, and is deeply optimized and recompiled into a complete higher-performance big data general computing platform, which realizes the organic coordination of various components. Therefore, compared with the open source big data platform, DKH has a performance improvement of up to 5 times (maximum) in computing performance.
DKH, through the unique middleware technology of Dakuai, simplifies the complex big data cluster configuration to three nodes (master node, management node, computing node), which greatly simplifies the management and operation of the cluster, and enhances the High availability, high maintainability, and high stability of the cluster.
Although DKH has been highly integrated, it still maintains all the advantages of open source systems and is 100% compatible with open source systems. Big data applications developed based on open source platforms can run efficiently on DKH without any changes, and The performance will be improved by up to 5 times.
traditional business approach
In this approach, a business will have a computer to store and process big data. For storage, the programmer will complete it with the help of the database vendor selected by himself, such as Oracle, IBM, etc., and the user interacts with the application to obtain and process data storage and analysis.
limitation
This approach is perfect for large data applications that can be stored by a standard database server, or up to the limits of the processor processing the data. But when it comes to processing large volumes of scalable data, it's a hectic task that can only be processed by a single database bottleneck.
Google's solution
Google solved this problem using an algorithm called MapReduce. The algorithm divides tasks into small pieces, distributes them to multiple computers, and collects results from these machines and combines them to form the resulting dataset.
Hadoop
Using the solution provided by Google, Doug Cutting and his team developed an open source project called HADOOP.
Hadoop uses the MapReduce algorithm to run applications where data is processed in parallel using other applications. In conclusion, Hadoop is used to develop applications that can perform complete statistical analysis of big data.