big data analytics software

, actions, and their relationships in the real world. What are the things in the real world, what actions should these things have, and what is the relationship between them, in the software world, we should design classes, methods, and associations between them. Only such a design is the most understandable design. This is the idea of "domain-driven design" [1]. In system restructuring, we will use the "Extraction Method" to break down the

should correspond to the things, actions, and their relationships in the real world. What are the things in the real world, what actions should these things have, and what is the relationship between them, in the software world, we should design classes, methods, and associations between them. Only such a design is the most understandable design. This is the idea of "domain-driven design" [1]. In system restructuring, we will use the "Extraction Meth

Big data refers to the analysis of massive data processing, may be the number of EB-level processing, we have previously mentioned that big Data has 4V features, Volume (Large), Velocity (high Speed), Variety (multiple), value (value), for the analysis of

non-join operations are in progress.Summary and ProspectFor big data analytics projects, technology is often not the most critical, and the key is who has a stronger ecosystem, and technically a momentary lead is not enough to ensure the ultimate success of the project. For Hive, Impala, Shark, Stinger, and Presto, it's hard to say which product will be the de f

Apache HadoopHadoop is now in its second 10-year development, but it is undeniable that Hadoop has developed in the 2014, with Hadoop moving from test clusters to production and software vendors, which is increasingly close to distributed storage and processor architectures, so This momentum will be more intense in 2015 years. Because of the power of the big Data

ability, machine learning ability under the guidance of algorithm, such as neural network (nonlinear regression), neural network is a very fire model in learning algorithm.The distinction between data mining and machine learning is:Data mining problems generally have huge data, especially the problem that the computational efficiency is more important than the statistic precision, usually stand in the comm

requirements Three key attention seats How to find the direction of investment? Nature needs to continue to the stock market summary, the market analysis, the target unit of follow-up to get. In the final analysis, the data collected and collated in the direction of investment often require the user to prepare for collection and collection of data at any time. Easy to focus on all aspects of

barsRealTime Druid–a Real time OLAP data store. Operationalized Time series Analytics databases Pinot–linkedin OLAP data store very similar to Druid.Data AnalysisThe analysis tools range from declarative languages like SQL to procedural languages like Pig. Libraries on the other hand is supporting out of the box implementations of the most common

with only 300 companies under 5000 who are Hadoop users. Considering that the total number of small and medium-sized companies is 10 times times that of large companies, this means that Hadoop's share in the big company market is 10 times times that of small and medium-sized companies. Most companies that use Hadoop are themselves high-tech data-driven companies. But we don't know why small companies have

big data Services for AWS, Azure and Google. Amazon Web Services AWS offers a very broad range of big data services. For example, Amazon elastic MapReduce can run Hadoop and Spark, while Kinesis Firehose and Kinesis Streams provide a way to import large datasets into AWS. Users can store

. Operationalized Time series Analytics databasesPinot–linkedin OLAP data store very similar to Druid.Data AnalysisThe analysis tools range from declarative languages like SQL to procedural languages like Pig. Libraries on the other hand is supporting out of the box implementations of the most common data mining and machine learn ing libraries.ToolsPig–provides a

improve the processing ability of the whole system by improving the computing ability of the single node, just like the diesel locomotive can not increase to 200 km/h Fabric-based computing provides a solid material base for the big data security analytics platform.MassiveBased on the "Harmony number" EMU and its integrated system, China's high-speed railway has

(Content-based recommendations, collaborative filtering, such as matrix decomposition, etc.)Then test on the public data set to see how the implementation works. A large number of public datasets can be found on the following Web site: UCI machine learning repository/3. Familiar with several open source tools: Weka (for getting started); LIBSVM, Scikit-learn, Shogun4. Take a few 101 races on Kaggle:go from Big