"Big Data" has been booming in recent years, not only as a business trend, but also as a technological innovation that has changed human life. Big data is also becoming increasingly important to industry users. Mastering data assets and making intelligent decisions has become the key to stand out from the crowd. Therefore, more and more enterprises are beginning to pay attention to the strategic layout of big data and redefine their core competitiveness.

Domestic companies doing big data are still divided into two categories: one is now already have access to big data capabilities of the company, such as Baidu, Tencent, Alibaba and other Internet giants, as well as Huawei, Longchao, ZTE and other leading domestic enterprises, do big data to store a aba gi three ear lead a Surabaya two five zero, covering the field of data collection, data storage, data analysis, data visualization, and data security; the other is the start-up big data, data storage, data analysis and data security, etc.; and the other is the start-up big data. The other category is the start-up big data companies, they rely on big data tools, in response to market demand, to bring innovative solutions for the market and promote the development of technology. Most of these big data applications still require the services of third-party companies.

More and more applications involve big data, and the attributes of these big data, including volume, velocity, diversity and so on, are presenting the growing complexity of big data, so the method of analyzing big data is particularly important in the field of big data, and it can be said to be the decisive factor in deciding whether the final information is valuable or not. Based on this, what are the more popular products for analyzing big data?

And in this, the brightest star is Hadoop, Hadoop has been recognized as a new generation of big data processing platform, EMC, IBM, Informatica, Microsoft and Oracle have been put into the arms of Hadoop. For big data, the most important thing is still the analysis of data, from which to find valuable data to help enterprises make better business decisions. Below, let's take a look at the following top ten enterprise-level big data analytics tools.

With the explosive growth of data, we are being surrounded by all kinds of data. The correct use of big data will bring great convenience to people, but at the same time also bring technical challenges to the traditional data analysis, although we have entered the era of big data, but "big data" technology is still in its infancy, and further development to improve the big data analytics technology is still a hot spot in the field of big data.

In the current Internet field, the application of big data has been very extensive, especially in the enterprise, the enterprise has become the main body of big data application. Can big data really change the way businesses operate? The answer is undoubtedly yes. As businesses begin to utilize big data, we are seeing new and wonderful applications of big data every day, helping people to really benefit from it. The use of big data has become widespread in every aspect of our lives, covering a wide range of industries, including healthcare, transportation, finance, education, sports, retail, and more.

Visual Analytics

Big data analytics users have big data analytics experts, but there are also ordinary users, but both of them for big data analytics is the most basic requirements for visual analytics, because visual analytics can intuitively present the characteristics of the big data, and at the same time can be very easy to be readers to accept, as looking at the picture of the The first step in the process is to make it easier for the reader to understand what is going on in the data.

2. Data mining algorithms

The theoretical core of big data analytics is data mining algorithms, and various data mining algorithms are based on different data types and formats in order to more scientifically present the characteristics of the data itself, and it is precisely because of these statistical methods (called truths) recognized by statisticians all over the world, that we can y penetrate into the world of big data analytics. It is because of these various statistical methods, which are recognized by statisticians around the world as truths, that we are able to dig deeper into the data and uncover its recognized value. It is also because of these data mining algorithms that big data can be processed more quickly; if an algorithm takes years to come to a conclusion, then the value of big data is lost.

3. Predictive analytics

One of the ultimate applications of big data analytics is predictive analytics, where features are mined from big data and scientifically modeled, and then new data can be brought in through the model to predict future data.

4. Semantic engines

The diversity of unstructured data brings new challenges to data analysis, and we need a set of tools to systematically analyze and refine data. Semantic engines need to be designed with enough artificial intelligence to be sufficient to proactively extract information from data.

5. Data quality and data management. Big data analytics cannot be separated from data quality and data management. High-quality data and effective data management, whether in academic research or in business applications, can ensure that the results of the analysis are true and valuable.

The basis of big data analysis is the above five aspects, of course, more in-depth big data analysis, there are many, many more characteristic, more in-depth, more professional big data analysis methods.

Technology of big data

Data acquisition: ETL tools are responsible for extracting data from distributed, heterogeneous data sources such as relational data, flat data files, etc. to a temporary middle layer after cleaning, transformation, integration, and finally loaded into a data warehouse or data mart, which becomes the basis for online analysis and processing, data mining.

Data access: relational databases, NOSQL, SQL, and so on.

Infrastructure: Cloud storage, distributed file storage, etc.

Data Processing:

Natural Language

Processing (NLP, Natural Language

Processing) is a discipline that studies the linguistic aspects of human-computer interaction. The key to processing natural language is to make the computer "understand" natural language, so natural language processing is also called natural language understanding is also called computational linguistics. On the one hand, it is a branch of linguistic information processing, on the other hand, it is one of the core topics of artificial intelligence.

Statistical analysis:

? Hypothesis testing, significance testing, analysis of variance, correlation analysis, t-test, analysis of variance ,

chi-square analysis, partial correlation analysis, distance analysis, regression analysis, simple regression analysis, multiple regression analysis, stepwise regression, regression prediction and residual analysis, ridge regression, logistic regression analysis, curve estimation,

Factor Analysis, Cluster Analysis, Principal Components Analysis, Factor Analysis, Fast Clustering and Clustering Method, Discriminant Analysis, Correspondence Analysis, Multivariate Correspondence Analysis (Optimal Scale Analysis), bootstrap techniques, and more.

Data Mining:

Classification, Estimation, Prediction, Affinity

grouping or association rules, Clustering, Description and Analysis. Clustering), Description and Visualization, Description and

Visualization), Mining of complex data types (Text, Web ,graphic images, video, audio, etc.)

Model Prediction : Predictive Modeling, Machine Learning, Modeling Simulation.

Results Presentation : Cloud Computing, Tag Cloud, Relational Graph, etc.

Big Data Processing

1. One of the Big Data Processing: Capture

Capture of Big Data refers to the use of multiple databases to receive

data from the client (in the form of web, app, or sensors, etc.), and the user can use these databases to perform simple querying and processing tasks. For example, e-commerce companies use traditional relational databases such as MySQL and Oracle to store each transaction, and NoSQL databases such as Redis and MongoDB are also commonly used for data collection.

In the process of big data collection, the main feature and challenge is the high concurrency, because there may be thousands of users

at the same time to access and operate, such as the train ticket website and Taobao, their concurrent access to the peak of millions, so it is necessary to deploy a large number of databases in the collection side to support. And how to load balance and shard these databases

does require deep thinking and design.

2. Big Data Processing II: Import/Preprocessing

While there are many databases at the collection end, if you want to effectively analyze the massive data, you should import the data from the front end into a centralized large-scale distributed database or distributed storage cluster. And you can do some simple cleaning and preprocessing work on the basis of import. Some users also use Storm from Twitter to stream data during import to meet some of their real-time computing needs.

The import and preprocessing process is characterized and challenged by the sheer volume of data being imported, which often reaches hundreds or even thousands of megabytes per second.

3. Big Data Processing III: Statistics/Analysis

Statistics and analysis mainly use distributed databases, or distributed computing clusters to store massive amounts of data within the ordinary

analysis and classification summary to meet the majority of common analytical needs, in this regard, some of the real-time needs In this regard, some real-time needs will use EMC's GreenPlum, Oracle's Exadata, and

MySQL-based columnar storage Infobright, etc., and some batch processing, or based on the demand for semi-structured data can be used Hadoop.

Statistics and analytics of this part of the main features and challenges is the analysis of the data involved in the large volume of data. Its system resources, especially I/O will have a great occupation.

4. Big Data Processing IV: Mining

Different from the previous statistical and analytical processes, data mining generally has no predetermined theme, mainly in the existing data

data based on a variety of algorithms on top of the calculations, so as to play a prediction (Predict) effect, so as to achieve some high-level data analysis needs. Some high-level data analysis needs. Typical algorithms include Kmeans for clustering, SVM for

statistical learning, and NaiveBayes for classification, and the main tools used are Mahout for Hadoop. The process is characterized and challenged mainly by the fact that the algorithms used for mining are complex and

and the computation involves a large amount of data and computation, and the commonly used data mining algorithms are predominantly single-threaded.