Shulou(Shulou.com)06/03 Report--

Big data has been integrated into various industries, which big data technology is the most popular? Which big data has great technological potential? Please listen to the teacher's introduction to the 10 hottest big data techniques.

(1) Predictive analysis

Predictive analysis is a statistical or data mining solution that contains algorithms and techniques that can be used in structured and unstructured data to determine future results. It can be deployed for many other purposes, such as forecasting, optimization, forecasting, and simulation. With the maturity of hardware and software solutions, many companies use big data technology to collect huge amounts of data, train models, optimize models, and issue prediction models to improve business level or avoid risks; at present, the most popular prediction and analysis tool is IBM's SPSS,SPSS, which integrates data entry, arrangement and analysis functions. Users can choose modules according to their actual needs and computer functions. The analysis results of SPSS are clear, intuitive, easy to learn and use, and can read EXCEL and DBF data files directly, which has been extended to computers of various operating systems.

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(2) NoSQL database

Non-relational databases include Key- value (Redis) database, document (MonogoDB) database and Neo4j database. Although NoSQL buzzwords have been popular for only one year, it is undeniable that the second generation movement has begun. Although the early stack code can only be regarded as an experiment, the current system has become more mature and stable.

(III) search and awareness of business

In the current era, big data and analysis have developed to a new height, that is, the cognitive era, the cognitive era is no longer a simple data analysis and display, it is more of a model that uses data to support human-computer interaction. For example, the go war some time ago, is a very good application, has been gradually extended to the application of robots, that is, the next economic flashpoint-artificial intelligence. Internet people are familiar with domestic BAT, as well as foreign apple, google, facebook, IBM, Microsoft, Amazon and so on. You can take a general look at their business layout, the future is all to the direction of artificial intelligence, of course, at present in the cognitive business of this IBM is the leader, especially the current main launch of watson this product, and achieved great results.

(4) flow analysis

At present, streaming computing is a hot topic in the industry. Recently, Twitter, LinkedIn and other companies have opened up streaming computing systems such as Storm, Kafka, etc., plus Yahoo! Before the open source S4, streaming computing research continues to heat up in the Internet field, and streaming analysis can clean, aggregate and analyze multiple high-throughput data sources in real time; the need for rapid processing and feedback of information flows in digital formats that exist in social networking sites, blogs, e-mail, videos, news, phone records, data transmission, and electronic sensors. At present, there are many × × analysis platforms, such as open source spark and ibm streams.

(5) memory data structure

Provide low-latency access and processing of massive data through distributed storage systems such as dynamic random memory access (DRAM), Flash and SSD

(6) distributed storage system

Distributed storage refers to a computing network with more than one storage node, multiple copies of data and high performance; using multiple storage servers to share the storage load and using location servers to locate and store information, it not only improves the reliability, availability and access efficiency of the system, but also is easy to expand. At present, the open source HDFS is still very good, friends in need can learn more about it.

(7) data visualization

Data visualization technology refers to the display of various types of data sources (including massive data on hadoop and real-time and near real-time distributed data); at present, there are many products for data analysis and display at home and abroad, if enterprises and government units suggest to use cognos, it is safe, stable, powerful, supporting big data and a very good choice.

(8) data integration

Business data integration through Amazon elastic MR (EMR), Hive, Pig, Spark, MapReduce, Couchbase, Hadoop and MongoDB software

(IX) data preprocessing

Data integration refers to cleaning, tailoring, and sharing diversified data to speed up data analysis.

(10) data verification

Check the massive and high-frequency data sets on the distributed storage system and database to remove the illegal data and fill in the missing. Data integration, processing, validation in the current collectively known as ETL,ETL process can be structured data and unstructured data for cleaning, extraction, conversion into the data you need, while ensuring the security and integrity of the data, on ETL products recommended to use datastage, for any data source can be perfectly handled.

Through the understanding of the above 10 hot big data technologies, we can also speculate the development trend of big data. Friends who want to learn from big data can also be used for reference.

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