Big data has been integrated into all walks of life. Which big data technologies are the most popular? What big data technologies have great potential? Please listen to the introduction of the 10 most popular big data technologies by the big podium teachers.
10 hottest big data technologies
(1) Forecast analysis
Predictive analytics is a statistical or data mining solution that includes 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 today’s hardware and software solutions, many companies use big data technologies to collect massive amounts of data, train models, optimize models, and publish predictive models to improve business levels or avoid risks; the most popular predictive analytics tool is IBM. The company’s SPSS, SPSS software is very familiar to everyone, it integrates data entry, sorting and analysis functions. Users can select modules according to actual needs and computer functions. The analysis results of SPSS are clear, intuitive, easy to learn and use, and can directly read EXCEL and DBF data files, and have been extended to a variety of operating systems.
In the process of getting started learning big data, I have encountered learning, industry, lack of systematic learning route, system learning planning, welcome to join my big data learning exchange skirt: 251956502, the skirt file has my big data learning manual compiled in recent years. , development tools, PDF document books, you can download them yourself.
(2) NoSQL database
Non-relational databases include Key-value (Redis) database, document (MonogoDB) database, and graph (Neo4j) database; although the NoSQL buzzword is only a year away, it is undeniable that it has now begun. The second generation of sports. Although the early stack code was only an experiment, the current system is more mature and stable.
(3) Search and cognitive business
In today’s 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 to rise to a use of data to support human-computer interaction Such models, such as the Go Wars of the previous period, are a good application and have been gradually extended to the application of robots, which is the next economic explosion point – artificial intelligence, Internet people are more familiar with domestic BAT, and abroad Apple, google, facebook, IBM, Microsoft, Amazon, etc.; can look at their business layout in general, the future is all toward artificial intelligence, of course, IBM is now the leader in cognitive business, especially the current The main push of watson this product, and achieved great results.
(4) Flow analysis
At present, streaming computing is a hot spot in the industry. Recently, companies such as Twitter and LinkedIn have opened up the streaming computing systems Storm, Kafka, etc., plus the open source S4 before Yahoo!, streaming computing research continues to heat up in the Internet field, streaming Analysis can perform real-time cleaning, aggregation, and analysis of multiple high-throughput data sources; in digital formats that exist in social networking sites, blogs, email, video, news, phone records, transmission data, and electronic sensors. The need for rapid processing and feedback of information flows. At present, there are many XX analysis platforms, such as open source spark, and ibm streams.
(5) Memory data structure
Provides low-latency access and processing of massive amounts of data through distributed storage systems such as Dynamic Random Access Memory (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 storage load and using location servers to locate storage information, it not only improves system reliability, availability, and access. Efficiency is also easy to expand. The current open source HDFS is still very good, and friends in need can have a deeper understanding.
(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); there are many products displayed at home and abroad for data analysis. If it is a corporate unit and a government unit, it is recommended to use cognos. , safe, stable, powerful, support big data, very good choice.
(8) Data integration
Business data integration through Amazon Elastic MR (EMR), Hive, Pig, Spark, MapReduce, Couchbase, Hadoop, and MongoDB;
(9) Data preprocessing
Data integration refers to cleaning, tailoring, and sharing diverse data to accelerate data analysis;
(10) Data verification
Data verification of massive and high-frequency data sets on distributed storage systems and databases, eliminating illegal data and completing missing. Data integration, processing, and verification are now collectively referred to as ETL. The ETL process can clean, extract, and transform structured data and unstructured data into the data you need, while also ensuring data security and integrity. Products on ETL are recommended to use datastage, which can be handled perfectly for any data source.
Through the understanding of the above 10 popular big data technologies, we can also speculate on the development trend of big data. Friends who want to learn big data can also be used for reference.