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Most people do not understand the knowledge points of this article "what technologies big data development engineers need to know", so the editor summarizes the following contents for you. The contents are detailed, the steps are clear, and they can be used for reference. I hope you can get something after reading this article. Let's take a look at this "what technologies big data development engineers need to know" article.

1. Prediction and analysis

Data analysis is one of the most important applications of big data. The ultimate purpose of all data is to get relevant conclusions and predictions through data analysis. A statistical or data mining solution that contains algorithms and techniques that can be used in structured words and unstructured data to determine future results. It can be deployed for many other purposes, such as forecasting, optimization, forecasting, and simulation. I believe you are already familiar with the software SPSS. Users can choose modules according to their actual needs and computer functions. The analysis results of SPSS are clean, intuitive, easy to learn and use, and read EXCEL and BDF data files directly, which has been extended to computers of various operating systems.

2. NoSQL database

Non-relational databases include Key- value (Redis) database, document (MonogoDB) database and Neo4j database. Although it has only been a year since the popularity of NoSQL buzzwords, the emergence of NoSQL database is to solve the challenges brought by large-scale data sets and multiple data types, especially the difficult problem of big data's application.

3. Search and Cognitive Business

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. That is, it is combined with the application fields related to artificial intelligence. Big data will become the cornerstone of artificial intelligence.

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 large data stream 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, supports big data, and is a very good choice.

8. Data integration

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

9. 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.

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