Big Data development work hard?
Today's society is a society of rapid development, technology development, information flow and exchange between people getting closer, more and more convenient life, big data product is this high-tech era. Big data is not in the "big", but rather "useful." Value content, the cost of mining is more important than quantity. Therefore, it is particularly important for the development and analysis of data for a large enterprise. Big Data development of talent has become hot.
Although large data relevant personnel are very popular, but some people are concerned that a large data development after too much overtime, would be more difficult, all the more hesitant. Today, the number of joint education to brief a real case of large data development, understanding the development of large data Sim is not hard.
First, we understand the daily work of large data content development:
(1) responsible for big data product development, responsible for large data cleansing, store, process, architecture design and development scenario analysis;
(2) the use of statistical or machine learning algorithms, structured data for statistical analysis, including classification and clustering, and predictive modeling; communications and operations, products, research and development department, identify their needs and provide data analysis and statistics and other support;
(3) responsible for business database system architecture design, high-performance architecture including high concurrency, high availability architecture; depth optimization is responsible for the database, with the current architecture or design business scenarios feasibility optimization;
(4) Construction of large distributed data services platform, and building companies involved include mass data storage, off-line / real-time computing, real-time query large data systems operation and maintenance system.
The main work:
First, the participation of large data requirements research and related systems requirements analysis, writing technical documentation;
Second, the project outline design, detailed design, preparation and implementation of development plans;
Third, to build large data relating to system development environment, complete the code Storm, Kafka, Flume, Spark, Flink and other large data components to achieve functional tuning, source code interpretation; if you are interested in big data development, want the system to learn big data words can be added to the large data exchange technology to learn buttoned Junyang: 522 189 307 , welcome to add, to understand course descriptions, access to learning resources.
Fourth, complete technical guidance Storm, Kafka, Flume, Spark, Flink and other large data components, completion of the technical components of troubleshooting and repair work;
Five scenarios for real-time processing architecture to provide overall project development and optimization;
From the above point of view, the real work, the big data development needs to be step by step, but because of changes in demand, changes in project schedule, sometimes needs to work overtime. But overall, great data development of overtime hours is not very long.
