Basic knowledge of big data technology

Hadoop definition: Hadoop is aOpen source distributed computing framework, it can beEfficiently store, process and analyze large-scale data in a cluster environment. ThatThe core component isHadoopDistributed file system (HDFS)andMapReduceComputing framework, they are all designed to deal with data scale above the PB level. HadoopNot only supports the storage and processing of massive data, can also support various data formats and programming languages ​​through extended methods. Hadoop has a wide range of applications, including search engines, social networks, e-commerce, finance and other fields.

Hadoop Features:Distributed storage and processing, scalability, high fault tolerance, adaptable to a variety of data types and processing methods, an open ecosystem。

• Distributed storage and processing: Hadoop adopts distributed storage and processing, that is, data and computing tasks are distributed on multiple nodes in the cluster. This method can achieve horizontal expansion of disk space and processing capabilities, thereby effectively coping with the storage and calculation of massive data.
• Scalability: Because Hadoop adopts a distributed architecture, cluster size can be scaled linearly as the amount of data increases, thereby improving processing performance and speed of completing tasks.
• High fault tolerance: Hadoop considered the problem of node failure at the beginning of its design and adopted multi-level fault tolerance strategies, such as data backup and task retry, to ensure the reliability of the data and the correctness of the calculation.
• Adapt to multiple data types and processing methods: Hadoop not only supports processing structured data, but also can process semi-structured and unstructured data. At the same time, Hadoop also supports a variety of processing methods, such as MapReduce, SQL query, stream processing, etc.
• Open ecosystem: Hadoop has a huge ecosystem that contains rich data storage and data processing tools. At the same time, Hadoop also supports the integration of other open source tools and frameworks, such as Spark, Hive, Pig, etc.

Hadoop application:Big data processing, log analysis, social network analysis, bioinformatics, financial analysis。

• Big Data Processing: Hadoop was originally designed to solve the storage and computing problems of large-scale data, so it has unique advantages in big data processing. For example, Hadoop can be used for ETL (extraction, transformation, loading), data warehouse, data mining and other aspects of massive data.
• Log Analysis: With the development of Internet technology, data generation is getting faster and faster, and various application systems are also generating more and more log data. Hadoop can collect, process and analyze these log data to support system management, operational optimization and security monitoring.
• Social network analysis: Social network platforms such as Facebook and Twitter will generate a large amount of user data. Hadoop can be used to process and analyze this data, such as user behavior analysis, social network modeling, recommendation algorithms, etc.
• Bioinformatics: Bioinformatics is a complex field that requires the analysis of large amounts of genomic and sequence data. Hadoop can be used to speed up this process, such as genome alignment, sequence modeling, drug research, etc.
• Financial analysis: Financial institutions such as banks and insurance companies generate massive amounts of data every day, which can be used to analyze risk assessment, monetary policy formulation, credit assessment and other aspects. Hadoop can help financial institutions process this data quickly and provide accurate predictions and suggestions.