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This article introduces you to the big data analysis platform system development ideas and steps, the content is very detailed, interested partners can refer to, I hope to help you.

1. Building a big data platform is inseparable from BI. BI has existed for a long time before big data, and it is obviously inappropriate to simply equate big data with BI. But the two are closely related and complement each other. BI is an application tool to achieve business management. Without BI, big data has no value conversion tool, and it cannot present the value of data to users, and it cannot effectively support enterprise management decisions. Big data is the foundation. Without big data, BI loses the foundation of existence, and there is no way to process data quickly, in real time and efficiently to support applications. Therefore, the value of data and the construction of big data platform must include big data processing and BI application analysis.

Big data has value. Take a look at the pyramid model of data usage. From the perspective of data usage, data is basically used in the following ways:

From top to bottom, you can see that the requirements for data are different:

The amount of data is getting larger and larger, and the dimensions are getting more and more.

Interaction is getting harder.

The technical difficulty is getting bigger.

The Lord is the Lord, and the Lord is the Lord.

The professional level of users is gradually improved, and the threshold is getting higher and higher.

The gradual improvement of enterprises 'requirements for data and efficiency also provides a platform for big data to show its ability. In the final analysis, the construction of big data platform by enterprises is to construct the data asset operation center of enterprises, give full play to the value of data and support the development of enterprises.

The overall plan is as follows:

Build the basic data center of the enterprise, construct the unified data storage system of the enterprise, and conduct unified data modeling to lay the foundation for the value presentation of data. At the same time, the data processing capacity sinks, and a centralized data processing center is built to provide powerful data processing capabilities; through a unified data management and monitoring system, the stable operation of the system is guaranteed. With the data foundation, build a unified BI application center to meet business needs and reflect data value.

When it comes to big data, it comes to hadoop. Big data is not the same as hadoop, but hadoop is indeed the hottest big data technology. Let's take a look at the most common mix architecture below to see how big data platforms can flexibly interface and adapt to various data source collections (such as integrated flume) through Kafka as the message management layer of the unified collection platform, providing flexible and configurable data collection capabilities. Using spark and hadoop technology, build the storage and processing capability center of the most core basic data of the big data platform, provide powerful data processing capabilities, and meet the interactive needs of data. At the same time, through spark streaming, it can effectively meet the requirements of real-time data of enterprises and build a real-time index system for enterprise development.

At the same time, in order to better meet the data acquisition requirements, RDBMS provides highly aggregated statistical data of enterprises, meets the requirements of regular statistical reports of enterprises, and lowers the threshold for use. For detailed query requirements of big data, HBase cluster is built to provide fast query capability of big data and meet query and acquisition requirements of big data.

The general big data platform from platform building to data analysis probably includes the following steps:

1, Linux system installation

CentOS, an open source version of Redhat, is generally used as the underlying platform. In order to provide a stable hardware foundation, RAID and data storage nodes for hard disks need to be configured according to the situation. For example, you can choose to make RAID2 for HDFS namenode to improve its stability, and place the data storage and operating system on different hard disks to ensure the normal operation of the operating system.

2. Distributed computing platform/component installation

Most of the current distributed systems use the Hadoop family of open source systems. At the heart of Hadoop is HDFS, a distributed file system. Common components on the basis of Yarn, Zookeeper, Hive, Hbase, Sqoop, Impala, ElasticSearch, Spark and so on.

Advantages of using open source components: 1) many users, many bugs can be found online answers (this is often the most time-consuming place in development);2) open source components are generally free, learning and maintenance is relatively convenient;3) open source components are generally continuously updated;4) because the code is open source, if bugs occur, the source code can be freely modified and maintained.

Commonly used distributed data warehouses are Hive and Hbase. Hive can query with SQL, and Hbase can read rows quickly. Sqoop is required to import and export external databases. Sqoop imports data from traditional databases such as Oracle and MySQL into Hive or Hbase. Zookeeper provides data synchronization services, Impala is a supplement to hive, which can achieve efficient SQL queries.

3. Data import

As mentioned earlier, the tool for data import is Sqoop. It can import data from files or traditional databases to distributed platforms.

4. Data analysis

Data analysis generally includes two stages: data preprocessing and data modeling analysis.

Data preprocessing is to prepare for the modeling analysis later. The main work is to extract available features from massive data and establish large-width tables. This process may involve Hive SQL, Spark QL, and Impala.

Data modeling analysis is to model the extracted features/data for preprocessing to get the desired results. As mentioned earlier, the best one to use is Spark. Commonly used machine learning algorithms, such as naive Bayes, logistic regression, decision trees, neural networks, TFIDF, collaborative filtering, etc., are already in ML lib, which is more convenient to call.

5. Result visualization and output API

Visual generalizations are used to present results or portions of raw data. There are generally two cases, row data display, and column lookup display.

About the big data analysis platform system development ideas and steps are what to share here, I hope the above content can be of some help to everyone, you can learn more knowledge. If you think the article is good, you can share it so that more people can see it.

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