Author: Zen and the Art of Computer Programming
1 Introduction
The data analysis platform is a tool for enterprises in data processing, exploratory data analysis, etc., and it plays an increasingly important role. As Internet companies' demand for data analysis increases, more and more data-related products and services are emerging. Different types of enterprises require data analysis platforms to maximize the value of data.
The architecture of the data analysis platform is a very important link, including three levels: data source management, data integration, and data visualization. Among them, data source management includes data collection, data storage, data cleaning and other functions, which can help enterprises improve efficiency and improve data quality; data integration focuses on how to integrate different types of data to form a data set for analysis; data Visualization is to present data to users through charts, reports, etc., to assist them in completing decision-making tasks. These levels will be closely related and affect the enterprise's data analysis experience at all times. Therefore, the design of the architecture will definitely bring huge business value.
This article will focus on the data integration in the design of data analysis platform architecture. First, it will introduce the role of data integration, then introduce the design elements of the data integration framework, and finally summarize the problems and solutions that may be encountered in the design of the data integration framework.
2. Introduction to data integration
Data integration refers to bringing together data from different data sources for data analysis and modeling. The data integration framework consists of four levels: data source management, data integration, data warehouse, and data services. The functions of each level are as follows:
Data source management: Responsible for data collection, data storage, data cleaning, etc., which can help enterprises improve efficiency and data quality. It mainly includes the following modules:
- Data access module: integrates all original data, provides a unified data interface to the outside world, and reduces data complexity.
- Data flow module: Responsible for moving data from various sources (such as various systems) to a unified location.
- Data mapping module: Responsible for converting data between various data models.
- Data verification module: ensure the correctness of data and avoid low data quality.
Data integration: This layer is the core link of data integration and is responsible for integrating, matching, and converting data from different data sources. It mainly includes the following modules:
- Data synchronization module: Collect source data in real time and keep it consistent with target data.
- Data routing module: Automatically select appropriate integration strategies based on rules to reduce human intervention and ensure data accuracy.
- Data standardization module: Standardize heterogeneous data based on a certain schema or set of schemas.
- Data cleaning module: remove abnormal data and integrate the data into a whole to avoid interference.
Data warehouse: This layer is subject-oriented modeling, responsible for storing the data required by the enterprise, and supporting applications such as business analysis, data mining and reporting. It mainly includes the following modules:
- Dimensional modeling: Build a data model to describe the logical structure and connections of enterprise data.
- ETL: Extract, transfer, load data, and convert source data into a format that can be analyzed and used.
- OLAP: Multidimensional data analysis, used for rapid query, analysis, and reporting of multidimensional data sets.
- BI: Integrate data analysis reports and create dashboards with intuitive visualization effects.
Data service: This layer supports data application development, data result output, etc., and provides data integration results through interfaces. It mainly includes the following modules:
- Data portal: Provides a user interface for data query, analysis and reporting.
- API: API interface that provides data services.
- Data sharing: Allow multiple departments to share data sets, reduce duplication of development, and promote information sharing.
The above are the design elements of the data integration framework. Next, we explore some key issues of data integration in more detail.
3. Key issues in data integration
There are many key issues with data integration, two of which are described below.
1. Data stratification
For enterprises, the classification and division of data are often complex, which requires the use of different layering methods to achieve data integration. There are three commonly used hierarchical methods: by subject, by field, and by business area. By subject, similar data is assigned to the same level. By field, the data is divided according to functions and attributes. By business field, data is divided into fine-grained parts based on the strategic needs of the enterprise.
However, deciding which layering approach to use is not a simple matter. First, data is often not fixed. Over time, data may change, and this change often leads to changes in the logic or structure of the data. Secondly, different levels have unique requirements for enterprise data integration. For example, data integration at the topic level often relies on early business understanding and analysis capabilities, while data integration at the business domain level requires later data analysis and analysis. Modeling capabilities. Furthermore, data at different levels often have different life cycles. For example, data at the subject level is generally retained for a longer period of time, while data at the business domain level may become invalid in the short term. Therefore, it is crucial to flexibly define and adjust data integration strategies at different levels.
2. Data sharing
Another key issue in data integration is data sharing. Data sharing between different departments not only means sharing information, but also creates challenges when sharing data. For example, data sharing is closely related to data utilization, and data sharing will also introduce data privacy risks. There are many ways to share data, such as direct sharing, subscription sharing, third-party data markets, etc. Each method needs to consider factors such as security, efficiency, and cost in different scenarios. Therefore, how to design a good data sharing strategy is the key to data integration.
4. Data integration framework design elements
After discussing the key issues of data integration, let's look at the elements of data integration framework design. The three core links of the data integration framework are data integration, data warehouse and data services. The data integration framework can be subdivided into five levels: data source management, data integration, data collection, data flow, and data mapping. Below we introduce the design principles of these elements respectively.
1. Data source management
The goal of the data source management layer is to design a complete, highly available data collection, storage, cleaning and integration solution. The modules of the data source management layer include:
Data access module: This module is responsible for providing a unified data interface to the outside world, including components such as protocol conversion, sampling, filtering, and rule engines. Provide a unified API interface to the outside world, reduce the complexity of data, and improve the efficiency of data integration.
Data flow module: The data flow module is responsible for moving data from various sources (such as various systems) to a unified location. Including data collection, log collection, event collection and other modules, it can perform data sampling, filtering, enhancement, conversion and other operations. The task of the data flow module is also to reduce the complexity of data processing and improve the efficiency of data integration.
Data mapping module: The data mapping module converts data between various data models. Including data standardization, format conversion, relationship mapping and other modules, it can convert data between different data models into a unified data model.
Data validation module: The data validation module ensures the correctness of data and avoids low data quality. Including data consistency, data validity and other modules, it can detect whether the data meets expectations and correct errors through data modification, deletion, supplementation, etc.
The characteristics that data source management should have are the following:
Comprehensive data: Data source management should provide comprehensive coverage of all source data. Data source management will not be optimal if it only focuses on certain types of source data.
Data accuracy: Data source management should be able to clearly identify and capture all data, correctly annotate data metadata, and use appropriate collection frequency and sample quantity according to different business scenarios.
Data is valid: Every attempt at data source management is a failed attempt, and it takes trial and error to finally obtain valid and correct data.
Data availability: Data source management should ensure data integrity and availability. In addition to ensuring the availability of original data, data source management should also improve data processing performance and adopt strategies such as hierarchical storage and hot and cold separation to ensure efficient data access and integration.
Data automation: Data source management should adopt automated methods to perform data collection, storage, cleaning, verification and other processes to reduce manual intervention as much as possible and speed up the data integration process.
2. Data integration layer
The goal of the data integration layer is to establish a complete data integration system based on the data source management layer to support applications such as business analysis, data mining, and reporting. The modules of the data integration layer include:
Data synchronization module: The data synchronization module collects source data in real time and keeps it consistent with the target data. Including master-slave replication, incremental replication, change data capture and other modules. The data synchronization module can ensure data accuracy and provide data support for business analysis.
Data routing module: The data routing module automatically selects appropriate integration strategies based on rules to reduce human intervention and ensure data accuracy. Including rules engine, data matching, triggers, label routing and other modules. The data routing module can automatically select the appropriate integration strategy based on business needs and data sources, avoiding the tedious process of manual configuration.
Data standardization module: The data standardization module standardizes heterogeneous data based on a certain pattern or set of patterns. Including modules such as field mapping, pattern matching, and entity recognition. The data standardization module can unify various heterogeneous data into a data model to facilitate subsequent data processing.
Data cleaning module: The data cleaning module removes abnormal data and integrates the data into a whole to avoid interference. Including anomaly detection, anomaly filling, missing value calculation, field standardization and other modules. The data cleaning module can eliminate unreasonable or invalid data and provide effective data for subsequent data analysis.
The characteristics that the data integration layer should have are the following:
Data integration specifications: The data integration layer should follow the data integration specifications to ensure data quality and consistency. Including specifications such as data types, data constraints, partitioning mechanisms, and indexing mechanisms. The data integration layer should strictly abide by data naming conventions and use consistent naming methods to identify data.
Flexible data integration: The data integration layer should be able to drive the data integration process through dynamic mechanisms such as rule engines and label routing, and flexibly respond to business changes. At the same time, the data integration layer should be able to identify the relationships between business data and map the relationships.
Data integration is controllable: The data integration layer should keep improving the integration process to ensure the controllability of the integration process. Including modules such as process visualization, process audit, and audit log. The data integration layer should provide a good integration control mechanism to ensure the safety and reliability of the data integration process.
Data integration automation: The data integration layer should use automated methods to perform the data integration process to achieve a high degree of automation. At the same time, the data integration layer should monitor and manage the automatic integration process to ensure that the quality of data integration is stable and controllable.
3. Data warehouse layer
The goal of the data warehouse layer is to build a unified, easy-to-use, and integrated subject data model to support enterprise data analysis, mining, reporting and other needs. The modules of the data warehouse layer include:
Dimensional modeling: The dimensional modeling module builds a data model to describe the logical structure and connections of enterprise data. Including fact tables, dimension tables, star dimensions, snowflake dimensions, etc. The purpose of the dimensional modeling module is to reduce the difficulty of data modeling and improve data analysis capabilities.
ETL: Extract, transmit, and load data modules extract, transmit, and load data, and convert source data into a format that can be analyzed and used. Including ETL components, connection pools, etc. ETL components can convert large batches of data into easy-to-use forms and improve data integration efficiency.
OLAP: Multidimensional data analysis module multidimensional data analysis, used for rapid query, analysis, and reporting of multidimensional data sets. Including MOLAP, ROLAP, DSS, etc. MOLAP supports small-scale low-latency queries, ROLAP supports large-scale high-volume queries, and DSS supports data mining and analysis.
BI: Data analysis report module data analysis report to create a dashboard with intuitive visualization effects. Including data display components, statistical analysis components, query components, etc. The data display component can visually present business data to assist corporate decision-making.
The characteristics that the data warehouse layer should have are the following:
Data integration friendly: The data warehouse layer should take into account the needs of data analysis and data integration to meet the various data analysis needs of the enterprise. The data warehouse layer needs to be able to support multiple data models, including time series, dimensions, text, images, etc.
Data analysis efficiency: The data warehouse layer should support different levels of analysis queries, including real-time query, offline query, cross analysis, combined analysis, etc. The data warehouse layer should have flexible data query capabilities and support complex query languages. At the same time, the data warehouse layer should support high-performance analytical queries, be real-time and scalable.
Data analysis is scalable: The data warehouse layer should be scalable and able to support queries of massive data volumes. At the same time, the data warehouse layer should have security, reliability, and high availability capabilities.
Data security: The data warehouse layer should ensure data security. Including identity authentication, authorization control, encrypted transmission and other technologies. The data warehouse layer should be able to identify, track, isolate malicious attackers, and protect data from attacks.
4. Data service layer
The goal of the data service layer is to provide a series of services for data application development, data result output, etc. The modules of the data service layer include:
Data Portal: The data portal module provides a user interface for data query, analysis and reporting. Including user rights control, data browsing, data export, data reports, data integration and other modules. The data portal module can improve the user experience of data applications and enhance the value of data applications.
API: The API module provides an API interface for data services. Including RESTful interface, RPC interface, MQ interface, etc. The API module allows external systems to obtain data integration results by calling the interface.
Data sharing: The data sharing module allows multiple departments to share data sets, reducing duplication of development and promoting information sharing. Including data integration scheduling, data subscription and other modules. The data sharing module can support collaboration and information sharing between different departments.
Data access center: The data access center module integrates all original data and provides a unified data interface to the outside world to reduce data complexity. It includes modules such as data access center, data specification, data governance, and data sharing. The data access center module can provide unified access channels and data specifications to improve the efficiency of data integration.
The data service layer should have the following characteristics:
Comprehensive services: The data service layer should provide a series of services such as data application development and data result output. The data service layer should provide data application interfaces, including RESTful interfaces, RPC interfaces, MQ interfaces, etc. At the same time, the data service layer should provide data visualization components, data analysis components, etc.
Efficient service: The data service layer should support high concurrent requests and have high throughput. At the same time, the data service layer should have good service stability and availability.
Low service cost: The data service layer should have low maintenance costs and can reduce deployment costs. At the same time, the data service layer should be able to expand on demand to meet business growth needs.
Service security: The data service layer should have a safe and reliable operating environment and provide sufficient security protection capabilities. The data service layer should have security protection mechanisms such as identity authentication, authorization control, and encrypted transmission to protect data from threats.