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1. Project Introduction
Open-source data visualization and analysis tools
Second, the implementation of functions
OLTP database
Online Transaction Processing (OLTP) databases are mainly used for fast processing of daily business transactions and have the characteristics of high throughput and low latency. Here are some common OLTP databases:
MySQL：
Features: open source, high performance, scalable.
Application scenarios: Widely used in web applications, content management systems, e-commerce platforms, etc.
Oracle：
Features: Powerful transaction management capabilities, high availability, and security.
Application scenarios: large enterprise applications, financial industries, and government agencies.
SQL Server：
Features: Good integration, close integration with the Microsoft ecosystem.
Application scenarios: internal information systems, ERP, CRM, etc.
PostgreSQL：
Features: Complex queries, rich data types, and ACID features.
Application scenarios: geographic information system (GIS), scientific computing, data analysis.
MariaDB：
Features: A branch of MySQL with good compatibility and optimized performance.
Application scenarios: website backend, log management, data storage, etc.
Db2：
Features: The relational database launched by IBM is mature and stable.
Application scenarios: banking, telecommunications, medical and health care, etc.
TiDB:
Features: Emerging distributed database, compatible with MySQL protocol, high availability and horizontal scalability.
Application scenarios: e-commerce systems with high concurrency and real-time analysis.
MongoDB-BI:
Features: Non-relational database, support document storage, easy to use BI tools.
Application scenarios: content management, configuration management, and big data applications.
OLAP database
The Online Analytical Processing (OLAP) database is mainly used for complex queries and data analysis, and is good at processing large amounts of data and performing multi-dimensional analysis. Here are some common OLAP databases:
ClickHouse：
Features: Columnar storage, fast read and write performance, suitable for large-scale data analysis.
Application scenarios: user behavior analysis, monitoring system, log analysis.
Apache Doris:
Features: Efficient multi-dimensional analysis capabilities, real-time data import.
Application scenarios: Internet advertising, operation reports, and data warehouse construction.
Apache Impala：
Features: Highly integrated with the Hadoop ecosystem, low-latency queries.
Application scenarios: real-time query of big data and data lake analysis.
StarRocks：
Features: High-performance OLAP engine, support real-time data update.
Application scenarios: real-time data analysis, user behavior analysis, and big data BI reports.
Data warehouses/data lakes
Data warehouses and data lakes are solutions for large-scale data storage and analysis, typically used in enterprise decision support systems.
Amazon RedShift：
Features: Fully managed cloud data warehouse service, supporting petabyte-level data storage, good scalability and performance.
Application scenarios: business intelligence (BI) reporting, data warehousing, data analysis.
Data files
Data files are the most basic form of storage and are suitable for storing and exchanging small-scale, structured or semi-structured data.
Excel：
Features: Store data in table form, support a variety of data operations, easy to use.
Application scenarios: data analysis, financial reporting, and project tracking.
CSV：
Features: Comma separated value file, easy to read, widely compatible.
Application scenarios: data import and export, backup, and cross-system data exchange.
API data sources
API data sources allow data to be exchanged between applications by providing a programming interface, making them ideal for dynamic data ingestion and real-time data interaction.
Features: High flexibility, access to real-time data, support for multiple formats (e.g. JSON, XML).
Application scenarios: weather information acquisition, social media data scraping, payment gateway integration.
summary
Different types of data storage and processing methods have their own advantages and disadvantages, as well as applicable scenarios. OLTP databases are designed for fast and reliable transaction processing and are suitable for scenarios with high concurrency and low latency. OLAP database is mainly used for large-scale data analysis, supporting complex queries and multi-dimensional analysis. Data warehouses and data lakes are large-scale data storage solutions that provide powerful data analysis and decision support for enterprises. Data files and API data sources provide easy data exchange and real-time data access.
3. Technology selection
SpringBoot
sight
MySQL, Oracle, SQL Server, PostgreSQL, MariaDB, Db2, TiDB, MongoDB-BI, Excel, CSV
Fourth, the interface display
5. Source code address
Private correspondence: 76