FarData

Big Data Platform

业务痛点

金融领域的数字化建设常常需要应对业务场景复杂、数据量庞大、系统间关联性强等特点。在保证开发质量以及满足合规风控等要求的前提下，还需要提高开发效率来快速响应市场变化，故而十分迫切需要代码辅助工具来为企业降本增效。

技术路线问题

IT架构开始向分布式架构转型升级，原有固化的技术标准和实施路线愈发难以支撑新时期的IT基础设施建设。

复杂架构问题

IT架构转型升级需要可高效支撑超复杂系统、多样化技术栈和异构共生的数字信息基础设施。

实践验证问题

支撑现代全融行业超大规模、超复杂应用的数字信息基础设施需在关键应用和重大工程中实践验证。

IT资产继承问题

新IT系统建设需提前考虑新建的IT资产在未来将面临的被继承的问题，以减少重复建设和资源浪废。

Product Overview

FarData is a big data development and data governance platform used to build data middleware and applications for governments and enterprises, aiming to reduce technical difficulty of big data application and development, as well as increase development efficiency. The product can be applied in a broad range of industries, including finance, internet, transportation, logistics, e-commerce, and manufacturing. It is capable of building real-time and offline data lakes to provide enterprises with lake-warehouse integrated big data solutions. The platform contains such components as Spark, Flink, Presto, Hive, and HBase that support different scenarios, including the real-time and offline analysis of full load, data middleware, big data development platforms, real-time data warehouses, and data governance.

Functional Features

Data exchange

Support data exchanges between heterogeneous databases, log data acquisition, and real-time synchronization of databases
Support data acquisition via message system transmission
Support data migration in the cloud connection of corporate applications and between relational databases
Support real-time database synchronization in real-time data warehouse scenarios

Data development

The data development platform supports task development such as online batch processing and real-time stream processing, and builds complicated DAG scheduling dependencies.
Script development supports online script writing and the visual display of database information.
Workflow development supports generating DAGs through dragging, pulling and releasing DAGs to the workflow scheduling platform for real-time scheduling.

Data quality management

Data quality services are used to check the uniqueness, completeness, consistency, and other aspects of data quality.
Application scenario 1: After completing data acquisition tasks, it checks whether the data entries and contents are consistent between the source and target sheets, helping to identify data issues more quickly and enhance data accuracy and credibility.
Application scenario 2: Before regulatory data submission, it checks whether the data quality meets industrial data standards.

Metadata management

By acquiring and analyzing metadata, it clarifies relationships between data and displays the 360-degree corporate data asset view from multiple perspectives.
It supports tracing data sources and processing workflows.

Data services

Quickly generate data API and manage their lifecycle
Provide API hosting services
Provide communications of high performance, high security, and high reliability and guarantee the security of internal system
Meet enterprises’ need for opening business to external partners and integrating communications between heterogeneous systems in the intranet

Data visualization

The data visualization platform is easy to learn and use. It performs the diverse display and interactive analysis of reports effortlessly, significantly shortening project timelines and reducing implementation costs.
Support online creation of reports and real-time search, etc.;
Support creation of charts in various forms, including pie charts, line charts, and maps;
Support creation of dynamic, large data screens through component combinations; meet the needs in various application scenarios with visual configuration, flexible layout, and quick release; with self-adapt to multi-screen display.

Product Advantages

High Performance and Stability

The data acquisition platform features high performance and stability: Equipped with high-performance transmission and storage, it supports highly reliable data acquisition at a ten-billion level daily.

Real-time data warehouse

Support real-time data warehouse: Using a massively scalable parallel processing (MPP) architecture, it supports real-time massive data writing, updating, and analysis and is capable of responding to hundreds of billions of queries in seconds.

Workflow scheduling system

The workflow scheduling system features high reliability, scalability, and ease of use. High reliability: The decentralized multi-Master and multi-Worker design prevents single points of failure. It supports the scheduling of 500,000 workflows daily.

Continuous concurrency

Continuous concurrency: The average response time for business operations is less than 2 seconds, and that for complex operations is less than 8 seconds.

应用场景

主要为政府和企业客户构建一个通用的大数据平台，可广泛应用于政务、金融、互联网、房地产、物流、零售、智慧城市和制造业等行业核心分析系统，有效降低大数据应用开发技术难度，提高开发效率达36%以上。主要应用场景：

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应用场景