FusionInsight Intelligent Data Lake - Huawei Enterprise

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FusionInsight Intelligent Data Lake

Intelligence for Agile Business

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Value Derived from Massive Data Accelerates Intelligent Transformation for Governments and Enterprises

HUAWEI CLOUD FusionInsight Intelligent Data Lake provides intelligent data lakes that are cost-effective and easy to build. It is used by over 3,000 customers in multiple industries in more than 60 countries and regions. The solution enables you to derive value from massive amounts of data. Application examples include e-government data governance and all-in-one network offices, real-time financial risk control, carrier BOM convergence, smart campuses for large enterprises, smart urban rail, and smart airports. FusionInsight includes cloud services such as MapReduce Service (MRS), GaussDB (DWS), Cloud Search Service (CSS), Graph Engine Service (GES), and DAYU. It is applicable to big data service scenarios such as offline analysis, real-time stream processing, real-time retrieval, and interactive query.

Advantages

Faster Building
- Building time for an all-cloud big data center is shortened from 22 days to 1 hour. Proactive O&M and quick response within 5 minutes are implemented.
Higher Resource Utilization
- Enterprise-level EC achieves a lower backup ratio (1:1.09). This helps to improve resource utilization to 91% and double data retention period.
Better Experience
- One copy of global data, one directory, and one interface implement data virtualization, simplifying data usage and improving analysis efficiency by 2 to 10 times. Over 1,000 refined metrics are displayed graphically and can be customized.

Architecture

HUAWEI CLOUD FusionInsight Intelligent Data Lake implements full-lifecycle (including data collection, storage, computing, management, and usage) and one-stop converged analysis on cloud. The solution supports installation and deployment and rolling upgrade for up to 20,000 clusters, without service interruption. Over 1,000 refined and visualized O&M metrics provide you with cluster management featuring high reliability, security, fault-tolerance, and ease-of-use.

MapReduce Service (MRS)

As a one-stop converged analysis platform of big data for enterprise-level customers, MRS runs big data components such as HDFS, Spark, Yarn, Hive, HBase, Flink, and Flume, implementing easy deployment and management, delivering high performance, convergence, and supporting sustainable evolution.

GaussDB(DWS)

An enterprise-level data warehouse in multiple modes, which is based on GaussDB. It is fully compatible with SQL standards, featuring high performance, high scalability, and high availability. GaussDB(DWS) is an enterprise-level kernel, allowing data collaboration without migration and enhancing hardware capabilities.

CSS (Cloud Search Service)

A fully hosted distributed search service powered by open-source Elasticsearch. It features searching, statistics, and reporting for structured and unstructured data, including text, time, digits, geographical locations, sounds, pictures, and videos.

Graph Engine Service (GES)

A graph-based service which facilitates querying and analysis of graph-structure data based on various relationships. It uses Huawei's in-house, high-performance graph engine EYWA, which is powered by multiple patented technologies. GES is specifically suited to scenarios requiring analysis of rich relationship data, including social apps, company relationship analysis, logistics, domain knowledge graph, and risk control.

DAYU

A platform providing one-stop data asset management, development, exploration, and sharing capabilities based on an enterprise data lake. The features help enterprises improve the efficiency in managing massive amounts of data, control their core data assets, and explore the value of data.

ModelArts

A one-stop AI development platform. It provides data preprocessing, semi-automated data labeling, distributed training, automated model building, and on-demand deployment of device-edge-cloud models to help AI developers build models quickly and manage the AI development lifecycle during machine learning and deep learning.

Case Studies

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