Application Scenarios

  • Cloud Migration

  • IoV

  • Finance & Insurance

  • Opinion Analysis

  • Smart Logistics

  • IoE

  • Smart Water Mgmt

  • Gaming

  • Energy

Cloud Migration

Quick Cloud Migration

You can quickly migrate big data platforms, such as IDC, CDH,
Hontonwoks, and FusionInsight, or other big data cloud service platforms to MRS.
With MRS you can move all of your services and data to the cloud in a few steps,
and you can future proof your enterprise by using the cloud environment as a
foundation to rapidly construct your off-cloud systems.


Advantages

Compute-Storage Decoupling

With MRS, compute and storage are decoupled so you can leverage multi-core
Kunpeng features to deploy your services more cost-effectively.

Unified Data Storage

Data silos will no longer exist with unified data storage and
multiple types of compute engines. One copy of data can be shared and analyzed
for multiple services.


Elastic Scaling

You can combine multiple types of compute and storage resources and configure
automatic scale in or out, greatly reducing the cost of migrating to the cloud.


Open Source Compatibility

MRS is fully compatible with open-source APIs. During migration,
your services are not affected and no service code needs to be modified. 

Simple and Fast Migration

A complete set of migration tools are provided for you to quickly
complete migration without interrupting services.

Related Services

IoV

Internet of Vehicles (IoV)

MRS leverages the open-source ecosystem to provide a fast and efficient data processing computing engine that helps automobile enterprises quickly migrate services to the cloud. It also flexibly builds an open, unified, and full-stack big data platform for data analysis.


Advantages

Unified, Full-Stack, Scalable Data Platform
MRS is an enterprise-level big data platform that isolates computing from storage, providing flexibility and convenience.


Multi-engine Processing for Hybrid Loads
MRS provides various open-source components that can be combined freely as needed, supporting real-time/offline complex service processing.


High Performance at a Low Cost
MRS Kafka and Storm can obtain real-time stream data to implement real-time computing and analysis with high throughput and low latency.


Compatible with Open-Source Standard APIs
MRS is based on open source. It is fully compatible with APIs of the Apache Hadoop ecosystem.


Related Services

Finance & Insurance

Finance and Insurance

MRS leverages the advantages of the big data platform on DeC to meet the strict requirements of the insurance industry with regards to compliance, security, and reliability. It reconstructs the IT architecture of traditional insurance enterprises, and quickly builds and deploys insurance service systems. This helps insurance enterprises achieve fast digital transformation, easy service innovation, and agile service evolution.


Advantages

Robust Security
Meets industry regulatory requirements and protects customers' sensitive data.


Dedicated Resources
Provides dedicated MRS clusters and exclusive resources, and separates computing resources from storage resources.


Flexible Creation, Full-Stack, Easy O&M
Allows users to create a full-stack big data platform with just one click and provides an enterprise-class platform management interface, simplifying O&M.


Related Services

Opinion Analysis

Public Opinion Analysis

Centering on the MRS one-stop big data platform, components such as Kafka, Storm, and HBase are used to collect, analyze, and process massive amounts of data to provide governments and enterprises with services such as public opinion monitoring, analysis, and message push.


Advantages

Flexible and Cost-effective
MRS supports elastic expansion of compute nodes and scheduled auto-scaling, significantly reducing costs.


Real-time and Efficient
MRS Kafka and Storm provide high-performance streaming cluster capabilities to collect and analyze tens of thousands of public opinions per second in real time, keeping delay within milliseconds.


PB-level Storage
MRS HBase provides the distributed and scalable NoSQL data storage services, which are suitable for analyzing and querying massive amounts of structured and semi-structured data.


Related Services

Smart Logistics

Smart Logistics

MRS big data analysis platform is used to implement intelligent management of logistics activities, improving service operation efficiency and greatly reducing costs.


Advantages

High Throughput and Low Latency
Dedicated MRS Kafka clusters with high-throughput, high availability, and low latency capabilities facilitate real-time access of millions of messages.


Large-scale Data Analysis and Processing
MRS Spark supports large-scale data computing. MRS HBase can load and update logistics data in milliseconds, and query and analyze petabytes of time series data.


More Intelligence with AI

MRS uses AI for big data mining, and provides precise and intelligent prediction and analysis capabilities for logistics organizations, marketing, and operation management.


IoE

Internet of Elevators (IoE)

To adapt to the rapid business innovation and flexible service modes involved in IoE, MRS functions as an open one-stop big data processing platform to implement intelligent elevator management.


Advantages

Open and Flexible
MRS provides a wide variety of computing products and storage hardware for your choices to meet scenario-specific needs and build a unified and open big data platform. In addition, it offers powerful computing engines and a massive amount of storage, and enables flexible integration of service components.


High Performance and Large Capacity
MRS Kafka works with high-performance general network enhancement (C3ne) ECSs to implement real-time data access for millions of elevators.


AI Supported
MRS supports GPUs that provide real-time high-speed parallel computing and floating-point computing capabilities. These are applicable to video encoding and decoding, deep learning, and scientific computing.


Related Services

Smart Water Mgmt

Smart Water Management

MRS Hadoop provides high-performance, reliable, unified big data storage and analysis functions for water management scenarios.


Advantages

Unified and Scalable Data Platform
MRS builds an enterprise-level big data platform and provides various open-source components that can be flexibly combined, supporting real-time/offline complex service processing.


High Throughput and Low Latency
Storm can obtain real-time stream data from Kafka to implement real-time computing and analysis with high throughput and low latency.


Integration of Various Types of Data
Various structured, semi-structured, and unstructured data can be computed and processed, and traditional data warehouse data can be easily migrated, facilitating cross-source data exploration and analysis.


Related Services

Gaming

Gaming

Game log data is accessed through Kafka and Flume in real time. Spark Streaming processes and analyzes the data in real time and stores the analysis results to HBase or Hive for quick game advertisement analysis, data query and analysis, and revenue analysis.


Advantages

Unified and Scalable Data Platform
MRS functions as an enterprise-level big data platform and provides various open-source components that can be combined flexibly to meet even highly complex service processing needs.


Real Time and High Throughput
MRS Kafka and Flume collect real-time data and integrate with high-performance general network enhancement (C3ne) ECSs to implement real-time access of massive amounts of data.


Energy

Energy

MRS provides enterprise-level big data cloud services that enable PV plant operators to easily run Hadoop, Spark, HBase, Storm, and other big data components for the purpose of predictive device maintenance.


Advantages

Unified Big Data platform
MRS functions as an enterprise-level big data platform and provides various big data components that support flexible use in different combinations, meeting customers' requirements for complex service processing.


Mass Data Collection
MRS Kafka and Sqoop support multiple data collection methods, facilitating real-time access of millions of messages.


Easy of Use
SQL APIs can be used to support querying multi-dimensional data for easy data exploration and analysis.


Related Services

Scenario-Specific Clusters

Hadoop Analysis Cluster

Analysis of vast amounts of data

Uses Spark to analyze and query vast amounts of data and uses Hive to analyze TB/PB-level data.

HBase Cluster

Massive data storage

Uses HBase to store massive amounts of data and query data in milliseconds.

Kafka Cluster

Low-latency streaming processing

Uses Flume to perform real-time data ingestion, Kafka to implement real-time access of tens of thousands of data records, and Storm to achieve reliable, fault-tolerant, and low-latency online service data processing.

Product Advantages

Product Advantages

  • Enterprise-class

    With one-click cluster installation, deployment, and capacity expansion, you do not need to worry about purchasing and maintaining hardware. Visualized enterprise-level cluster management allows for real-time node status monitoring and provides SMS notifications when there are alarms. Needed service patches are provided in a timely manner and can be installed in a single click.
    With one-click cluster installation, deployment, and capacity expansion, you do not need to worry about purchasing and maintaining hardware. Visualized enterprise-level cluster management allows for real-time node status monitoring and provides SMS notifications when there are alarms. Needed service patches are provided in a timely manner and can be installed in a single click.
  • Storage-compute Decoupling

    Take advantage of the HUAWEI CLOUD On OBS solution. Powered by a comprehensive big data engine, OBS is fully compatible with open-source APIs, and you can use Huawei-developed Kunpeng servers. The multi-core and high-concurrency capabilities that Kunpeng provides increase hardware compute performance and reduces costs.
    Take advantage of the HUAWEI CLOUD On OBS solution. Powered by a comprehensive big data engine, OBS is fully compatible with open-source APIs, and you can use Huawei-developed Kunpeng servers. The multi-core and high-concurrency capabilities that Kunpeng provides increase hardware compute performance and reduces costs.
  • High Elasticity

    You can select different flavors and deployment configurations running on Kunpeng or x86, and hybrid deployments of bare metal servers and VMs are supported within a single cluster. Tasks in peak and off-peak hours can be automatically scaled based on preconfigured policies.
    You can select different flavors and deployment configurations running on Kunpeng or x86, and hybrid deployments of bare metal servers and VMs are supported within a single cluster. Tasks in peak and off-peak hours can be automatically scaled based on preconfigured policies.
  • High Security

    Kerberos authentication management and fine-grained enterprise management allow you to manage cluster permissions by project. Multi-tenant management is supported and compute and storage resources are isolated within clusters by tenant. Encrypted transmission and storage, including data tables and columns, ensure that sensitive data is kept secure.
    Kerberos authentication management and fine-grained enterprise management allow you to manage cluster permissions by project. Multi-tenant management is supported and compute and storage resources are isolated within clusters by tenant. Encrypted transmission and storage, including data tables and columns, ensure that sensitive data is kept secure.
  • Multi-level Availability

    Instances can be distributed across cluster nodes, so if any individual node fails, its instances can be quickly migrated to other nodes. Cross-AZ data synchronization and backup are also supported, so you can enjoy redundant backups across different AZs, and cluster metadata can be stored externally, in RDS. All service management nodes in a cluster support cross-AZ HA deployment for multi-level high reliability VMs and clusters.
    Instances can be distributed across cluster nodes, so if any individual node fails, its instances can be quickly migrated to other nodes. Cross-AZ data synchronization and backup are also supported, so you can enjoy redundant backups across different AZs, and cluster metadata can be stored externally, in RDS. All service management nodes in a cluster support cross-AZ HA deployment for multi-level high reliability VMs and clusters.
  • High Performance

    Get responses in seconds when analyzing even trillions of records thanks to Huawei-developed CarbonData indexing, materialized views, and caching. The large-scale cluster scheduler Superior, from Huawei, allows you to blast through the traditional limitations of single cluster deployments. With Superior, the scheduling capability of a single cluster can exceed 10,000 nodes, and the Huawei data acceleration engine, DataTurbor, turbocharges compute performance.
    Get responses in seconds when analyzing even trillions of records thanks to Huawei-developed CarbonData indexing, materialized views, and caching. The large-scale cluster scheduler Superior, from Huawei, allows you to blast through the traditional limitations of single cluster deployments. With Superior, the scheduling capability of a single cluster can exceed 10,000 nodes, and the Huawei data acceleration engine, DataTurbor, turbocharges compute performance.

Compatible with x86, ARM, and Open Source Big Data Ecosystem

Huawei-developed ARM servers are integrated with Huawei-developed OSs and databases, achieving integration of software and hardware. Various open source computing frameworks, for example, MapReduce, Spark/Spark SQL, Hive, and Storm are supported.

CarbonData, a high-performance file storage format, is supported.

Spark SQL is compatible with Hive SQL.

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