Product Advantages

  • Compatibility and Openness

    DLI seamlessly migrates offline Spark applications to the cloud. Leveraging the open-source Apache Spark ecosystem and APIs, DLI makes migration easier.
  • Powerful Computing Power

    DLI uses a high-scalability big data architecture to process data at the TB to EB scale, allowing you to handle data analysis requests in countless scenarios with ease.
  • Outstanding Performance

    DLI uses the in-memory computing model, directed acyclic graph (DAG) scheduling framework, and efficient optimizer to deliver 100-fold improvement in performance compared with the traditional MapReduce model.
  • Cost Efficiency

    DLI is billed in compute units (CUs) based on time used. One CU includes four vCPUs and 16 GB of memory. DLI costs ¥1.4 per CU per hour.

Functions

  • Standard SQL

    DLI uses JDBC and SDK to run ANSI SQL 2003, enabling you to analyze massive amounts of data without investing your time in deployment and maintenance of SQL engines.
    DLI uses JDBC and SDK to run ANSI SQL 2003, enabling you to analyze massive amounts of data without investing your time in deployment and maintenance of SQL engines.
  • Serverless Spark

    DLI offers full-stack Spark capabilities, such as Spark SQL, Spark Streaming, and Spark Batch based on the Apache Spark ecosystem. You can use DLI to analyze data at the TB-EB scale with standard SQL statements or Spark APIs.
    DLI offers full-stack Spark capabilities, such as Spark SQL, Spark Streaming, and Spark Batch based on the Apache Spark ecosystem. You can use DLI to analyze data at the TB-EB scale with standard SQL statements or Spark APIs.
  • Enterprise-class Multi-tenancy

    Computing resources are isolated between tenants to meet job SLAs. Your data permissions can be restricted to a specific table or column for data sharing between departments and permissions management.
    Computing resources are isolated between tenants to meet job SLAs. Your data permissions can be restricted to a specific table or column for data sharing between departments and permissions management.
  • SQL on AI

    DLI integrates the processing and analyzing images, videos, and languages in SQL to offer convergent analysis for both structured and unstructured data.
    DLI integrates the processing and analyzing images, videos, and languages in SQL to offer convergent analysis for both structured and unstructured data.
  • Federated Analysis of Heterogeneous Data Sources

    DLI supports various formats like CSV, JSON, Parquet, ORC, and CarbonData, and performs federated analysis of data from multiple cloud services (for example, OBS, DWS, CloudTable, and RDS) with data migration, helping you innovate faster and obtain valuable insights from your data.
    DLI can supports various formats like CSV, JSON, Parquet, ORC, and CarbonData, and performs federated analysis of data from multiple cloud services (for example, OBS, DWS, CloudTable, and RDS) with data migration, helping you innovate faster and obtain valuable insights from your data.
  • Auto Scaling

    Auto scaling of storage and computing resources allows you to query data without worrying about whether you have sufficient resources.
    Auto scaling of storage and computing resources allows you to query data without worrying about whether you have sufficient resources.

Application Scenarios

  • Large-scale Log Analysis

  • Federated Analysis of Heterogeneous Data Sources

  • Big Data ETL

  • Geographic Big Data Analysis

Large-scale Log Analysis

Operational Data Analysis

Different departments of a company can analyze daily logs via the data analysis platform to obtain data required for intelligent decision making. For example, operational departments may use the platform to obtain data on new users, active users, the retention and churn rates, and the payment rates and determine follow-up actions based on the data. The delivery department can use the platform to obtain the channel sources of new and active users to help them determine what internal platforms to allocate resources to.

Advantages

Efficient Spark Programming

DLI uses Spark Streaming to directly ingest and preprocess data from DIS. You only need to edit the processing logic. There is no need to deal with multi-threading.

Easy to Use

You can use standard SQL statements to compile metric analysis logic. There is no need to navigate a complex distributed computing platform.

Pay-per-Use

Log analysis is scheduled periodically based on time-critical requirements. There is a long idle period between every two scheduling operations. DLI is billed for usage, which is at least 50% cheaper than purchasing exclusive clusters. You only pay for the resources actually used for scheduling.

Related Services

Federated Analysis of Heterogeneous Data Sources

Digital Transformation for Car Companies

In the face of new competition pressures and changes in travel services, car companies build the IoV cloud platform and IVI OS to streamline Internet applications and vehicle use scenarios, completing digital service transformation for car companies. This delivers better travel experience for vehicle owners, increases the competitiveness of car companies, and promotes sales growth. For example, collect and analyze daily vehicle metric data (such as batteries, engines, tire pressure, and airbags), and send maintenance suggestions to vehicle owners in time.

Advantages

No Need for Migration in Multi-source Data Analysis

RDS stores the basic information about vehicles and vehicle owners, CloudTable stores real-time vehicle location data and health status details, while DWS stores periodic statistics. DLI allows for federated analysis of data from multiple sources without data migration.

Tiered Data Storage

Car companies need to archive all historical data for auditing and other services that require only occasional data access. Warm and cold data is stored in OBS and frequently accessed data is stored in CloudTable and DWS, reducing the overall storage cost.

Big Data ETL

Carrier Big Data Analysis

Carriers typically require petabytes, or even exabytes of data storage, including both structured (base station details) and unstructured (messages and communications), and they need to be able to access the data with extremely low data latency. An efficient method to extract value from this data is a major concern.

Advantages

Big Data ETL

You can enjoy TB to EB-level data governance capabilities to quickly perform ETL processing on massive carrier data. Meanwhile, distributed datasets are provided for batch processing.

Enterprise-class Multi-tenancy

If a company is divided into multiple departments, different departments may require not only data isolation, but also the ability to share data when needed. In this scenario, DLI is an excellent choice. Computing resources can be isolated by tenant to ensure the SLA. Meanwhile, permissions can be refined to tables or columns, facilitating data sharing and permission management between departments.

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Geographic Big Data Analysis

Geographic Big Data Analysis

Geographic big data has all the characteristics typical of big data. It features large data volume (for example, PB-scale global satellite remote sensing image data) and numerous data varieties (for example, structured remote sensing image raster data, vector data, unstructured spatial location data, and 3D modeling data). Users focus on how to use efficient mining tools or mining methods to get insights from the large volume of geographic big data.

Advantages

Spatial Data Analysis Operators

With full-stack Spark capabilities and rich Spark spatial data analysis algorithm operators, DLI delivers comprehensive support for real-time processing of dynamic streaming data with location attributes and offline batch processing. DLI can handle massive data, including structured remote sensing image data, unstructured 3D modeling, and laser point cloud data.

Data Governance

DLI allows you to quickly migrate remote sensing image data to the cloud and perform image data slicing to offer resilient distributed datasets (RDDs) for distributed batch computing.

Success Stories
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Chengdu Longyuan Network works with HUAWEI CLOUD to query and analyze gaming data in an effective and efficient manner. The analysis provides support for different departments launching new services. Data applications are integrated, the entire organization benefits.
Longyuan Network

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