Precision Marketing

Associative analysis combines information from multiple channels to improve conversion rates.

Advantages

• Cross-source analysis

  Advertisement CTR data stored in OBS and user registration data in RDS are able to be directly queried without migration to DLI.

• Only SQL needed

  Interconnected data sources map together with a table created using just SQL statements.

Log Analysis

Gaming companies need a quality data analysis platform to improve ad placement, new player retention, operations, and feedback for future game iterations.

Pain Points

• Log analysis is usually performed by period. During the idle periods between each task, resources are wasted.

Advantages

• Pay per use & auto scaling

  Release idle resources with flexible scaling policies and save half the costs of exclusive clusters.

• Converged analysis

  Just a single copy of metadata works for real-time cleaning and offline ETL processing. The data processing result can be directly used in interactive analysis for data mining.

Permission Control

When multiple departments need to manage resources independently, fine-grained permissions management improves data security and operations efficiency.

Advantages

• Easier permissions assignment

  Grant permissions by column or by specific operation, such as INSERT INTO/OVERWRITE, and set metadata to read-only.

• Unified management

  A single IAM account handles permissions for all staff users.

Library Integration

Genome analysis relies on third-party analysis libraries, which are built on the Spark distributed framework.

Pain Points

• High technical skills are required to install analysis libraries such as ADAM and Hail.

• Every time you create a cluster, you have to install these analysis libraries again.

Advantages

• Custom images

  Instead of installing libraries in a technically demanding process, package them into custom images uploaded directly to the Software Repository for Container (SWR). When using DLI to create a cluster, custom images in SWR are automatically pulled so you don't have to reinstall these libraries.

• Built-in base images

  Huawei-enhanced Spark and Flink images (multiple versions) and open-source AI images (TensorFlow/Keras/PyTorch) are available for your convenience.

Real-time Risk Control

Almost every aspect of financial services requires comprehensive risk management and mitigation.

Pain Points

• There is very little tolerance for excessive latency when it comes to risk control.

Advantages

• High throughput

  Real-time data analysis in DLI with the help of an Apache Flink dataflow model keeps latency low. A single CPU processes 1,000 to 20,000 messages per second.

• Ecosystem coverage

  Save real-time data streams to multiple cloud services such as CloudTable and SMN for comprehensive application.

Real-time Displays

With COVID-19 raging across the globe, governments need to be able to monitor key data the moment things happen.

Pain Points

• Government employees do not necessarily have a background in big data. SQL is usually far more familiar.

Advantages

• Millisecond responsiveness

  Thanks to its powerful in-memory computing framework, the built-in openLooKeng engine optimizes query performance for interactive analysis on the spot.

• Mainstream compatibility

  DLI queries use SQL syntax, so staff don't need a big data background. This familiar syntax is fully compatible with standard ANSI SQL 2003.

Big Data Analysis

Massive volumes of data include petabytes of satellite images and many types – structured remote sensing raster data, vector data, and unstructured spatial location data. The analysis and mining of all this data needs efficient tools.

Advantages

• Spatial data analysis

  Spark algorithm operators in DLI enable real-time stream processing and offline batch processing. They support massive data types, including structured remote sensing image data, unstructured 3D modeling, and laser point cloud data.

• CEP SQL functionality

  SQL statements are all that is needed for yaw detection and geo-fencing.

• Heavy data processing

  Quickly migrate up to exabytes of remote sensing images to the cloud, then slice them to data sources for distributed batch processing.