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

• 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.

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

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

• 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.

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

• 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.

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

• 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.

• 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.

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

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

• 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.

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

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

• 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.

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.

• 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.