Advantages
Full SQL Compatibility
You do not need a background in big data to use DLI for data analysis. You only need to know SQL, and you are good to go. The SQL syntax is fully compatible with the standard ANSI SQL 2003.
Decoupled Storage and Compute
DLI compute and storage loads are decoupled. This architecture allows you to flexibly configure storage and compute resources on demand, improving resource utilization and reducing costs.
Enterprise Multi-Tenancy
You can manage compute or resource related permissions by project or user, and implement fine-grained control to isolate data for each task.
Serverless DLI
DLI is fully compatible with Apache Spark and Apache Flink ecosystems and APIs. It is a serverless big data computing and analysis service that integrates real-time, offline, and interactive analysis. Offline applications can be seamlessly migrated to the cloud, reducing the migration workload. DLI provides a highly-scalable framework integrating batch and stream processing, allowing you to handle data analysis requests with ease. With a deeply optimized kernel and architecture, DLI delivers 100-fold performance improvement compared with the MapReduce model. Your analysis is backed by an industry-vetted 99.95% SLA.
DLI has the following advantages over self-built Hadoop clusters:
Advantage |
Dimension |
Data Lake Insight |
Self-built Hadoop |
---|---|---|---|
Low cost |
Capital cost |
Billing is based on the actual amount of data scanned or used CUH. Saving up to 50% costs. |
Long-term resource occupation, causing severe resource waste and high costs |
Elastic scalability |
Container-based Kubernetes, intelligent elastic scaling |
Not supported. |
|
O&M free |
O&M cost |
Out-of-the-box, serverless architecture |
Strong technical capabilities are required for configuration and O&M |
High availability |
Cross-AZ DR |
N/A |
|
Easy to use |
Learning cost |
Low. The optimization parameters are standardized based on 10 years' experience in thousands of projects. In addition, DLI provides a GUI for intelligent optimization. |
High. Hundreds of tuning parameters need to be learned. |
Supported data sources |
|
|
|
Ecosystem compatibility |
DLV, Yonghong BI, and Fanruan BI |
Big data ecosystem tool |
|
Custom image |
Supported. Dependencies can be added as required to meet service diversity requirements. |
Not supported. |
|
Workflow scheduling |
Scheduling between Data Lake Factory (DLF) and DataArts Studio |
Self-built scheduling tools, such as Airflow |
|
Multiple enterprise-level tenants |
Table-based permission management, providing column level permission granularity. |
File-based permission management |
|
High performance |
Performance |
Higher performance with in-depth software and hardware optimization |
Performance is the same as that of Hadoop open-source versions |
Cross-Source Analysis
Analyze your data across databases. No migration required. A unified view of your data gives you a comprehensive understanding of your data and helps you innovate faster. There are no restrictions on data formats, cloud data sources, or whether the database is created online or off.
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