Data Lake Compute

Data Lake Compute utilizes a big data analysis architecture that separates storage from computing, enabling rapid and flexible deployments through big data component containerization. This approach supports unlimited scalability on cloud object storage. Its advanced cloud-native elastic model is adaptable to virtually any business type, effectively reducing costs. As a cost-efficient and highly elastic cloud data lake solution, it consolidates data assets and maximizes performance for agile and innovative business applications.

Typical Use Cases:

Batch Log Query

Instead of the conventional method of storing enterprise log data as JSON and text files, data can be stored in COS and queried in batches using standard SQL statements through Data Lake Compute. This allows for the swift generation of data reports and enhances data visualization and productivity. With minimal configuration, cloud-based log service data can also be imported into a data lake for agile analysis.

Service Benefits

• Cost-effective: Data Lake Compute operates on a pay-as-you-go basis, enabling precise cost control through its cloud-native data lake architecture with separate storage and computing.
• Easy-to-use: Data Lake Compute simplifies the process of getting started, allowing for faster queries with its unified SQL syntax.

Data Lake Compute represents an advanced data architecture that facilitates closed-loop big data analysis with a lightweight, agile, user-friendly, and cost-efficient approach. It features a unified metadata management view, enabling the elimination of data silos. By integrating the strengths of various cloud-based big data services, it supports both real-time and offline data analysis scenarios, effectively addressing a broad spectrum of data challenges. Additionally, with its efficient and rapid data flows, it harnesses the capabilities and advantages of multiple cloud services, making it an ideal solution for enterprises establishing a data middle-end.

Typical Use Cases:

Unified Metadata View
Data Lake Compute allows for the unification of all disparate metadata views into a single, centralized view. This enables the streamlined management and utilization of metadata from various sources, fostering agility in building your metadata center and facilitating seamless transitions between products and versions. Notably, it permits the easy reuse of the same metadata across different products.

Agile and Versatile Data Analysis
Within the big data ecosystem, Presto is adept at interactive analysis, while Spark excels in ETL tasks. Data Lake Compute offers unified syntax and lightweight clustering capabilities, allowing seamless data transition between different engines across various scenarios. It also integrates with WeData, enables data import and export to and from numerous products and data sources, such as AltoDB and CLS. This maximizes the strengths of each product through efficient data flows.

Service Benefits:

• Out-of-the-box: Reduces unnecessary operations tasks and costs.
• Metadata management: Supports multiple data sources to unify metadata management and eliminate data silos.
• Comprehensive coverage: Data Lake Compute thoroughly addresses data analysis and application scenarios, including data integration, synergy, scheduling, development, and governance.

Data Lake Compute facilitates a seamless transition from traditional databases to big data environments, enabling you to query and analyze heterogeneous, multi-source data in the cloud, sourced from object storage, databases, and other services. Its unified data view and standard SQL capabilities accelerate federated data queries and analysis, breaking down data silos and fully harnessing the value of your data.

Typical Use Cases

Cross-Business Federated Data Query
Enterprise departments and business units often utilize distinct data architectures for their specific systems, resulting in dispersed data across various storage solutions—such as transaction data in relational databases, active data in Redis, and historical records in object storage. Data Lake Compute enables the alignment and analysis of heterogeneous data from multiple sources, facilitating quicker utilization of cross-business data.

Service Benefits

• Ready-to-use: Eliminates the need for setting up data transfer pipelines, reducing operational overhead and costs.
• Secure and efficient: Features a unified and granular permission management system down to the column level, ensuring fast query performance.
• User-friendly: Enables cross-business analysis without the need for programming language adjustments.

AI Applications, Including Machine Learning and Deep Learning

Data Lake Compute seamlessly integrates with a wide range of AI capabilities and platforms, supporting numerous machine learning functions and offering comprehensive solutions for various intelligent data lake analysis applications. It grants access to multiple industry databases at no cost, allowing for immediate data analysis without the need for data acquisition and cleansing. Additionally, it provides robust BI capabilities to facilitate predictive analysis and gain valuable data insights.

Typical Use Cases

Enhancing Business Growth with Data

Data Lake Compute delivers native machine-learning capabilities through an advanced machine-learning platform, offering a complete smart analysis solution. It addresses real-world business challenges, such as intelligent recommendations and recall strategies, thereby fostering business growth. Machine learning scenarios often face issues like large data volumes, slow model training, and suboptimal algorithm performance. This solution provides ready-to-use machine learning models to develop data-driven predictions and enhance business outcomes. Moreover, its BI capabilities enable efficient business analysis and improved operational efficiency.

Service Benefits

• Ease of Use: The service is seamlessly integrated with Alto Cloud’s machine learning platform, providing access to a vast array of models and APIs.
• Data Standardization: Unified data management and governance ensure more standardized data for effective data operations.