Unified Data Lake Metadata Layer for In-Situ Cloud Analytics
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Solution Overview
Problem
Existing data warehousing and data lake environments struggle to efficiently manage, transform, and analyze large volumes of structured, semi-structured, and unstructured data while ensuring data governance, security, and performance, particularly in cloud-based systems.
Innovation Solution
A unified, cloud-based data platform that integrates embedded data lake capabilities with advanced data warehousing and analytics, supporting open table formats like Apache Parquet and Delta Lake, and provides elastic compute and robust metadata services for secure, policy-driven access and lifecycle management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in a data lake in native format, then storage capacity and flexibility are improved, but data management complexity and access efficiency deteriorate
Solution Approach 1:
The patent introduces a unified metadata layer and query optimization engine as intermediaries between the data lake storage and access points. This metadata layer provides a standardized interface for managing diverse data formats, while the optimization engine translates complex queries into efficient execution plans, thereby reducing management complexity without sacrificing storage flexibility
Solution Approach 2:
The system implements a universal query interface that can handle multiple data types (structured, semi-structured, unstructured) and formats through a single standardized access mechanism. This multi-functional interface consolidates various data access paths into one unified system, reducing the complexity of managing diverse data formats while maintaining adaptability
2Productivity
If data from various sources is consolidated in a data warehouse, then data analysis capability is improved, but system complexity and data governance challenges worsen
Solution Approach 1:
The patent segments the data consolidation process into distinct layers: raw data ingestion layer, standardized metadata layer, and analysis query layer. Each layer handles specific tasks independently, with the metadata layer providing standardized schemas for different data sources. This segmentation enables complex data analysis while managing system complexity through modular architecture
Solution Approach 2:
A unified metadata service acts as an intermediary between diverse data sources and analysis queries. This metadata service provides standardized data dictionaries, data lineage tracking, and governance policies that simplify the consolidation process while maintaining data quality and security across multiple sources
3Quantity of substance
If cloud-based object storage is used for data lake, then storage scalability and cost efficiency are improved, but data access performance and security control worsen
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing data into columnar formats (Parquet, Delta Lake) during ingestion, creating optimized file structures with embedded metadata and partitioning schemes. This preliminary organization enables faster query execution and reduces the performance penalty of cloud object storage without limiting scalability
4Adaptability or versatility
If open table formats like Parquet and Delta Lake are supported, then data format compatibility and analytics performance are improved, but system complexity and metadata management burden worsen
Solution Approach 1:
The patent implements a universal metadata service that provides a single standardized interface for managing multiple open table formats (Parquet, Delta Lake, Iceberg). This service handles format-specific metadata operations through a unified API, automatically translating between different format requirements and the standardized metadata model, thereby maintaining format compatibility while reducing the management burden
Data Source
AI summary
A cloud-based data platform is disclosed, enabling storage-agnostic data management and analytics in a unified environment. The system may integrate a data lake implemented as a hyperscaler object store, a cloud-based database management system (DBMS), elastic compute resources, and analytics engines. Data may be stored in open table formats, such as Apache Parquet, Delta Lake, and Apache Iceberg, supporting ACID transactions, schema evolution, and efficient query processing. Virtual tables may map data stored in the data lake to the DBMS, enabling in-situ query processing via SQL interfaces. The platform may support advanced features, including change data capture (CDC), time travel, and lifecycle management using a SAGA pattern for atomic operations. Security may be ensured through X.509 certificates, web tokens, and role-based access controls. Elastic compute resources, such as Apache Spark, facilitate large-scale data transformations and analytics.