GBase Cloud Data Warehouse Empowers Cloud-Native Architecture Transformation

Architecture Evolution of Data Warehouses

Data warehouse technology started from early standalone databases and has evolved through multiple stages driven by business growth and technological advancements. Initial data warehouses were built on standalone databases like Oracle and DB2. However, limited computing power and storage capacity often caused performance bottlenecks when processing large-scale data. To address this, the Share Disk cluster architecture represented by Oracle RAC emerged, enabling horizontal scaling of computing power via shared disks. Yet, with multiple computing nodes sharing the disk, contention for read/write resources often led to I/O performance bottlenecks. Additionally, cache information sharing among multiple computing nodes is achieved through high-speed inter-node networks, which creates significant pressure when the number of nodes is large, thus limiting node scalability.

To overcome storage limitations, the Share Nothing architecture was developed. This architecture is categorized into MPP appliance architecture and open MPP architecture. Representative products of the MPP appliance include Teradata and Oracle Exadata, while open MPP architectures include Greenplum, Vertica, and GBase 8a (MPP).

The appliance architecture integrates software and hardware for excellent integrated performance, but due to proprietary hardware, it has obvious disadvantages in scalability flexibility and cost. Open MPP architectures use commodity x86-based PC servers, commercial switches, and routers to build clusters, offering significant advantages in scalability flexibility and hardware cost.

Open MPP architectures also have limitations in scalability, scale-in/out, mixed workloads, and data skew, which become more pronounced as business grows and data volumes increase. In terms of scalability, MPP architectures have limited expansion capability because as the number of nodes increases, communication and synchronization overhead also grow, degrading performance. Therefore, MPP architectures struggle to support large-scale data processing and computing tasks. Regarding scale-in/out, storage and compute are tightly coupled in cluster nodes, so scaling requires data redistribution that consumes computing resources. For mixed workloads, when a cluster simultaneously handles batch jobs and query services based on analysis results, long-running batch tasks contend for computing resources, causing unstable query performance. For data skew, data must be distributed and transferred across multiple nodes in MPP architectures. Uneven or incomplete data distribution can leave some nodes idle while others are busy, wasting resources and reducing performance.

Cloud computing offers several advantages: scalability – it provides elastic capacity to dynamically add or remove computing resources based on demand; flexibility – cloud platforms offer a variety of services, including VMs, storage, networking, and databases, to meet diverse business needs; high availability – cloud platforms ensure business continuity and stability through mechanisms such as backup and disaster recovery; cost reduction – enterprises can reduce IT infrastructure investment and maintenance costs because cloud service providers handle management and maintenance; easy management – cloud platforms provide automated management and monitoring, helping enterprises manage and maintain their operations more easily; improved efficiency – cloud computing enables rapid development of new applications, boosting operational efficiency.

In summary, decoupling storage and compute—achieving storage-compute separation adapted to cloud environments—greatly mitigates the problems inherent in the tightly coupled MPP architecture of traditional IT systems. Hence, cloud-native data warehouses designed for cloud environments have emerged. A cloud data warehouse is a technology that separates storage and compute, offering high elasticity, strong security, easy sharing, and high availability. It represents the growing demand and technology trend of migrating large-scale data processing and analytics tasks to the cloud.

Cloud data warehouses are typically built on distributed storage and computing technologies, delivering high-performance, high-throughput, and low-latency data processing services. Representative products include Snowflake, GBase 8a (GCDW), as well as cloud-native databases from cloud providers such as Amazon Redshift and Google BigQuery.

Advantages of Cloud Data Warehouses

With cloud data warehouse solutions, enterprises can gain the following advantages:

Scalability: Cloud data warehouses can be scaled dynamically based on business demands——whether increasing storage capacity, boosting computing power, or enhancing analytical performance——quickly and flexibly.
Flexibility: Cloud data warehouses offer multiple data storage and processing models, such as batch processing, stream processing, and interactive queries, enabling you to choose the appropriate service model based on actual needs.

High Availability and Disaster Recovery: Cloud data warehouses typically feature high availability and disaster recovery capabilities to ensure data reliability and business continuity. They also provide data backup and recovery functions.

Cost Reduction: Enterprises no longer need to make heavy investments in IT infrastructure. Cloud data warehouses offer excellent resource reuse, making full use of existing resources and avoiding waste.

Easy Management: Cloud data warehouses provide automated management and monitoring, making it easier for enterprises to manage their operations. Through dashboards or APIs offered by cloud service providers, enterprises can monitor resource status and usage in real time and perform necessary management and configuration.

Improved Efficiency: Cloud data warehouses simplify application development and deployment, enabling enterprises to rapidly build and deploy applications, thereby increasing operational efficiency. Additionally, using the analytics and reporting tools provided by cloud service providers, enterprises can gain better insight into their business status and perform optimizations accordingly.

Global Deployment: With cloud data warehouses, enterprises can easily deploy and manage applications and data worldwide to meet globalization needs.

Technology Innovation and Collaboration: Partnering with cloud service providers gives access to the latest technologies and innovative solutions, while fostering closer collaboration and innovation with other partners.

Security and Reliability: Cloud data warehouse providers typically offer advanced security measures and technologies to ensure data security and integrity. Additionally, because computing resources are shared among multiple users, cloud computing delivers higher reliability.

Data Integration and Analytics: Cloud data warehouses make it easier to integrate data from diverse sources and perform in-depth analysis, yielding valuable business insights.

Single-Copy Analytics Capability: By separating storage and compute, multiple analytical perspectives can be derived from a single copy of data. Even when users request different analytical views or algorithms, results can be obtained from a single data source, avoiding data redundancy and inconsistency.

Avoiding Data Redundancy: In traditional data warehouses, data often becomes redundant to accommodate varying analytical requirements. The architecture of cloud data warehouses optimizes how data is stored and used, eliminating unnecessary redundancy.

Unified Data Standards: In multi-department or cross-departmental environments, data definitions can vary due to different data sources and interpretations, leading to inconsistent data standards. Cloud data warehouse management features enable unified data definitions, storage methods, and usage, ensuring data accuracy and consistency.

Data Warehouse and Lake Integration: “Integrated warehouse and lake” is a new data processing and analytics paradigm that unifies the functions of data warehouses (for structured data) and data lakes (for unstructured data). The boundaries between them blur, enabling more efficient and flexible data processing and analysis.

In summary, the evolution of cloud data warehouse architecture provides enterprises with more flexible, efficient, and reliable data processing and analytics capabilities. It not only improves operational efficiency and decision-making accuracy but also accelerates digital transformation and innovation.

Currently, many data warehouse users still rely on open MPP architectures, but as their business grows and data volumes increase, the limitations of this architecture become increasingly apparent. GBase Cloud Data Warehouse is an ideal technology choice to address these challenges. It fully meets customer needs by delivering high-performance, highly available, highly scalable, and highly secure data storage and processing services. By migrating data warehouses to the cloud and leveraging cloud computing advantages, enterprises can gain superior data processing and analytics capabilities to better support business growth.

GBase's Advantages

As an independent cloud data warehouse vendor, GBase's main advantages are reflected in the following aspects:

Technical Expertise: GBase has years of accumulated expertise and experience in distributed databases, backed by strong R&D capabilities. Its GBase series database products deliver outstanding performance, stability, and reliability, meeting the needs of various industries.

Product Innovation: GBase focuses on product innovation, continuously launching new products and services that adapt to market changes. For example, in emerging technology fields like cloud computing and big data, it has introduced cloud database products and big data solutions to meet customer demands.

Service Support: GBase offers comprehensive service support including consulting, implementation, and maintenance. The company has an experienced professional team that provides efficient technical support and problem-solving services.

Industry Experience: GBase has extensive practical experience across various industries, enabling it to deliver customized solutions based on actual customer needs. Deep industry knowledge and accumulated experience help customers better address business challenges.

Scalability: GBase's cloud database products offer excellent scalability, providing expansion capabilities that grow with customers' business. This supports customers' long-term database investment and planning.

Cost-effectiveness: GBase is committed to providing more cost-effective solutions by optimizing product and service structures, thereby reducing customers' total cost of ownership.

Security: GBase prioritizes data security and privacy protection, offering comprehensive security mechanisms and privacy safeguards. It ensures customer data security and compliance, reducing security risks.

Final Words

As an independent cloud data warehouse vendor, GBase has built deep capabilities in technical expertise, product innovation, service support, industry experience, scalability, cost-effectiveness, and security. We hope to leverage these strengths to help more enterprise users plan, discover, and realize data value in this increasingly crowded database market, winning customer trust and reputation.