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Alibaba Cloud Named Leader in Gartner Magic Quadrant for Cloud DBMS for Fifth Year in a Row
Zara
January 23, 2025
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Alibaba Cloud Named Leader in Gartner Magic Quadrant for Cloud DBMS for Fifth Year in a Row
Alibaba Cloud, the technology backbone of Alibaba Group, has secured its position as a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMS) for the fifth consecutive year.
Gartner assesses cloud DBMS providers based on their Ability to Execute and Completeness of Vision, considering 15 critical factors such as product quality, customer experience, innovation, market understanding, and strategy.
According to Gartner, “Leaders may represent diverse cloud ecosystems, of which their cloud DBMS is a part, or have good interoperation with such systems. These vendors demonstrate consistently high customer satisfaction and strong customer support.”
Dr. Feifei Li, President of Database Products Business at Alibaba Cloud, stated: “We are honoured to be named a Leader in Gartner’s Magic Quadrant for Cloud DBMS for five consecutive years. As data management becomes central to businesses and generative AI development, we are committed to providing advanced, high-performance solutions to help organisations unlock the full potential of their data.”
Alibaba Cloud offers a comprehensive suite of cloud-native database products, including PolarDB (relational database), AnalyticDB (data warehouse), Lindorm (multimodal database), and its Data Management Service (DMS).
At its 2024 Apsara Conference, Alibaba Cloud introduced DMS: OneMeta+OneOps, a unified platform for managing data across databases, warehouses, and lakes in multi-cloud environments. This innovation improves data utilisation efficiency by up to ten times.
The company also supports an open ecosystem, collaborating with global partners like MongoDB, ClickHouse, and SelectDB. These partnerships enable businesses to choose from a wide range of third-party and open-source database solutions to meet evolving requirements.
Alibaba Cloud’s cloud-native innovations and partnerships have been adopted across various sectors, including finance, retail, internet services, and generative AI.
MiniMax: A leading AI company in China, MiniMax uses Alibaba Cloud’s Lindorm database to power its MiniMax API Platform and Hailuo AI app. Lindorm provides large-scale storage and high-speed data retrieval, enabling advanced AI search capabilities.
TNG Digital: Malaysia’s largest fintech platform, TNG Digital, relies on PolarDB to handle large transaction volumes smoothly, even during peak events. With over 22 million users and payments supported by over 2 million merchants, TNG eWallet achieved zero downtime during Malaysia’s eMADANI event.
ZA Bank: Hong Kong’s first digital bank, ZA Bank, fully transitioned to Alibaba Cloud’s database services, using ApsaraDB RDS for MySQL. This provided scalable, secure, and efficient data management while ensuring compliance and enhanced privacy protections.
As businesses increasingly depend on efficient data management to drive innovation and productivity, Alibaba Cloud continues to deliver scalable, secure, and cutting-edge solutions. Its consistent recognition as a Leader in the Gartner Magic Quadrant reflects its dedication to transforming global data management and empowering organisations across industries.
Effective Solutions for Managing Data Across Multi-Cloud Environments
Organizations are increasingly adopting multi-cloud data lake and lakehouse strategies to gain better insights from their data. These strategies utilize scalable computing power, resilient storage solutions, and flexible tools offered by cloud platforms to process large datasets. This approach reduces technological complexity, speeds up time-to-market, and provides a competitive edge for advanced analytics and AI/ML initiatives.
The multi-cloud environment spans multiple cloud service provider (CSP) regions, ensuring flexibility, redundancy, and enhanced performance. Effective data governance is vital in ensuring consistency, quality, and security of data assets while adhering to compliance and regulatory standards. Robust governance frameworks simplify complexity, reduce risks, and build trust in decision-making processes.
Challenges in Multi-Cloud Data Governance
Managing data governance across multiple clouds comes with unique challenges, including:
Cross-Cloud Inconsistencies: Disparate toolsets, policy scopes, security controls, data classification methods, and access configurations across platforms make standardization difficult.
Data Silos and Fragmentation: Managing diverse data environments introduces vulnerabilities, complicates collaboration, and increases risks during data transfer and storage.
Compliance Complexities: Aligning with region-specific regulations and conflicting legal requirements while maintaining auditable processes across multiple jurisdictions poses significant challenges.
Data Proliferation: Rapid data growth, diverse formats, and inconsistent lifecycle management across platforms create security and management difficulties.
Open-Table Format and Interoperability Challenges: The flexibility of formats like Apache Iceberg, Delta, and Apache Hudi enables seamless data movement but complicates versioning, lineage tracking, and role-based access control (RBAC).
For instance, synchronizing encryption and RBAC policies becomes complicated when the same Delta table is accessed via AWS EMR (using AWS KMS encryption) and Google Dataproc (using Cloud KMS). Similarly, maintaining the principle of least privilege across platforms adds further complexity.
These challenges highlight the need for a platform that offers advanced tools, seamless connectivity, and robust collaboration capabilities to govern data effectively in multi-cloud environments.
Key Pillars of Data Governance
Effective data governance relies on several foundational principles:
Policies and Compliance: Define and enforce data privacy, security, and usage policies to meet regulatory standards.
Data Quality: Monitor key quality indicators (KQIs) to ensure data integrity, relevance, and readiness for analytics.
Data Discovery and Lineage: Enable users to explore and document data relationships while understanding origins and impact.
Metadata Management: Centralize metadata to provide a unified view, enhancing data classification and profiling.
Privacy and Security: Safeguard sensitive data through encryption, masking, and access controls.
Collaboration and Democratization: Foster collaboration by providing self-service access to data for different user roles.
Monitoring and Optimization: Track governance metrics, monitor data quality, and ensure continuous improvement.
People, Processes, and Technology: Integrate skilled personnel, efficient processes, and tools like data profiling and automation to ensure alignment and scalability.
Focusing on these pillars helps organizations reduce risks, maintain compliance, and achieve sustainable success.
Data Governance with Snowflake Horizon Catalog
Snowflake 101, a multi-cloud SaaS data platform, operates seamlessly across AWS, Microsoft Azure, and Google Cloud. Its cloud-agnostic design enables organizations to store, process, and analyze data efficiently.
The Snowflake Horizon Catalog is an integrated governance and discovery solution within the Snowflake AI Data Cloud. It empowers teams like data governors, engineers, and AI/ML professionals to manage and secure data, applications, and models across multiple clouds. With Horizon Catalog, organizations can mitigate cross-cloud security risks, enforce governance protections, and simplify data discovery and sharing.
Capabilities of the Horizon Catalog
Compliance: Automates compliance tracking, real-time monitoring, and audit trails to maintain regulatory adherence. Users can audit access history, monitor data quality, and track data lineage effortlessly.
Security: Implements advanced protections like threat detection, data encryption, and granular access controls (e.g., column- and row-level protections). Sensitive information is safeguarded using PII masking and centralized risk monitoring.
Privacy: Ensures controlled access through data anonymization, pseudonymization, and secure collaboration tools like Data Clean Rooms (DCRs). Differential privacy techniques further protect against privacy breaches.
Discovery: Simplifies data exploration with universal search, automated metadata classification, and AI-powered tagging. Data sharing is enhanced through internal and external marketplaces.
Collaboration: Supports secure data sharing and coordination using controlled permissions, unified billing, and self-service trials across cloud regions without duplication or complex ETL processes.
Transforming Multi-Cloud Governance with Snowflake Horizon
Snowflake Horizon consolidates fragmented data environments into a unified ecosystem. For example, a global enterprise can centrally manage data access across AWS in North America, GCP in Europe, and Azure in Asia-Pacific through a single interface. This centralization allows for consistent governance, data lineage tracking, and compliance enforcement across the entire multi-cloud environment without switching between tools.
In today’s complex multi-cloud landscape, effective data governance is essential. Snowflake Horizon Catalog provides the tools and strategies to simplify governance, ensure compliance, and protect sensitive data. By addressing critical areas like security, privacy, and collaboration, Snowflake enables organizations to overcome data management challenges and unlock their full potential. With Horizon Catalog, businesses can confidently navigate the complexities of multi-cloud ecosystems while ensuring trust, transparency, and operational excellence.
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