AnalyticDB for MySQL covers five major scenarios: real-time data warehousing, data lakehouse analytics, BI reporting, precision marketing, and AI-driven analytics.
Real-time data warehousing
Running real-time analytics and batch ETL on separate stacks drives up architecture complexity, development effort, and O&M costs. Idle resources during off-peak hours further inflate spending.
AnalyticDB for MySQL unifies both workloads on a single platform:
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Unified real-time and batch processing: Data can be inserted, updated, and deleted in real time, with online analytics and ETL jobs running side by side. Resource groups isolate workloads to prevent interference.
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Independent scaling of compute and storage: A decoupled architecture lets you scale compute and storage independently based on demand, cutting idle-resource costs.
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Multi-source real-time sync: Synchronize data in real time from ApsaraDB RDS, PolarDB, Kafka, Log Service, and more. Sharded databases can be aggregated into a single table for unified analysis.
Data lakehouse analytics
Data lakes offer low-cost storage but poor query performance. Data warehouses deliver fast queries but at higher storage costs. Moving data between the two introduces redundancy and consistency issues.
AnalyticDB for MySQL Data Lakehouse Edition bridges the gap between lakes and warehouses:
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Unified metadata management: A unified metadata service manages lakehouse data, eliminating data silos.
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Serverless Spark: A built-in Spark engine runs large-scale data processing without requiring a separate cluster.
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Open storage formats: Natively reads and writes Hudi, Delta Lake, and other open formats on Object Storage Service (OSS), fully decoupling storage from compute.
BI and interactive reporting
BI workloads require data to be queryable the moment it is written, with sub-second response times for multi-dimensional analysis. Broad BI tool compatibility reduces frontend development costs.
AnalyticDB for MySQL provides a high-performance query engine and wide ecosystem compatibility for BI workloads:
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Millisecond-level write-to-query: Writes millions of rows per second and makes them queryable within milliseconds.
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Sub-second multi-dimensional analysis: Handles queries spanning hundreds of dimensions, returning results for complex JOINs across dozens of tables within milliseconds to seconds.
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Direct BI tool connectivity: Highly compatible with MySQL and SQL:2003 standards, supporting Tableau, FineReport, Quick BI, and dozens of other BI tools out of the box.
Precision marketing and user growth
Marketing teams need real-time visibility into user acquisition, engagement, and retention across channels to measure ROI quickly. Faster feedback loops drive more agile campaign optimization and product iteration.
AnalyticDB for MySQL delivers real-time data processing for marketing analytics:
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Multi-source real-time sync: Structured and unstructured data from multiple business systems is synchronized to AnalyticDB for MySQL in real time, breaking down data silos.
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Real-time campaign analysis: Run real-time correlations across logs and business data for instant campaign performance feedback — no batch-processing wait.
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Elastic scaling for traffic spikes: Scale compute resources up during promotions and back down afterward to keep costs optimized.
AI-driven analytics
Businesses want non-technical users to query data in natural language, lowering the analytics barrier. Meanwhile, AI applications need efficient vector storage and retrieval for RAG, recommendation, and search use cases.
AnalyticDB for MySQL has built-in AI capabilities that turn the data warehouse into an intelligent analytics platform:
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Natural language querying (NL2SQL): Business users ask questions in plain language, and the system generates and runs SQL automatically — no coding required.
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Vector search and RAG: Native vector indexing and similarity search, combined with large language models, power retrieval-augmented generation (RAG) applications for intelligent Q&A over private enterprise data.
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Agent framework: The built-in OpenClaw Agent framework lets you rapidly build AI applications with tool calling, long-term memory, and multi-turn conversation capabilities.
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AI nodes: Call large language models directly within SQL workflows for text analysis, classification, and summarization — all without moving data out of the warehouse.