Analytics buckets enable analytical workflows on large-scale datasets while keeping your primary database optimized for transactional operations.
Why Analytics buckets?
Postgres tables are purpose-built for transactional workloads with frequent inserts, updates, deletes, and low-latency queries. Analytical workloads have fundamentally different requirements:
- Processing large volumes of historical data
- Running complex queries and aggregations
- Minimizing storage costs
- Preventing analytical queries from impacting production traffic
Analytics buckets address these requirements using Apache Iceberg, an open-table format specifically designed for efficient management of large analytical datasets.
Ideal use cases
Analytics buckets are perfect for:
- Data warehousing and business intelligence - Build scalable data warehouses for BI tools
- Historical data archiving - Retain large volumes of historical data cost-effectively
- Periodically refreshed analytics - Maintain near real-time analytical views
- Complex analytical queries - Execute sophisticated aggregations and joins over large datasets
By separating transactional and analytical workloads, Zuvo lets you build scalable analytics pipelines without compromising your primary Postgres performance.