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Tiger Data architecture for real-time analytics<br>A whitepaper detailing the architectural choices and optimizations for real-time analytics that power TimescaleDB and Tiger Cloud

Tiger Data has created a powerful application database for real-time analytics on time-series data. It integrates seamlessly<br>with the PostgreSQL ecosystem and enhances it with automatic time-based partitioning, hybrid row-columnar storage, and vectorized execution, enabling high-ingest performance, sub-second queries, and full SQL support at scale.

Tiger Cloud offers managed database services that provide a stable and reliable environment for your<br>applications. Each service is based on a PostgreSQL database instance and the TimescaleDB extension.

By making use of incrementally updated materialized views and advanced analytical functions, TimescaleDB reduces compute overhead and improves query efficiency. Developers can continue using familiar SQL workflows and tools, while benefiting from a database purpose-built for fast, scalable analytics.

This document outlines the architectural choices and optimizations that power TimescaleDB and Tiger Cloud's performance and<br>scalability while preserving PostgreSQL's reliability and transactional guarantees.

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Introduction<br>Section titled “Introduction”

What is real-time analytics?<br>Section titled “What is real-time analytics?”

Real-time analytics enables applications to process and query data as it is generated and as it accumulates, delivering immediate and ongoing insights for decision-making. Unlike traditional analytics, which relies on batch processing and delayed reporting, real-time analytics supports both instant queries on fresh data and fast exploration of historical trends, powering applications with sub-second query performance across vast, continuously growing datasets.

Many modern applications depend on real-time analytics to drive critical functionality:

IoT monitoring systems track sensor data over time, identifying long-term performance patterns while still surfacing anomalies as they arise. This allows businesses to optimize maintenance schedules, reduce costs, and improve reliability.

Financial and business intelligence platforms analyze both current and historical data to detect trends, assess risk, and uncover opportunities, from tracking stock performance over a day, week, or year to identifying spending patterns across millions of transactions.

Interactive customer dashboards empower users to explore live and historical data in a seamless experience, whether it's a SaaS product providing real-time analytics on business operations, a media platform analyzing content engagement, or an e-commerce site surfacing personalized recommendations based on recent and past behavior.

Real-time analytics isn't just about reacting to the latest data, although that is critically important. It's also about delivering fast, interactive, and scalable insights across all your data, enabling better decision-making and richer user experiences. Unlike traditional ad-hoc analytics used by analysts, real-time analytics powers applications, driving dynamic dashboards, automated decisions, and user-facing insights at scale.

To achieve this, real-time analytics systems must meet several key requirements:

Low-latency queries ensure sub-second response times even under high load, enabling fast insights for dashboards, monitoring, and alerting.

Low-latency ingest minimizes the lag between when data is created and when it becomes available for analysis, ensuring fresh and accurate insights.

Data mutability allows for efficient updates, corrections, and backfills, ensuring analytics reflect the most accurate state of the data.

Concurrency and scalability enable systems to handle high query volumes and growing workloads without degradation in performance.

Seamless access to both recent and historical data ensures fast queries across time, whether analyzing live, streaming data, or running deep historical queries on days or months of information.

Query flexibility provides full SQL support, allowing for complex queries with joins, filters, aggregations, and analytical functions.

Tiger Cloud: real-time analytics from PostgreSQL<br>Section titled “Tiger Cloud: real-time analytics from PostgreSQL”

Tiger Cloud is a high-performance database that brings real-time analytics to applications. It combines fast queries,<br>high ingest performance, and full SQL support, all while ensuring scalability and reliability. Tiger Cloud extends PostgreSQL with the TimescaleDB extension. It enables sub-second queries on vast amounts of incoming data while providing optimizations designed for continuously...