Structured Vs. Unstructured Time-Series Data (Examples)

SUMMARY

Structured and unstructured time-series data represent two fundamental approaches to organizing temporal information. Understanding the differences between these data types is crucial for designing efficient data storage systems and analytical workflows in financial markets, industrial applications, and IoT environments.

Understanding Data Structures in Time Series

Structured time-series data follows a predefined schema with consistent fields and data types. In financial markets, order book data exemplifies structured time-series data, where each record contains specific fields like timestamp, price, and volume. For example, the QuestDB trades table demonstrates this structure:

SELECT timestamp, symbol, price, amount 
FROM trades 
WHERE timestamp > dateadd('h', -1, now()) 
LIMIT 3;

In contrast, unstructured time-series data lacks a rigid schema and may contain varying fields or formats. The ethblocks_json table shows this flexibility with JSON data:

SELECT timestamp, data::json->>'blockNumber' as block_num
FROM ethblocks_json
LIMIT 2;

Storage and Performance Implications

Structured data typically enables better query performance and storage efficiency in time-series databases. When working with weather data, structured formats allow for efficient aggregations:

SELECT 
    timestamp,
    avg(tempF) as avg_temp,
    min(tempF) as min_temp,
    max(tempF) as max_temp
FROM weather
WHERE timestamp >= '2023-01-01'
GROUP BY timestamp;

Next generation time-series database

QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.

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Data Processing and Analysis

The choice between structured and unstructured formats significantly impacts real-time data ingestion and analysis capabilities. Industrial IoT applications often combine both approaches, with structured sensor readings alongside unstructured maintenance logs.

Weather monitoring systems demonstrate this hybrid approach:

SELECT 
    w.timestamp,
    w.tempF,
    w.windSpeed,
    w.skyCover
FROM weather w
WHERE timestamp > dateadd('d', -1, now())
ORDER BY timestamp DESC;

Common Use Cases

Financial Markets

Market data feeds typically use structured formats for core price data while maintaining flexibility for additional metadata. The AAPL_orderbook table exemplifies complex structured data:

SELECT 
    timestamp,
    bid_px_00 as top_bid,
    ask_px_00 as top_ask,
    bid_sz_00 as bid_size
FROM AAPL_orderbook
WHERE timestamp > dateadd('m', -5, now());

Industrial Applications

Industrial systems often generate mixed data types. While sensor readings follow structured patterns, maintenance logs and event data may be unstructured:

SELECT 
    timestamp,
    windSpeed,
    windDir,
    visMiles
FROM weather
WHERE timestamp > dateadd('h', -24, now())
AND windSpeed > 20;

Summary

The choice between structured and unstructured time-series data formats depends on specific use cases and requirements. Structured data offers better performance and efficiency for well-defined data patterns, while unstructured formats provide flexibility for varying data types and evolving schemas. Modern time-series applications often employ both approaches, leveraging their respective strengths for different aspects of data management and analysis.