Do You Have Time-Series Data? ✅

Are your queries getting slower and sluggish? Is your database performance degrading? You may be dealing with time-series data, or temporal data, that you didn't know you had.

Time-series data is more common than you might think. We often find that developers don't always refer to their data as time series, even though they often are.

If your data has a timestamp, even if it's not the primary dimension, you're dealing with time-series data. Let's explore the most common types of time-series data.

Sensor Data

Whether you're working with environmental sensors (temperature, humidity) or tracking building occupancy data, you can efficiently store and analyze time-series data from your IoT devices.

CREATE TABLE smart_home_data (
    timestamp TIMESTAMPTZ NOT NULL,
    device_id UUID NOT NULL,
    temperature DOUBLE PRECISION,
    motion_detected BOOLEAN,
    PRIMARY KEY(timestamp, device_id)
);

Transaction Data

(financial transactions, customer transactions, order history, etc.)

Working with transaction data like customer purchases, ATM withdrawals, or order history? PostgreSQL and Timescale store and process this data effectively, making it easy to analyze patterns over time.

CREATE TABLE financial_transactions (
    transaction_id SERIAL PRIMARY KEY,
    customer_id INT NOT NULL,
    timestamp TIMESTAMPTZ NOT NULL,
    amount DECIMAL(20,2) NOT NULL,
    transaction_type VARCHAR(50) NOT NULL,
    description TEXT
);

Operational analytics& Real-Time Application data

When your apps generate tons of time-series operational data - metrics, logs, events - PostgreSQL and Timescale help you store and query it efficiently. These databases are built to handle the continuous streams of data that pile up when you're monitoring services, tracking system performance, or debugging production issues.

CREATE TABLE operational_data (
    data_id SERIAL PRIMARY KEY,
    app_id INT NOT NULL,
    timestamp TIMESTAMPTZ NOT NULL,
    cpu_usage DECIMAL(5,2) NOT NULL,
    memory_usage DECIMAL(5,2) NOT NULL,
    response_time INT NOT NULL,
    error_rate DEC
);

Fleet data & Logistics data

If you're building apps that handle vehicle GPS streams, sensor readings, and usage stats, PostgreSQL and Timescale can manage those frequent location pings and timing data. Think real-time positions, telematics, route histories - the kind of high-frequency data points that pile up when you've got hundreds of vehicles on the road.

CREATE TABLE fleet_data (
    record_id SERIAL PRIMARY KEY,
    vehicle_id INT NOT NULL,
    timestamp TIMESTAMPTZ NOT NULL,
    location GEOGRAPHY NOT NULL,
    speed DECIMAL(5,2) NOT NULL,
    fuel_level DECIMAL(5,2),
    engine_status BOOLEAN NOT NULL
);

Metrics Data

Think constant streams of metrics data, like energy metrics - sensor readings flooding in every second from equipment, buildings, or infrastructure. PostgreSQL and Timescale handle these continuous data flows, like tracking power consumption across office floors, temperature sensors reporting constantly, or occupancy monitors updating in real-time. We're talking millions of data points piling up each day.

CREATE TABLE energy_metrics (
    metric_id SERIAL PRIMARY KEY,
    timestamp TIMESTAMPTZ NOT NULL,
    facility_id INT NOT NULL,
    power_usage DECIMAL(10,2) NOT NULL,
    voltage DECIMAL(10,2) NOT NULL,
    current DECIMAL(10,2) NOT NULL,
    efficiency DECIMAL(5,2)
);

Tick data/Fintech data/Trading data

When working with market data, you're ingesting price ticks and trades coming in multiple times per second, amassing millions of daily records, you need a database that can keep up. PostgreSQL and Timescale handle these high-frequency data streams, whether you're tracking stock prices, crypto exchanges, or market indicators.

This data analysis allows traders to identify market trends, forecast price changes, and plan their strategies. Additionally, real-time analysis enables immediate responses to market shifts, either capitalizing on opportunities or minimizing risks.

CREATE TABLE tick_data (
    tick_id SERIAL PRIMARY KEY,
    timestamp TIMESTAMPTZ NOT NULL,
    symbol VARCHAR(10) NOT NULL,
    price DECIMAL(10,2) NOT NULL,
    volume INT NOT NULL
);

Event Data

When your apps log user events, you're tracking actions with timestamps: clicks, searches, page views, purchases. For high-traffic applications, these events accumulate fast.

For example, take an e-commerce site - every product view, search query, and cart modification gets logged. Or gaming platforms tracking player interactions, item usage, and in-game events. You're easily looking at millions of events daily, each needing efficient storage and quick querying.

CREATE TABLE event_data (
    event_id SERIAL PRIMARY KEY,
    user_id INT NOT NULL,
    timestamp TIMESTAMPTZ NOT NULL,
    event_type VARCHAR(50) NOT NULL,
    details JSONB
);

Vector data

Vector data represents geospatial objects as points, lines, or polygons - think buildings, roads, bodies of water, as well as abstract features like administrative boundaries and electoral districts. PostgreSQL and Timescale can handle this type of data, which is commonly used in GIS for spatial analysis, mapping, and visualization.

Weather data

Weather data includes temperature, humidity, wind speed/direction, pressure, and precipitation measurements, with readings typically collected hourly. PostgreSQL and Timescale handle these continuous streams of meteorological measurements, whether you're working
with current conditions or analyzing historical patterns.

CREATE TABLE weather_data (
    record_id SERIAL PRIMARY KEY,
    location_id INT NOT NULL,
    timestamp TIMESTAMPTZ NOT NULL,
    temperature DECIMAL(5,2),
    humidity DECIMAL(5,2),
    wind_speed DECIMAL(5,2),
    wind_direction DECIMAL(5,2),
    pressure DECIMAL(7,2),
    precipitation DECIMAL(5,2)
);

Insurance data

Insurance data encompasses policy details, claim histories, and customer interactions. When handling thousands of daily claims, PostgreSQL and Timescale track key data points: claim timestamps, processing duration, type of claim, and interaction patterns.

CREATE TABLE insurance_data (
    record_id SERIAL PRIMARY KEY,
   policy_number VARCHAR(50) NOT NULL,
    customer_id INT NOT NULL,
    issue_date TIMESTAMPTZ NOT NULL,
    expiry_date TIMESTAMPTZ,
    coverage_type VARCHAR(50) NOT NULL,
    coverage_amount DECIMAL(20,2) NOT NULL,
    premium DECIMAL(10,2) NOT NULL,
  claim_status VARCHAR(50)
);

Call records

For telecommunication companies, these databases offer efficient handling of call records, aiding in billing, fraud detection, and network optimization.

CREATE TABLE call_records (
    call_id SERIAL PRIMARY KEY,
    caller_id INT NOT NULL,
    receiver_id INT NOT NULL,
   start_time TIMESTAMPTZ NOT NULL,
   end_time TIMESTAMPTZ NOT NULL,
    call_duration INT NOT NULL,
    call_type VARCHAR(20) NOT NULL,
    location GEOGRAPHY
);

Is Your Data Time Series?

Time-series data shows up in more places than you might think. Whether you're dealing with sensor readings, event logs, market ticks, or any of the examples above, the pattern is similar: timestamp-based data that accumulates continuously.

PostgreSQL with Timescale's extensions helps manage this kind of data efficiently - from storage to querying historical records.

If your working with these data types and your queries are getting slower, consider diving in and to learn how you can solve degrading database performance/.