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Concepts

Introduction

Welcome to Tailglow! This guide will help you understand the core concepts of our analytics system. At the heart of Tailglow are three key components:

  • Records: The raw data points collected from your application.
  • Views: An initial filter of your records to categorize them, for example, as users, orders, or products.
  • Metrics: Aggregated insights derived from views.

Understanding these concepts will allow you to effectively use Tailglow to analyze and monitor your data.

Records

What Are Records?

Records are the foundational building blocks of Tailglow. Every action, event, or data point you send to Tailglow is stored as a record. A record contains structured data that you can later aggregate and analyze.

Record Structure

A record typically includes:

  • Timestamp created_at: The exact time the record was created.
  • Value value: A numerical representation of the data.
  • Keys: User-defined attributes that can be used for filtering and grouping.

How Records Are Collected

Records are ingested via Tailglow’s API. Each record is associated with a project and is securely stored for further analysis.

Views

What Are Views?

Views are custom filters that allow you to focus on specific subsets of your data. Instead of working with raw records directly, you can define views to streamline your analysis.

Creating a View

To create a new view:

  1. Navigate to the Project Settings in the sidebar.
  2. Click on the Views section.
  3. Click “New View”.
  4. Apply filters based on your analysis needs.
  5. Save the view for quick access in the future. It will appear in the sidebar.

Why Use Views?

Views help you:

  • Organize and segment data efficiently.
  • Reduce noise and focus on key insights.
  • Share specific filtered data with your team.

Metrics

What Are Metrics?

Metrics help you analyze trends and patterns in your data. By applying filters and groupings, you can extract meaningful insights from your raw records.

How Metrics Work

A metric is created by aggregating records using:

  • Filters: Narrow down the dataset by specifying conditions (e.g., only include records where value > 100).
  • Group By: Segment data based on a specific attribute (e.g., group records by user_id).
  • Aggregation Functions: Compute statistical summaries (e.g., count, sum, average).

Types of Metrics

Tailglow supports various metric types, including:

  • Count Metrics: Count the number of records that match a filter.
  • Sum/Average Metrics: Aggregate numerical values over a set of records.
  • Trend Metrics: Visualize changes over time.

Summary

Understanding Records, Metrics, and Views is essential to getting the most out of Tailglow. Records store raw data, Views allow for streamlined access to specific data subsets, and Metrics provide analytical insights. By leveraging these three components, you can build powerful analytics tailored to your needs.

Continue exploring the documentation to learn more about setting up projects, sending data, and using Tailglow to its full potential!