Hi friends, in our previous article, we introduced Azure AI Search as Microsoft’s cloud-based, AI-powered search service. Now, let’s dive a little deeper and understand the three core building blocks of Azure AI Search:

• Index – the structure where searchable data lives.
• Data Sources – the origin of your content.
• Indexers – the bridge that moves and enriches data.

These components form the foundation of every Azure AI Search solution. In a simple term, think of them like the engine, fuel and pipeline for your search system.

Index – Heart of Search

The index is the central structure that powers search in Azure AI Search. Think of it as a specialized search database, designed not for transactions like SQL, but for information retrieval.

Key Concepts

• Fields: Each index contains fields (like database columns). Fields can be
  • Searchable → Text fields analyzed for full-text search (e.g., book title, product description).
  • Filterable → Fields used in WHERE clauses (e.g., price < 50).
  • Sortable → Fields used to order results (e.g., sort by release date).
  • Facetable → Used to create navigation filters (e.g., product categories).
  • Retrievable → Fields that return in search results.
• Schema design matters: Choosing the right field attributes is critical. If a field isn’t marked as searchable during creation, you can’t query it later without rebuilding the index.

Best Practices

• Normalize your schema → Don’t overload one field with multiple values.
• Use consistent naming → Helps developers when querying the index.
• Plan for future queries → Think ahead about how your data will be searched.

Limitations

• Once created, you cannot change field attributes (like making a non-searchable field searchable).
• Large documents need chunking for performance.

👉 The index is the brain of your search system — you should be careful while design.

Data Sources – Feeding the Index

A data source is the connection to where your content resides. It tells Azure AI Search: “Here’s where to find the raw data.”

Supported Data Sources

• Azure Blob Storage → Store PDFs, images, JSON, text files.
• Azure SQL Database / Azure SQL Managed Instance → Structured relational data.
• Cosmos DB → NoSQL JSON-based storage.
• Azure Table Storage → Key-value pairs.
• SharePoint Online → Collaboration content.

Features

• Stores connection details (credentials, endpoints).
• Can link to multiple sources at once.
• Supports structured and unstructured content.

Best Practices

• Keep your source clean: garbage in = garbage out.
• Consider partitioning data in storage for easier indexing.
• Use incremental indexing when possible to reduce costs (only index what’s changed).

👉 Data sources are the fuel of Azure AI Search — the better your source, the better your search.

Indexers – Automating the Pipeline

An indexer is the worker that takes content from your data source and loads it into the index. It’s the bridge that keeps your search index fresh and up to date.

What Indexers Do

• Connect to a data source.
• Extract data (structured or unstructured).
• Optionally run AI enrichment through skillsets:
  • OCR on images.
  • Key phrase extraction.
  • Language detection.
  • Text translation.
  • Content chunking for long files.
• Push enriched data into the index.
• Run on a schedule or on-demand.

Best Practices

• Schedule indexers based on how often your data changes (e.g., hourly, daily).
• Avoid running indexers too frequently to reduce costs.
• Monitor failures using Azure Monitor or diagnostic logs.

Limitations

• Some connectors are limited (e.g., SharePoint Online indexer doesn’t support all file formats).
• Large updates may require batching.
• Indexers don’t transform complex business logic — that should be done before ingestion.

👉 Indexers are the pipeline — without them, your data won’t flow.

How It All Works Together

Here’s a simple pipeline:

Data Source → Indexer → Index → Search Queries

Example workflow:

1. Connect Blob Storage (data source).
2. Define an index with fields like Title, Content, Tags.
3. Create an indexer that extracts text from PDFs, applies OCR, and pushes everything into the index.
4. Users query the index for results, either by keyword or semantic meaning.

This flow makes it possible to build search experiences like:

• E-commerce product search.
• Company knowledge base.
• AI-powered chatbot using retrieval-augmented generation (RAG).

Comparison Table

Component	Role	Example	Analogy
Index	Stores searchable data in structured fields	Product catalog (Title, Price, Description)	The brain
Data Source	Where raw content comes from	Azure SQL DB, Blob Storage	The fuel
Indexer	Moves data from source → index (with enrichment)	OCR documents before indexing	The pipeline

Conclusion

The Index, Data Sources, and Indexers are the three pillars of Azure AI Search:

• The Index is your optimized search database.
• The Data Source is where content originates.
• The Indexer keeps your index fresh, enriched, and usable.

Understanding these components is essential before moving on to advanced topics like semantic ranking, vector search, and RAG integration.

If you design your index well, connect clean data sources, and configure indexers properly, you’ll have a scalable, AI-powered search system ready for enterprise or AI use cases.

Happy Searching…