What is Azure Cognitive Search and Its Key Features

Azure Cognitive Search is a powerful cloud search service that allows you to easily add a robust search experience to your application. It's built on a scalable and secure foundation that can handle large volumes of data.

With Azure Cognitive Search, you can index and search through vast amounts of data, making it a great tool for businesses that need to manage and analyze large datasets. Azure Cognitive Search is particularly useful for applications that require complex search queries, such as those involving natural language processing and machine learning.

One of the key features of Azure Cognitive Search is its ability to handle large volumes of data, with support for indexing up to 100 million documents. This makes it an ideal choice for businesses that need to manage and analyze large datasets.

By leveraging Azure Cognitive Search, you can create a robust search experience that provides users with fast and accurate results, improving their overall experience and increasing engagement with your application.

Broaden your view: Azure Cognitive Search Vector

Azure Cognitive Search is a powerful tool that uses artificial intelligence to help you find what you're looking for in large amounts of data. It's designed to handle the problem of unstructured data, which can be overwhelming to sift through manually.

Azure Cognitive Search is built on top of Azure Search, but it offers more advanced features and capabilities. Here are some key differences between the two:

The benefits of using Azure Cognitive Search include improved search accuracy, faster search results, and the ability to analyze unstructured data.

You can get started with Azure Cognitive Search via the Azure portal, REST APIs, or Azure SDKs like the Azure SDK for .NET. The Azure portal offers tools for developing and querying your skillsets and indexes, as well as help for service administration and content management.

To begin, you'll need to choose a tier and a region. One free search service is allowed per subscription, and you can complete all quickstarts on the free tier. If you need more capacity and features, you'll require a chargeable tier.

Curious to learn more? Check out: Free Search Engine for Website

Here's a simplified step-by-step guide to get you started:

Alternatively, you can create, load, and query a search index in atomic steps, but be aware that this method may require more manual effort and can be more time-consuming.

Your search service has two primary workloads: indexing and querying. Indexing is where content is loaded and made searchable, using tokens and inverted indexes for quick scans.

You can upload JSON documents or serialize data into JSON using an indexer. This process is essential for making your content searchable.

If your content requires language or picture analysis before it can be indexed, AI enrichment comes into play. This cognitive skill-based AI enhancement can extract text, translate text, and infer text and structure from non-text files.

Here are the four steps to complete a full investigation of fundamental search features:

Azure Search is an API-based service that provides REST APIs via protocols like OData or integrated libraries like the .NET SDK. It primarily consists of creating data indexes and search requests within the index.

Data to be searched is uploaded into logical containers called indexes. An interface schema is created as part of the logical index container that provides the API hooks used to return search results.

Curious to learn more? Check out: Azure Cognitive Search Index

You can start with the Azure portal, which offers tools for developing and querying your skillsets and indexes, as well as help for service administration and content management.

The Azure portal is a great place to begin, and it's free to use.

To get started, you'll need to choose a tier and a region. One free search service is allowed per subscription, and you can complete all quickstarts on the free tier.

Here are the basic steps to follow:

Alternatively, you can create, load, and query a search index in atomic steps, but be aware that you'll need to reset the indexer before you run it, or delete and recreate the objects on each run if you're using the free tier.

Azure Cognitive Search offers two different indexing engines, including Microsoft's own NLP technology and Apache Lucene's open source analyzers.

The service can analyze text documents and create an index that associates keywords with documents.

Curious to learn more? Check out: Google Leak Search Documents

Azure Cognitive Search can extract text from scanned images or documents using optical character recognition (OCR) and understand the meaning of the text using machine learning algorithms.

This allows users to find information even if a document doesn't contain the exact keyword they were looking for.

The service can also interpret audio recordings, images, and texts, enabling semantic searches that take into account contextual proximity, synonyms, and other criteria to narrow down relevant results.

Azure Cognitive Search is a powerful tool that allows you to search and index data from various sources, including text documents, images, and external sources. It can even extract text from scanned images using optical character recognition (OCR).

You can create a search index using the Azure portal or by using APIs, such as the REST API, .NET SDK, or another SDK. The index schema defines the structure of searchable content, and you can upload content using the "push" model or use the "pull" model (indexers) if your source data is of a supported type.

Azure Cognitive Search supports search strings using simple query syntax, which includes logical operators, the suffix operator, and query with Lucene query syntax. You can also use hit highlighting to highlight the snippet of text in the search results matching the search query.

The service provides two different indexing engines: Microsoft's search engine, which is built on the Elasticsearch model, and the analysers of the open source libraries of Apache Lucene. It can also use machine learning algorithms to understand the meaning of the text and give users the opportunity to find information even if a document does not contain exactly the keyword they were looking for.

Here are some of the key features of Azure Cognitive Search:

With Azure Cognitive Search, you can integrate multi-source and multi-format data, including unstructured documents such as PDFs, text documents, and presentations. This allows you to quickly find the information you need, regardless of its origin or format.

If this caught your attention, see: Google Search Pdf Documents

Azure Search supports 56 different languages, each equipped with a text analyzer to handle language-specific characteristics.

These analyzers provide features like text segmentation, word normalization, and entity recognition when processing text documents.

Azure Search uses both Lucene-backed analyzers and Microsoft's natural language processing technology-backed analyzers.

The list of supported languages can be found in the Microsoft Azure Documentation.

Geo-spatial support is a powerful feature in Azure Search that allows users to explore data based on a specified geographic location. This means you can easily search and analyze data that's tied to a specific place on the map.

Azure Search supports geo-spatial information, which is a game-changer for applications that require location-based searches. You can find more information about geo-spatial support in the Azure Search documentation.

An overview of geo-spatial support can be found in the Azure Search and Geo-spatial Data section, which provides a detailed explanation of how this feature works.

Check this out: Location Search Optimization

The Differences Between Azure Search and Azure Cognitive Search are actually quite minimal. Azure Cognitive Search is essentially the same as Azure Search, but with additional cognitive services and AI processing capabilities.

The name change from Azure Search to Azure Cognitive Search was made in October 2019 by Microsoft. The reason for the change was to reflect the expanded use of cognitive abilities and AI processing in the service.

The good news is that the API versions, NuGet packages, namespaces, and connection points that were already in place with Azure Search remained unchanged. This was a relief to the technical community, as it meant that existing search solutions would not be affected by the name change.

The main difference is that Azure Cognitive Search offers powerful indexing capabilities, which allows programmers to create unique solutions for finding value in all types of data.

On a similar theme: Dropbox Ai Search

Azure Cognitive Search can help you manage your data more efficiently by integrating multi-source and multi-format data. This means you can search for information in isolated data sources, with different filing systems and file types.

Curious to learn more? Check out: What Is the Data Storage in Azure Called

With Azure Cognitive Search, you can index and search the content of unstructured documents like PDFs, text documents, and presentations, making it easier to find the information you need quickly. Cognitive Search can also be used to search for information from external sources, such as RSS feeds, social media data, or web services.

Here are some data management features of Azure Cognitive Search:

Azure Cognitive Search also helps minimize duplicating data by allowing you to link data from different systems into a single research platform. This reduces complexity and costs associated with managing data, ensuring consistency and accuracy of the information provided.

Azure Cognitive Search can help you connect fragmented information by linking and grouping data from different departments and management systems into a single search platform. This allows for a unified and organized view of the data.

With this service, you can index and link customer data from Customer Relationship Management with sales data from the ERP system, giving a complete view of the customer's life cycle. This ensures that companies can make more informed decisions on marketing plans and sales strategies.

A different take: Azure Storage Account Lifecycle Management

Azure Cognitive Search minimizes the complexity and costs associated with managing data by eliminating the need for separate copies of customer data in different systems. Instead, it's possible to link this data into a single research platform.

You can also group related information to make it more accessible and useful for users. For example, you could use the search to group related products based on user preferences or past purchases, allowing them to quickly find what they are looking for and increase sales through personalized recommendations.

By connecting fragmented information, Azure Cognitive Search helps companies make better decisions and improve their business operations.

Storing output is a crucial aspect of data management. An indexer in Azure AI Search saves the output it creates, which can include up to three data structures.

A searchable index is required for full text search and other query forms. It's used to store index content populated from skill outputs and source fields mapped directly to fields in the index.

Index content is stored in the search service, which is a dedicated location for searchable indexes.

A knowledge store is optional and used for downstream apps like knowledge mining or data science. It's defined within a skillset and determines whether enriched documents are projected as tables or objects in Azure Storage.

Knowledge stores are stored in Azure Storage, which is a scalable and secure data storage solution.

An enrichment cache is also optional and used for caching enrichments for reuse in subsequent skillset executions. It stores imported, unprocessed content and enriched documents created during skillset execution.

Here's a summary of the data stores created by an indexer:

Indexes and knowledge stores are fully independent of each other, allowing for flexibility in data management.

Azure Cognitive Search is a powerful tool, but it's essential to understand its availability and pricing before you start using it.

You can check the availability of enrichment on the regions list page, which is where you can find out if it's available in your area.

Availability is limited to regions that have Azure AI services.

Billing for Azure Cognitive Search follows a pay-as-you-go pricing model.

This means you only pay for what you use, which can be a big cost-saver.

The costs of using built-in skills are passed on when a multi-region Azure AI services key is specified in the skillset.

Text extraction and utility skills aren't billable, which is a nice perk.

For more information on how you're charged, see the How you're charged for Azure AI Search section.

Recommended read: Azure Storage Services