Azure Cognitive Search - With Real Life Example (Advanced)

Build Indexers, Index, Enrichment Pipeline using Skillsets, Caching, Search and Filter REST APIs, Suggest, Synonym map.

What you'll learn
Create DataSource(Cosmosdb) , Index, and Indexers
Synonym Mapping for Fields
Access index using REST API. Simple and Lucene syntax search examples.
Data Enrichment with Custom Web API skill and In Built Skill
Suggestion API
Input and Output Field Mapping
Create and Use Azure Function App
Filter, Facets, SearchMode and other Search API use cases.
Requirements
Azure Account with Free tier Access
Description
In this course we are going to understand each aspect of Microsoft Azure's Cognitive Search Service. We will start with hosting an Azure Function App Service to warm up and setup Azure CLI and account access using programmatic keys. Also will  have a basic understanding of Cosmosdb and Storage blob and queues.Then we will start to dive into What and Why's of Azure Cognitive Service.We will take  Vancouver's street address data available as Open Data and create our Cosmosdb collection datasource.Once our data source is ready we will start creating Azure Cognitive Service , Index, and then create an Indexer. Then we will add in-built and custom web api skill ( Azure function app api) to Enrich our source data with new fields. We will use  incremental indexing for Updated /Newly added documents in data source.In the process we will also understand skillset Input and Output Field mappings.We will set Incremental Enrichment Pipeline using Storage Blob ContainerThen we will see how we can Search our index using REST APIs for our Search Service. We will also see examples of:Synonym Mapping for Index FieldsReal Time Suggestion API for UI Search Text BoxLucene and Simple Search using REST APIsFilter search data using Odata query, Learn to do Geo-Spatial SearchGetting Facets, Pagination etcCourse will be updated regularly. Next you can expect OCR based document extract pipeline and Reactjs sample UI to depict Search and suggestion API usage in UI

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Setup Development Environment

Lecture 3 Azure Account(free-tier)

Lecture 4 Setup Azure CLI

Section 2: Azure Function App

Lecture 5 Introduction

Lecture 6 Azure Core Function Tools

Lecture 7 Azure Function - HTTP Trigger

Lecture 8 Azure Function - Test Locally & Deploy

Lecture 9 Azure Storage Service

Lecture 10 Azure Storage Service (hands-on)

Lecture 11 Storage Explorer(hands-on)

Lecture 12 Azure Function - Storage Queue trigger

Lecture 13 Cosmosdb

Lecture 14 Cosmosdb(hands-on)

Lecture 15 Azure Function - Output Binding(Cosmosdb)

Lecture 16 Azure Function - Storage Blob Trigger

Section 3: Azure Cognitive Search

Lecture 17 Overview

Lecture 18 Sample Data set

Lecture 19 Indexing with Cosmosdb Source

Lecture 20 Getting Cosmos Source Collection Ready

Lecture 21 Indexing- Search Service

Lecture 22 Indexing-Create Data Source

Lecture 23 Indexing- Create Index(Schema)

Lecture 24 Indexing - Create Indexer

Lecture 25 Indexing- Incremental Indexing & Resetting Index( High water Mark)

Lecture 26 Field Mapping And Mapping Functions

Lecture 27 Skills And Skillset

Lecture 28 Custom Skill - Azure Web Api

Lecture 29 Custom Skill - Output Field Mapping

Lecture 30 Incremental Data Enrichment (Caching with Blob Storage)

Section 4: Search Service - REST APIs (Search and Filter)

Lecture 31 REST APIs Overview

Lecture 32 Search Querying - Simple Type

Lecture 33 Search Querying - Lucene - Fuzzy Search (~)

Lecture 34 Search Query - Lucene - Proximity Search (~4)

Lecture 35 Search Query - Lucene - Term Boosting

Lecture 36 Search Query - Lucene - Regular Expression (REGEX)

Lecture 37 Search Query - Lucene - Wildcard Search (*,?)

Lecture 38 Filter & Facets ( Geo-Spatial Distance & Exact Match Search)

Section 5: Search Service - Management APIs

Lecture 39 Synonym Maps

Lecture 40 Attach Synonym Maps to Index Fields

Lecture 41 Suggestion API

Lecture 42 Thank you

Developers looking to index huge amount of data using Azure data sources like CosmosDB , Storage Tables, Data lakes etc,Developers looking to make huge datasources available via REST APIs for AI/ML score based lexical Searches,Get search results in sub 100ms search results for millions of items in dataset,Developers looking to Create Data enrinchment pipeline using AI/ML Azure services and third-party APIs