{"id":11685,"date":"2021-08-11T00:00:33","date_gmt":"2021-08-11T07:00:33","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=11685"},"modified":"2025-06-13T20:40:57","modified_gmt":"2025-06-14T03:40:57","slug":"couchbase-analytics-now-supports-azure-blob-storage-using-external-analytics-collections","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/couchbase-analytics-now-supports-azure-blob-storage-using-external-analytics-collections\/","title":{"rendered":"Couchbase Analytics Now Supports Azure Blob Storage Using External Analytics Collections"},"content":{"rendered":"

You can now analyze even more of your enterprise data using Couchbase Analytics<\/strong> \u2013 without having to move or migrate a byte.<\/p>\n

Starting today, I’m excited to announce the forthcoming support for Microsoft Azure Blob Storage for external Analytics Collections<\/strong> (currently in Developer Preview). This news follows the support for external Analytics Collections for Amazon Web Services Simple Storage Service (AWS S3)<\/a> in the Couchbase Server 6.6 release. <\/p>\n

In short, this announcement means more Couchbase customers can combine data from external sources (e.g., Azure Blob Storage and AWS S3) with local data (Couchbase Analytics<\/a>) as well as remote Couchbase data (i.e., Remote Analytics Collections<\/a>).<\/p>\n

(Note: This topic has been similarly covered in External Datasets: Accessing AWS S3 in Couchbase Analytics<\/a>. If you’ve used external Analytics Collections before, then the concepts covered in<\/em> this article will be familiar to you.)<\/em><\/p>\n

Why Use External Analytics Collections?<\/h2>\n

Some Couchbase<\/a> customers use Azure Blob Storage to reduce storage costs for large volumes of data (e.g., multiple years of historical data, offline business data for machine learning, product reviews, etc.). <\/p>\n

These customers want to combine, query and utilize data from Azure Blob Storage in real time for business users and data analytics. (Discover how other customers use Couchbase Analytics in this article<\/a>.)<\/p>\n

In order to keep this data in low-cost storage but still use it for data analysis, Couchbase customers can use external Analytics Collections. <\/p>\n

How External Analytics Collections Work<\/h2>\n

External Analytics Collections enable you to dynamically query and analyze data residing in external sources (such as AWS S3 and Azure Blob Storage), allowing you to easily combine data in real time from both inside and outside your Couchbase Analytics nodes. <\/p>\n

All it takes to combine your internal and external datasets is three simple steps: <\/p>\n

    \n
  1. Set up an external link using a REST API call<\/a> or the command-line interface (CLI)<\/a>.<\/li>\n
  2. Create an external Analytics Collection on the external link.<\/li>\n
  3. Query the Analytics Collection using N1QL for Analytics<\/a> (or your favorite BI tool)<\/li>\n

    .\n<\/ol>\n

    Let\u2019s walk through a simple example. <\/p>\n

    iMaz, an ecommerce company, sells consumer products online. Their order, product, and user data are stored on a Couchbase cluster using both the Data and Analytics Services (on separate sets of nodes in the cluster). iMaz uses the Analytics Service to run ad hoc and complex queries to analyze their business. They store their product reviews on Azure Blob Storage, and they would like to combine and analyze the top three most highly rated products using the Couchbase Analytics Service.<\/p>\n

    Here’s some sample product data:<\/p>\n

    \r\n[\r\n{\r\n  \"id\": \"Product_1\",\r\n  \"docType\": \"Product\",\r\n  \"productId\": 1,\r\n  \"price\": 811.76,\r\n  \"salePrice\": 70.14,\r\n  \"productName\": \"Ergonomic Cotton Ball\",\r\n  \"desc\": \"Plastic fused metallic Ergonomic Cotton Ball\"\r\n}\r\n]\r\n<\/pre>\n
     <\/div>\n
    And here’s some sample review data:<\/p>\n
    \r\n{\r\n    \"id\": \"Review_0001764a17a844279a2227e137cc4e36\",\r\n    \"docType\": \"Review\",\r\n    \"reviewId\": \"0001764a17a844279a2227e137cc4e36\",\r\n    \"productId\": 1,\r\n    \"userId\": 5862,\r\n    \"reviewerName\": \"M. Schaefer\",\r\n    \"reviewerEmail\": \"...@mmail.com\",\r\n    \"rating\": 5,\r\n    \"title\": \"Works well and meets expectations.\",\r\n    \"review\": \"Product works great and will buy one more for my extended family.\",\r\n    \"reviewDate\": 1597273484\r\n  }\r\n<\/pre>\n
     <\/div>\n
    Now we’ll walk through each of the three steps from above using sample setup code along with a N1QL<\/a> for Analytics query.<\/p>\n
    Step 1: Set Up the Links<\/h3>\n
    First, you’ll need to create an Azure Blob Storage link using a REST API call<\/a>. (Alternatively, you can use the CLI to create Azure Blob Storage links<\/a>.) <\/p>\n
    You’ll need to provide:<\/p>\n