How to build a real-time personalization API in Microsoft Fabric with Functions and Cosmos DB

You can see companies everywhere trying to give more personal experiences. The global CRM market may reach $82.7 billion by 2025. Now, 87% of businesses focus on using AI in their CRM plans. Many companies use a Real-Time Personalization API to study and use user data right away. With real-time data and analytics APIs, you can sell more and spend less on customer service. Today, agentic AI and collaborative filtering help you match user likes and make better choices for many people.

Key Takeaways

• Real-time personalization APIs make user experiences better. They give quick suggestions using live data.

• Use Microsoft Fabric, Functions, and Cosmos DB to build a strong system. This helps process and get data fast.

• Use blue-green deployment to update your API safely. This lowers downtime and helps users switch smoothly.

• Add reverse ETL to keep your suggestions new. It moves processed data from Spark to Cosmos DB.

• Keep things safe by using HTTPS and managing identities. Test your API often to protect user data.

Real-Time Personalization API Architecture

Components Overview

When you make a Real-Time Personalization API, you need some key parts. Each part helps you get, handle, and share user data fast. Here are the main things you will use:

• Microsoft Fabric is a strong tool for data analytics and joining data.

• Functions let you run code and open your API endpoints.

• Cosmos DB keeps your data and lets you find things quickly.

• Real-Time Intelligence helps you work with live data and find patterns.

• Apache Spark helps with machine learning and making recommendations.

• Eventhouse gathers and sorts your event data for study.

You can use these tools to collect telemetry, make recommendations, and send results with reverse ETL. Your Real-Time Personalization API gives users answers very quickly.

Tip: You can use OneLake and Medallion architecture to put your data in neat layers. This helps you handle both hot and cold data paths. You can also set up alerts with Data Activator to act on important events.

Data Flow

It is important to know how data moves in your Real-Time Personalization API. The table below shows each step and what happens in that step.

You can use this flow to catch user actions, handle them right away, and show results to your team or customers. This way, you give fast and useful recommendations.

Data Ingestion and Processing

Eventhouse Integration

You need a strong system to collect user events. Eventhouse gives you fast and big storage for event data. It can handle millions of events every hour. This makes Eventhouse great for storing and finding lots of events. You can use Eventhouse to get quick answers from your data and support real-time analytics.

When you build your data ingestion pipeline, follow these best steps:

• Choose the right way to bring in data. You can use batch, streaming, or both, based on your needs.

• Set up data contracts. These contracts show how your data should look and what types it has.

• Make sure your pipeline can spot duplicate events. Use special event IDs and time windows to remove copies.

• Add error handling. Dead letter queues help you check and fix failed events.

Eventhouse works with other tools to help you. You can use continuous ingestion from Azure Storage. This brings in new files by itself, so you do not need hard ETL pipelines. Eventstream derived streams let you bring in real-time events without writing code. OneLake Catalog Integration helps you find and move data from Lakehouse to Eventhouse.

MetricDescriptionIngestionLatencyInSecondsTime from when data comes in to when you can query it. This changes based on how you ingest data.EventsProcessedTotal number of events handled by data connections.

Tip: Use dead letter queues to catch and check failed events. This keeps your data clean and safe.

Real-Time Intelligence

You want to work with data as soon as it comes in. Real-Time Intelligence lets you grab user activity and offline data for personalization. You can stream event data and handle it right away. This helps you change user experiences based on live likes and actions.

You can use Real-Time Intelligence to power your Real-Time Personalization API. You get quick and correct recommendations for each user. You can handle millions of events and answer in seconds.

Recommendations with Spark

Collaborative Filtering

You can use collaborative filtering to give smart suggestions. Spark helps you work with lots of data very fast. With Spark Streaming, you can handle live data in less than a second. This lets you show new recommendations right after users do something.

Spark works with other tools like Apache Kafka. You can build a system that reacts to users right away. Spark uses many computers to avoid slowdowns and can grow bigger. If you get more data, you can add more computers. This keeps your recommendations quick and steady.

Here are ways Spark helps collaborative filtering for real-time systems:

• You can use Spark with Scala to make collaborative filtering models.

• You can make results better by cleaning data by genres, style, or content.

• You can mix collaborative filtering with algorithms like XGBoost for changing recommendations.

Tip: Try splitting your data into smaller parts. This helps Spark work faster and gives users quicker answers.

You can use these features to power your Real-Time Personalization API. Your users get suggestions that fit their likes as soon as they use your site.

Reverse ETL

Reverse ETL helps you move finished recommendations from Spark to Cosmos DB. You can set up a pipeline to send user and product vectors to your serving layer. This lets your API give new recommendations every time.

To set up reverse ETL with Spark and Cosmos DB, follow these steps:

1. Turn on Change Data Feed (CDF) by setting the Delta table property.

2. Read the Change Data Capture (CDC) data from the Delta table using Spark.

3. Write the changes to Azure Cosmos DB in append mode.

4. Set up a streaming query to read the CDC stream and write to Cosmos DB with checkpointing.

5. Watch the stream for stopping or timeout.

You can use the Apache Parquet file format to save your data well. Make things faster by splitting datasets into smaller parts. Be careful with shuffle operations, as they use network and disk. Use Broadcast Hash Join to make joining data quicker. Save middle results to speed up work. Change executor memory settings like spark.executor.memory and spark.memory.fraction for better speed.

# Example: Writing Spark CDC stream to Cosmos DB
streamingDF = spark.readStream.format("delta").table("user_vectors")
streamingDF.writeStream \
    .format("cosmos.oltp") \
    .option("checkpointLocation", "/checkpoint") \
    .option("spark.cosmos.accountEndpoint", "<your-endpoint>") \
    .option("spark.cosmos.accountKey", "<your-key>") \
    .start()

Note: Always watch your streaming jobs. This helps you find mistakes and keep your recommendations fresh.

You can use reverse ETL to keep your Real-Time Personalization API quick and correct. Your users get the best suggestions every time they visit your site.

Cosmos DB Serving Layer

Blue-Green Deployment

When you update your personalization API, you want it to keep working. Blue-green deployment helps you do this. You make two environments. One is called "blue." It is the current version. The other is "green." It is the new version. Users go to the blue environment. You test the green one. If the green version works well, you switch users to it.

Here are some good steps for blue-green deployment with Cosmos DB:

• Automate your deployment steps. This helps you avoid mistakes and keeps things the same.

• Use infrastructure as code. It lets you set up environments fast and correctly.

• Watch your systems and set alerts. You can find problems early and fix them quickly.

• Move traffic slowly from blue to green. This helps you catch issues before everyone sees them.

• Plan for rollbacks. If something goes wrong, you can send users back to blue.

• Keep both environments safe. Protect your data at every step.

• Use feature flags to turn new features on or off for some users.

• Try A/B testing to see what users like about new changes.

• Use a service mesh to control how traffic moves between blue and green.

Tip: Blue-green deployment helps you update your API with less risk and less downtime.

Key-Value Lookups

Cosmos DB is great for real-time personalization because it is fast. It gives you quick key-value lookups. You can store user and product vectors as key-value pairs. When a user visits your site, your API finds their data in milliseconds. This speed helps you show recommendations right away.

Here is how Cosmos DB helps with real-time lookups:

Cosmos DB also supports quick reads and writes. This means users do not have to wait for recommendations. You can scale up or down as your user numbers change. The database stays available, even if you have millions of users at once.

Note: Using Cosmos DB as your serving layer helps you give every user a fast, personal experience, no matter where they are in the world.

API Endpoints with Functions

Real-Time Personalization API

You can use Azure Functions to make fast endpoints. These endpoints let your website or app ask for recommendations right away. When a user visits your site, your function gets their user ID. It looks up their data in Cosmos DB. Then it sends back a list of products or content they might like.

To build a strong API, keep endpoints simple and easy. Here is an example of what your endpoints could be:

• GET /api/v1/recommendations/{userId} – Gives a list of items for a user.

• POST /api/v1/feedback – Takes user feedback to help make better recommendations.

Azure Functions can handle these requests quickly. To make sure your API is fast, pick the right hosting plan. Set up your function app for quick responses. The table below shows how to set up Azure Functions for best speed:

Tip: Pick the Premium plan if you need very fast answers and want to stop cold starts. Always test your endpoints to see if they are fast enough.

Think about how to update your Real-Time Personalization API without causing problems. Good versioning helps you add new features and keep old ones working. Here are some best practices:

• Make sure old clients still work.

• Write down all API versions and share changelogs.

• Do not change endpoints or response formats; add new ones for new features.

• Put the API version in the URI path, like /api/v1/.

• Watch how many users use each version before removing old ones.

This way, your Real-Time Personalization API stays reliable and easy to use, even when you add new features.

Security and Scaling

You must keep your Real-Time Personalization API safe. Azure Functions gives you ways to protect your endpoints. Here are some important security steps:

• Use safe communication channels to protect data while moving.

• Set your Azure Function to use HTTPS for all messages.

• Follow safe deployment steps and update often.

• Use managed identity to get settings without showing secrets.

• Give only needed permissions with Azure Role-Based Access Control (RBAC).

• Check and clean all input data to stop attacks.

• Use strong login and permission checks, like Azure AD or OAuth.

• Keep secrets in Azure Key Vault or Azure App Configuration.

• Test your API with penetration testing to find weak spots.

• Watch your environment and check security controls often.

Note: Always use HTTPS for your API endpoints. This keeps your users’ data safe from bad people.

Scaling your API is important when you have lots of users. Azure Functions has different hosting options to help with high traffic. The table below shows the main choices and their features:

Pick the plan that fits your needs. If you expect many users at once, Flex Consumption or Premium plan can help you scale fast. The Consumption plan works well if your traffic goes up and down.

Tip: Watch your API’s speed and change your scaling plan as your user base grows.

By following these security and scaling steps, you keep your Real-Time Personalization API fast, safe, and ready for any number of users.

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You can make a Real-Time Personalization API that grows with your needs by doing these things: First, get real-time data right when users do something. Next, put the data in a good spot so you can find it fast. Then, clean up and change the data so it is ready to study. Last, use your API to give users tips and suggestions.

You can make your API better by using microservices, caching, event-driven updates, and keeping data safe.

Watch your system, make your suggestions better, and add new ways to personalize. These ideas help you give users a better experience.

FAQ

How fast can my API respond to user requests?

You can expect your API to respond in milliseconds. Cosmos DB and Azure Functions work together to give you quick key-value lookups. This speed helps you show recommendations right away.

What is reverse ETL, and why do I need it?

Reverse ETL moves your processed data from Spark into Cosmos DB. You need it to make sure your API always has the latest recommendations ready for users.

Can I scale my API for many users?

Yes! Azure Functions and Cosmos DB both scale automatically. You can handle more users by choosing the right hosting plan and setting up autoscaling.

How do I keep user data safe?

You use HTTPS for all API calls. You store secrets in Azure Key Vault. You also set up role-based access control so only the right people can see or change data.

What happens if I want to update my API?

You use blue-green deployment. This lets you test new changes in a safe environment. When you know everything