Tag: data lake

Azure Cosmos DB: real-time data movement using Change Feed and Azure Functions

Arjun13 Comments

Many people who read my Cosmos DB articles are looking for an effective way to export data to SQL, either on-demand or in real-time. After performing a search term analysis for my blog earlier this year, I had made up my mind about posting a solid article on exporting data from Cosmos DB to SQL Server.

Note that this serverless and event-based architecture may be used to not only persist Cosmos DB changes to SQL Server, but trigger alternate actions such as stream processing or loading to blob/Data Lake.

CosmosDBSearches_SQLRoadie.jpg

Real-time ETL using Cosmos DB Change Feed and Azure Functions

In this article, we will focus on creating a data pipeline to ETL (Extract, Transform and Load) Cosmos DB container changes to a SQL Server database. My main requirements or design considerations are:

  • Fault-tolerant and near real-time processing
  • Incur minimum additional cost
  • Simple to implement and maintain

Cosmos DB Change Feed

Cosmos DB Change Feed listens to Cosmos DB containers for changes and outputs the list of items that were changed in the chronological order of their modification. Cosmos DB Change Feed enables building efficient and scalable solutions for the following use cases:

  • Triggering a notification or calling an API
  • Real-time stream processing
  • Downstream data movement or archiving

AzureCosmosDBChangeFeedOverview

Types of operations

  • Change feed tracks inserts and updates. Deletes are not tracked yet
  • Cannot control change feed to track only one kind of operation, for example only inserts
  • For tracking deletes in the Change Feed, workaround is to soft-delete and assign a small TTL (Time To Live) value of “n” to automatically delete the item after “n” seconds
  • Change Feed can be read for historic items, as long as the items have not been deleted
  • Change Feed items are available in order of their modification time (_ts system attribute), per logical partition key, and tagged with the same _lsn (system attribute) value for all items modified in the same transaction

Read more about Azure Cosmos DB Change Feed from Microsoft docs to gain a thorough understanding. Change Feed can be processed using Azure Functions or Change Feed Processor Library. In this article, we will use Azure Functions.

Azure Functions

Azure Functions is an event-driven, serverless compute platform for easily running small pieces of code in Azure. Key points to note are:

  • Write specific code for a problem without worrying about an application or the infrastructure to run it
  • Use either C#, F#, Node.js, Java, or PHP for coding
  • Pay only for the time your code runs and trust Azure to scale
  • As of July 2019, the Azure Functions trigger for Cosmos DB is supported for use with the Core (SQL) API only

Read more from Microsoft docs to understand full capabilities of Azure Functions.

If you use Consumption plan pricing, it includes a monthly free grant of 1 million requests and 400,000 GBs of resource consumption per month per subscription in pay-as-you-go pricing across all function apps in that subscription, as per MS docs.

Compare hosting plans and check out pricing details for Azure Functions at the Functions pricing page to gain a thorough understanding of pricing options.

Real-time data movement using Change Feed and Azure Functions

The following architecture will allow us to listen to a Cosmos DB container for inserts and updates, and copy changes to a SQL Server Table. Note that Change Feed is enabled by default for all Cosmos DB containers.

I will create a Cosmos DB container and add an Azure Function to listen to the Cosmos DB container. I will then modify the Azure Function code to parse modified container items and save them to a SQL Server table.

1. First, I navigated to Azure portal, Cosmos DB blade and created a container called reservation in my Cosmos DB database. As it is purely for the purposes of this demo, I assigned lowest throughput of 400 RU/s

01_ContainerCreation02_ContainerCreated

 

2. Now that the container is ready, proceed to create an Azure Function App. The Azure Function will be hosted in the Azure Function app

03_AddFunctionApp.png

04_AddFunctionApp.png

 

3. Add an Azure Function within the newly created Azure Function App. Azure Function trigger for Cosmos DB utilizes the scaling and event-detection functionalities of Change Feed processor, to allow creation of small reactive Azure Functions that will be triggered on each new input to the Cosmos DB container.

055_AzureFunction.png

05_FunctionAppCreated.png

06_AddAzureFunction

 

4. Configure the trigger. Leases container may be manually created. Alternately, check the box that says “Create lease collection if it does not exist”. Please note that you would incur cost for storage and compute for leases container.

07_AzureFunctionConfig

I got this error that read – “The binding type(s) ‘cosmosDBTrigger’ are not registered. You just need to install the relevant extension. I saw many posts about this, so it will most likely be fixed soon.

08_AzureFunctionBindingError.png

Sort out the error by installing the extension for Azure Cosmos DB trigger.

09_AzureCosmosDBTriggerExtensionInstall

 

5. Once the function is up and running, add an item to the reservations container that we are monitoring. And we have a working solution!

10_CosmosDBContainer_AddEntries11_AzureFunctionRunning

 

6. Trigger definition may be modified to achieve different things, in our case we will parse the feed output and persist changes to SQL server. You can download the csx file I used.

12_AzureFunctionDefinitionModify.png

13_AzureCosmosDBContainer_ModifyItem14_AzureFunction_SavingToDatabase15_SavedInDatabase

Summary

We have successfully implemented a serverless, event-based low cost architecture that is built to scale. Bear in mind that you would still end up paying for Azure Function and the underlying leases collection, but there will be minimum additional RU cost incurred from reading your monitored container(s) as you are tapping into the Change Feed.

You can monitor the function and troubleshoot errors.

17_Funcion_Control20_Monitor_AzureFunction

I hope you found the article useful. Add a comment if you have feedback for me. If you have any question, drop me a line on LinkedIn. I’ll be happy to help 🙂 Happy coding!

Resources:

https://docs.microsoft.com/en-us/azure/cosmos-db/change-feed
https://docs.microsoft.com/en-us/azure/cosmos-db/changefeed-ecommerce-solution
https://azure.microsoft.com/en-au/services/functions/
https://docs.microsoft.com/en-us/azure/azure-functions/functions-overview
https://docs.microsoft.com/en-us/azure/azure-functions/functions-bindings-cosmosdb-v2
https://docs.microsoft.com/en-us/azure/cosmos-db/change-feed-functions
https://azure.microsoft.com/en-au/resources/videos/azure-cosmosdb-change-feed/
https://h-savran.blogspot.com/2019/03/introduction-to-change-feed-in-cosmos.html

Azure Databricks – Introduction (Free Trial)

Arjun

Microsoft’s Azure Databricks is an advanced Apache Spark platform that brings data and business teams together. In this introductory article, we will look at what the use cases for Azure Databricks are, and how it really manages to bring technology and business teams together.

Databricks

Before we delve deeper into Databricks, it is good to have a general understanding of Apache Spark.

Apache Spark is an open-source, unified analytics engine for big data processing, maintained by the Apache Software Foundation. Spark and its RDDs were developed in 2012 in response to limitations of MapReduce. 

Key factors that make Spark ideal for big data processing are:

  • Speed – up to 100X faster
  • Ease of use – code in Java, Scala, Python, R and SQL
  • Generality – use SQL, streaming and complex analytics
Apache Spark Ecosystem.jpg
Pic courtesy: Microsoft

Databricks – the company – was founded by creators of Apache Spark. Databricks provides a web-based platform for working with Spark, with automated cluster management and IPython-style notebooks. It is aimed at unifying data science and engineering across the Machine Learning (ML) life cycle from data preparation, to experimentation and deployment of ML applications. Databricks, by virtue of its big data processing capabilities, also facilitates big data analytics. Databricks, as the name implies, thus lets you build solutions using bricks of data.

Azure Databricks

Azure Databricks combines Databricks and Azure to allow easy set up of streamlined workflows and an interactive work space that lets data teams and business collaborate. If you’ve been following data products on Azure, you’d be nodding your head along, imagining where Microsoft is going with this 🙂

Azure Databricks enables integration across a variety of Azure data stores and services such as Azure SQL Data Warehouse, Azure Cosmos DB, Azure Data Lake Store, Azure Blob storage, and Azure Event Hub. Add rich integration with Power BI, and you have a complete solution.

Azure Databricks Overview
Pic courtesy: Microsoft

Why use Azure Databricks?

By now, we understand that Azure Databricks is an Apache Spark-based analytics platform that has big data processing capabilities and brings data and business teams together. How exactly does it do that, and why would someone use Azure Databricks?

  1. Fully managed Apache Spark clusters: With the serverless option, create clusters easily without having to set up your own data infrastructure. Dynamically auto-scale clusters up and down, and auto-terminate inactive clusters after a predefined period of inactivity. Share clusters with your teams, reduce time spent on infrastructure management and improve iteration time.

  2. Interactive workspace: Streamline data processing using secure workspaces, assign relevant permissions to different teams. Mix languages within a notebook – use your favorite out of R, Python, Scala and SQL. Explore, model and execute data-driven applications by letting Data Engineers prepare and load data, Data Scientists build models, and business teams analyze results. Visualize data in a few clicks using familiar tools like Matplotlib, ggplot or take advantage of the rich integration with Power BI.

  3. Enterprise security: Use SSO through Azure Active Directory integration to run complete Azure-based solutions. Roles-based access control enables fine-grained user permissions for notebooks, clusters, jobs, and data.

  4. Schedule notebook execution: Build, train and deploy AI models at scale using GPU-enabled clusters. Schedule notebooks as jobs, using runtime for ML that comes preinstalled and preconfigured with deep learning frameworks and libraries such as TensorFlow and Keras. Monitor job performance and stay on top of your game.

  5. Scale seamlessly: Target any amount of data or any project size using a comprehensive set of analytics technologies including SQL, Streaming, MLlib and GraphX. Configure number of threads, select number of cores and enable autoscaling to dynamically scale processing capabilities leveraging a Spark engine that is faster and performant through various optimizations at the I/O layer and processing layer (Databricks I/O).

Of course, all of this comes at a price. If this article has piqued your interest, hop over to Azure Databricks homepage and avail the 14 day free trial!

Azure Databricks - Free Trial 14 days.jpg

Suggested learning path:

  1. Read more about Azure Databricks – https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks
  2. Create a Spark cluster and run a Spark job on Azure Databricks – https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal#clean-up-resources
  3. ETL using Azure Databricks – https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse
  4. Stream data into Azure Databricks using Event Hubs – https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-stream-from-eventhubs
  5. Sentiment analysis on streaming data using Azure Databricks – https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-sentiment-analysis-cognitive-services

I hope you found the article useful. Share your learning experience with me. My next article will be on Real-time analytics using Azure Databricks.

Azure Databricks - Real time analytics.jpg
Azure Databricks

Resources:

https://azure.microsoft.com/en-au/services/databricks/
https://databricks.com/product/azure
https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks
https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal#clean-up-resources
https://databricks.com/blog/2019/02/07/high-performance-modern-data-warehousing-with-azure-databricks-and-azure-sql-dw.html