Monthly Archives: July 2020

We have learnt about serverless computing on Azure using Azure Functions and Cosmos DB in a previous post. In this post lets see how we can run an Azure Function from an Azure Stream Analytics job. This can be achieved in the following steps: Step 1: Create Azure Stream Analytics job: This can be doneContinue reading “Run Azure Functions from Azure Stream Analytics”

In previous post, we discussed various inputs supported by Azure Stream Analytics. In today’s post, we are going to discuss about the outputs. Azure Stream Analytics supports the following outputs: ADLS Gen1: ADLS support for Stream Analytics is available globally except a couple of Azure regions. SQL Database:  Stream Analytics jobs can write to aContinue reading “Azure Stream Analytics Outputs”

In the previous post, we discussed about streaming units in Azure Stream Analytics. In today’s post, we will learn about allocating the optimum number of streaming units to Stream Analytics jobs. The optimum number of streaming units to be allocated depends on two factors: Partition configuration for the inputs The query used to process eventsContinue reading “Azure Stream Analytics: Streaming Units Allocation”

In previous post, we have discussed some ways to optimize Azure Stream Analytics outputs. In today’s post, lets have a look at streaming units which are used to optimize the performance of the Azure Stream Analytics jobs. To optimize performance, we need  to monitor and adjust the resources allocated to Stream Analytics jobs. Streaming UnitsContinue reading “Performance monitoring in Azure Stream Analytics”

Azure Stream Analytics has been designed to enable users to create jobs that can run 24×7, ingesting and processing continuous stream of events. Microsoft guarantees 99.9% availability for Azure Stream Analytics. Further, to analyze the root cause if and when an error occurs, Azure Stream Analytics comes integrated with performance metrics, logs, job states etc.Continue reading “Two key metrics to monitor Azure Stream Analytics jobs”

In the previous post, we discussed some ways to optimize Azure Stream Analytics SQL Output. In this post, let’s continue the discussion and have a look at some other optimization best practices. Partitioned Table and Indexes: A good way to ensure that there are no bottlenecks during partition writes is to use partitioned SQL tableContinue reading “Azure Stream Analytics SQL Azure Output and In-memory Tables”

In this post, we will discuss options available in Azure Stream Analytics to output results to an Azure SQL Database and how to optimize the configuration setting to achieve optimal throughput. Depending on the SQL database schema design, indexing and compression, the easiest way to enhance performance for the Azure Stream Analytics job is toContinue reading “Optimizing Azure Stream Analytics output to Azure SQL Database”

In this post, we will discuss some important concepts related to handling time in Azure Stream Analytics. Event time: The instance of time when an event occurs (happens). Processing Time: The instance of time when the ingested event reaches the processing stage. Typically, the event processing system will take a few moments to process theContinue reading “Time Concepts in Azure Stream Analytics”

In this post, we are going to discuss about the various states an Azure Stream Analytics job can be in. There are four states for an Azure Stream Analytics Job: 1. Running: This means that the Stream Analytics job is working as intended i.e. reading incoming event data from the inputs, processing the data andContinue reading “Azure Stream Analytics Job States”

Azure Stream Analytics is a real time, big data, events processing and analytics engine. Stream Analytics jobs can accept multiple inputs and are highly integrated with other Azure services. Azure Stream Analytics natively supports inputs from Azure Event Hubs, Azure IoT hub and Azure Blob Storage. Stream Analytics data inputs can be of two types:Continue reading “Azure Stream Analytics Inputs”