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Category: Cloud

Comparing Azure Event Hubs to Apache Kafka

Published 2024-09-03 by Kevin Feasel

Dharmbir Kashyap makes a comparison:

In the realm of event streaming and real-time data processing, choosing the right platform is critical to the success of your project. Two of the most popular options available today are Azure Event Hub and Apache Kafka. Both platforms offer robust solutions for handling large volumes of streaming data, but they are designed with different architectures, features, and use cases in mind. This blog post will delve into the key differences between Azure Event Hub and Kafka, helping you determine which platform is best suited for your specific needs.

Read on for an overview of each product and where each product fits.

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vCore-Based Subscription Limits for Azure SQL DB and Synapse Dedicated SQL Pools

Published 2024-08-27 by Kevin Feasel

Raj Tiwari announces a change in subscription limits:

New vCore based limits: The new limits will be based on vCores per Subscription per Region, which will be directly equivalent to DTU and DWU.

Default logical servers limit: The previous limits on Logical Server DTUs have been discontinued. All new and existing subscriptions will now have a default limit of 250 logical servers. 

Configurable vCore limits: Subscription vCore limits can now be easily managed through the support section on the Azure Portal, with approvals typically processed within minutes.

Read on to learn more about these limits and how you could extend them.

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Compressing Indexes and Shrinking Azure SQL MI Databases

Published 2024-08-27 by Kevin Feasel

Kendra Little has a good reason for an often-bad act:

Shrinking databases in SQL Server isn’t fun – it’s slow, it causes blocking if you forget to use the WAIT_AT_LOW_PRIORITY option, and sometimes it persistently fails and refuses to budge until you restart the instance. You only want to shrink a SQL Server database when you’ve got a good reason and a lot of patience.

If you’re using Azure SQL Managed Instance and you haven’t already used data compression on your indexes and shrunk your databases, you probably have two good reasons to do both of those things: performance and cost reduction.

Compressing indexes is very often (almost always?) a good thing. Shrinking databases is very often (again, almost always?) a bad thing. This is like a buddy cop movie for your database.

Kendra gives some good advice but also lays out a warning if you’re on General Purpose V1, so read the whole thing.

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Always Encrypted and Key Vault

Published 2024-08-22 by Kevin Feasel

Rod Edwards starts a series on Always Encrypted:

For the purposes of this post, i’m going to show how to set up Always Encrypted for column data held in an OnPrem SQL Server (which could be MI, or Azure SQL DB ) using keys stored in Azure Key Vault. And showing you some of the many potential issues that you need to be aware of along the way.

I’m not going to talk about the Secure Enclaves flavour here, just the ‘common or garden’ Always Encrypted.

Read on to learn more.

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Speech to Text with Streamlit and Azure AI

Published 2024-08-21 by Kevin Feasel

I have a new video:

In this video, I show how we can integrate with the Azure AI Services Speech service, using two different methods to capture speech from the microphone via our Streamlit application and submit that to Azure OpenAI.

Check out the video and final set of code. There’s an intermediate set of code for detecting a single utterance. But I think the final product works out pretty well.

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Performance Tuning via Query History in Snowflake

Published 2024-08-21 by Kevin Feasel

Kevin Wilkie gets down to tuning:

In our last post, we talked about some of my favorite queries I use in Snowflake to see various items of interest – such as finding the worst-performing queries. For today’s post, though, I want to talk about performance tuning.

Yes, you read that right. We’re going to use query history to do some fun performance tuning.

Click through for two queries that can help you find what you may need to tune.

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PowerShell Script to Move Azure SQL DB from General Purpose to Business Critical

Published 2024-08-20 by Kevin Feasel

Sakshi Gupta shares a script:

Recently, we faced a requirement to upgrade large number of Azure SQL databases from general-purpose to business-critical.

As you’re aware, this scaling-up operation can be executed via PowerShell, CLI, or the Azure portal and follow the guidance mentioned here – Failover groups overview & best practices – Azure SQL Managed Instance | Microsoft Learn

Given the need to perform this task across a large number of databases, individually running commands for each server is not practical. Hence, I have created a PowerShell script to facilitate such extensive migrations.

Click through for the scenarios Sakshi tested, some important considerations, and the script itself.

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Building a SQL Server Failover Cluster in Google Cloud Platform

Published 2024-08-19 by Kevin Feasel

Dave Bermingham talks about availability:

Building a SQL Server Failover Cluster in Google Cloud Platform (GCP) is a powerful way to ensure your databases remain highly available, even in the face of unexpected failures. High Availability (HA) is crucial for any business-critical application. Downtime can mean lost revenue, decreased productivity, and even damage to your company’s reputation. However, creating HA clusters in the cloud, especially in GCP, presents unique challenges—most notably, the lack of shared storage, which has traditionally been a key component of SQL Server Failover Clustering.

Click through for an overview and a whitepaper on the topic.

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Choosing Compute Options for Databricks

Published 2024-08-16 by Kevin Feasel

Matt Collins shares some recommendations:

You can use Databricks for a vast range of applications these days. From handling streaming datasets, running Deep Learning models to populating data model fact tables with complex transformations, choosing the correct compute option can seem a lot like a stab in the dark followed by (potentially expensive) trial end error.

You can choose from an incredible range of configurations in the Databricks Compute User Interface.

This variety is comprised of the Virtual Machine (VM) category, size, availability and access mode (also referred to as family).

Determining the right compute choice for you could be answered by the classic answer “it depends”, but this guide aim to inform the decision-making process — both in terms of cost and performance.

Read on for more information.

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