A Fine Slice Of SQL Server
Kevin Wilkie doesn’t want to spend that extra money:
Now that we’ve loaded our Costs Dashboard with all sorts of goodness, let’s take it a step further and make it even more useful. The goal: track unused warehouses to help manage spend and reduce clutter.
Read on to see how you can do this.
Leave a CommentMatt Collins shares a deployment guide:
For those who have been following along with our product CF.Cumulus, we have been gearing up for some exciting developments and want to give more power and independence to users. As such, we’re putting together some comprehensive “How-to” guides to simplify the deployment process for Community Edition users.
This deployment guide walks you through setting up CF.Cumulus with the Azure Resources depicted below.
Click through for the guide.
Leave a CommentKevin Hill has a reminder for us:
Here’s a common theme I hear from small IT teams:
“Our SQL Server is in the cloud now. We don’t need a DBA.”
Not quite.
When you’re running SQL Server on cloud virtual machines like Azure SQL VM or AWS EC2, you’re still responsible for a lot of the same challenges you faced on-prem. The difference? Now you’re paying cloud rates for the same headaches. But you don’t have to deal with hardware and some of the other infrastructure hassles.
Read on to see what that entails in practice. Though I’m pretty sure my target audience generally understands this and it’s people two or three levels up who should give Kevin’s post a read.
Comments closedEd Elliott troubleshoots an issue:
I recently encountered an interesting issue with ADF where the publish feature suddenly attempted to republish every single object, claiming they were new, despite having incrementally published changed objects for some time.
We were using the publish feature where you work on a branch until you are happy, then you raise a PR to main, merge to main, and then switch back to ADF and click publish to push the changes to the
adf_publishbranch.
Click through for the answer. I also love how Ed’s tl;dr is “too bad, read it anyhow.”
Comments closedRayis Imayev does a bit of logging:
Azure Data Factory is no exception when it comes to logging options. All your debug or triggered pipeline executions—their parameters passed during execution, statuses, timings, durations, and more, can be monitored natively in Azure Data Studio. Once you immerse yourself in the realm of previously executed pipelines and start seeing all activities, passed input values, processed output results, and variables being transformed into something else that can only be understood by examining internal expressions and many other details, you begin to feel like an investigator meticulously analyzing everything.
Read on to see what kinds of logging options are available and how you can work with them.
Comments closedEd Lima combines expensive with expensive:
Integrating Azure API Management (APIM) with Microsoft Fabric’s API for GraphQL can significantly enhance your API’s capabilities by providing robust scalability and security features such as identity management, rate limiting, and caching. This post will guide you through the process of setting up and configuring these features.
API Management is a really neat service, though it’s rather costly. That’s my biggest complaint about it, though it is a doozy.
Comments closedHarshavardhan Yedla gives us some guidance:
Optimizing a Snowflake data warehouse (DWH) is crucial for ensuring high performance, cost-efficiency, and long-term effectiveness in data processing and analytics. The following outlines the key reasons optimization is essential:
Read on for some tips around optimizing Snowflake warehouses. A lot of this stays at a pretty high level and doesn’t provide detailed guidance, but it’s a good checklist for thinking about your own situation.
Comments closedSudhindra Sheshadrivasan announces a new connector has become generally available:
We’re excited to announce the General Availability (GA) of the Confluent fully managed V2 connector for Apache Kafka® for Azure Cosmos DB! This release marks a major milestone in our mission to simplify real-time data streaming from and to Azure Cosmos DB using Apache Kafka®.
The V2 connector is now production-ready and available directly from the Confluent Cloud connector catalog. This managed connector allows you to seamlessly integrate Azure Cosmos DB with your Kafka-powered event streaming architecture—without worrying about provisioning, scaling, or managing the connector infrastructure.
Read on to learn more about the new connector and what it takes to hook everything up.
Comments closedJosephine Bush doesn’t want to do things one at a time:
I was sick of changing context in Azure Data Studio while cleaning up things discovered in our vulnerability assessments.
Read on to see how you can do this, but also see the complexity involved.
Comments closedBen Johnston looks at the art of the possible:
In my last post I described issues that might stop your migration to a SQL Server Managed Instance (SQL MI). This covers configuration items that differ or are not supported in SQL MI. These likely won’t stop your migration, but they could slow you down if you aren’t ready for these changes.
As with previous issues discussed, testing your migration is key. Validate all of your settings and be prepared to make some changes during your migration process. Most of the incompatible options make sense when you think about the purpose of SQL MI – it is controlled by Microsoft. Hardware settings, local file access, high-availability settings, and auditing are configured differently or completely disabled.
Click through to see what you can and cannot do when it comes to configuration.
Comments closed