A Fine Slice Of SQL Server
Eric Rouach replaces SQL Agent:
For future use in our PowerShell script and since we do not want our Server’s credentials to appear in clear text, our next step will the creation of a credential within the Automation account. This will hold the credentials (username and password) that we’d use for connecting to our SQL Server target.
Click through to learn more.
Comments closedDani Ljepava announces support for Managed Instance link is now in public preview:
As of today, we are pleased to announce that the link feature for Managed Instance is available in the open public preview, in all Azure regions worldwide. It can be used with existing, or new managed instances, and SQL Server 2019 Enterprise, or Developer edition, including SQL Server 2022 CTP (available through EAP). We have also released the tooling support for the link in the form of automated wizards available in SQL Server Management Studio, starting from SSMS v18.11.1.
With the link, replicated databases from SQL Server on Managed Instance are usable as R/O secondary replicas. While the link is in operation, transactions commited on SQL Server (primary) are instantaneously committed to Managed Instance (secondary). This provides an exact replica of your SQL Server database on Managed Instance, synced near real-time. The link was built to be resilient, in case of the network being down, SQL Server being rebooted, or maintained, or in case of some other issue, the link will automatically resume replicating where it has left off when the issue has been resolved.
Support for 2019 is a shrewd idea, given the SQL Server version adoption curve for companies. This isn’t going to replace having a proper availability group for high availability or even (most) disaster recovery options, though, because the link is currently one-way—though Dani does mention eventual support for bi-directional operation with SQL Server 2022.
Comments closedJovan Popovic shows off Synapse Link:
The serverless SQL pools enable you to implement near-real-time analytics solutions on top of your Cosmos DB data. Serverless SQL pools with the Synapse Link provide a cost-effective analytics solution for analyzing NoSQL data stored in Cosmos DB, which is not affecting or spending the resource units on your Cosmos DB transactional store. You can run heavy analytics on the serverless SQL pools that will not affect your workload or price of the main Cosmos DB transactional store. The serverless SQL pools enable you to use the T-SQL query language for analytics that enables you to connect the reporting & analytics tools (such as Power BI, Analytics Services) from a large ecosystem that works with SQL Server or Azure SQL database.
When you are integrating the serverless SQL pools in your solution, you need to apply some best practices. There are general best practices for the serverless SQL pools in the Synapse Analytics workspace, but some of these settings are not applicable to the Cosmos DB scenario. Probably you will use only a subset of the best practices that you can find here. In this post, you will find only the best practices that you should apply in the Cosmos DB solution and some additional hints that could help you to optimize your solution.
Click through to see how the process works and a few recommendations.
Comments closedDrew Furgiuele is asking for it:
Consider this: you’ve developed a data ingestion strategy that is taking in remote thermostat readings. Usually, the devices report in on a set frequency and you’re able to calculate aggregate readings an hourly interval. A materialized view could be created that does this calculation and stores the results out for querying. But what if something causes some of this data to become duplicated? You’d first have to eliminate these duplicates, re-ingest the data, and then do your calculations again.
This is where we can leverage creating a materialized view over a materialized view. Our first materialized view will handle the deduplication, and our second can handle the aggregation of the deduplicated data.
Yo dawg, I heard you like materialized views, so I put some materialized views in your materialized views so you can materialize views while you materialize views.
Comments closedRajendra Gupta builds a simple Azure Function:
As a Platform as a Service (PaaS) service, Azure SQL Database enables developers to deploy SQL Database in Azure Cloud without managing the infrastructure. We use SQL Server Agent to schedule jobs to run at a specific schedule in an on-prem SQL instance. However, Azure DB does not have agent functionality.
There are multiple ways to schedule job or batch processes in the Cloud. You can explore the Azure automation series for executing scripts using Azure Logic apps and automation runbooks.
This article focuses on the Azure functions for scheduling a job for Azure SQL Database.
Read on for the process.
Comments closedAnna Hoffman brings us tidings:
Emily Lisa kicked it off by telling us about the latest Azure SQL Database product updates including Azure SQL maintenance windows, zone redundancy for SQL Hyperscale, and configurable backup storage redundancy for SQL Hyperscale.
Dani Ljepava from the Azure SQL Managed Instance product team came on to share a range of new features announced at SQL Bits now available in public preview: Link feature for Managed Instance, data virtualization with ADLS, Advanced notifications for planned events, Hybrid Service Broker, Windows authentication for Azure AD Principals, and the general availability of Maintenance windows. Watch the episode to see a special look at Link feature for Managed Instance!
Pam Lahoud also came on to talk about about the latest improvements that help you manage your SQL Server in Azure Virtual Machines, including the newly released SQL best practices assessment, all available to you by installing the free SQL IaaS Agent extension!
Read on for plenty more links and updates.
Comments closedPaul Andrew wraps up a series:
When we consider this in the context of what I’ve already established in part 1 of the series, I focused on our data products and ownership. Now I want to re-introduce our data domains as a level above our data products. We can even consider this a hierarchy.
– Data Domains
– Data Products
Why?
Read on for that answer.
Comments closedMladen Andzic announces data virtualization in Azure SQL Managed Instance:
Data virtualization capabilities, now in preview in Azure SQL Managed Instance, enable you to execute Transact-SQL (T-SQL) queries against data from files stored in Azure Data Lake Storage Gen2 or Azure Blob Storage and combine it with relational data stored locally in the managed instance using logical joins. This way you can transparently access external data while keeping it in its original format and location. There is no data duplication or need to run and maintain ETL processes, which means that you can extract and deliver insights faster. Currently supported file formats are Parquet, CSV, and JSON.
I’m going to start calling it PolyBase Duck Typing: it’s not actually PolyBase but the syntax is the same and the outcome is the same and the method to enable it is the same and “PolyBase” is a lot easier to say than “data virtualization.” So even though it’s not PolyBase, I’m going to call it PolyBase until there’s a meaningful split.
Comments closedAhmed Mahmoud performs networking wizardry:
Customer wants to connect Azure Data Factory on one subscription to an Azure SQL Server on Virtual Machine (SQL VM) on another subscription. check out the architecture diagram below for more clarification.
Click through for that diagram as well as the process. And between VNet peering and Private Link, I believe (but could be wrong in saying) the traffic would never leave Azure-hosted machines even when it transits between subscriptions.
Comments closedAbhishek Narain announces a pretty nice improvement to Azure Data Factory and Synapse Pipelines:
We are introducing a Script activity in pipelines that provide the ability to execute single or multiple SQL statements.
Using the script activity, you can execute common operations with Data Manipulation Language (DML), and Data Definition Language (DDL). DML statements like SELECT, UPDATE, and INSERT let users retrieve, store, modify, delete, insert and update data in the database. DDL statements like CREATE, ALTER, and DROP allow a database manager to create, modify, and remove database objects such as tables, indexes, and users.
Be sure to read the limitations at the bottom, however.
Comments closed