In this blog post, we’ll go through the setup step by step, with screenshots, to get everything ready to use Copilot in SSMS.
There is already some official documentation on this, but I believe many people will find it clearer to see everything illustrated.

Copilot is not free: you need an Azure subscription which pays according to the number and complexity of prompts.

I will use the API Key but you can also use Microsoft Entra to authenticate to Azure OpenAI for Copilot in SSMS which is probably safer.

Azure OpenAI Service

To start with, we’ll use the Azure OpenAI service.

Click “Create Azure OpenAI”

Create a Resource group if needed and choose a name.

Leave the “All networks” option selected on the Network pane.

AI Model deployment

Now, In the newly created resource click on “Go to Azure AI Foundry Portal”.

Then, on the left panel choose “Deployments” and click “Deploy model” and select “Deploy base model”.

Select “gpt-4o” and Confirm. It’s the only supported model for now.

Then click on “Customize”.

Here you can modify the name and select a Deployment type.
Here, I recommend reading the documentation to choose the option that best fits your needs.
Doc: Deployment types for Azure AI Foundry Models

SSMS

Now go to SSMS and click “Copilot”.

The Azure OpenAI Endpoint and the API Key can be found on the Azure OpenAI Service page, by clicking on “Click here to view endpoints”.

Finally the Open AI Deployment is the name we defined earlier. “ai-snrel-ssms-deployment” for me.

Note that the configuration can be edited under “Tools”, “Options”, “Copilot”.

We are done. I can now start chatting with the AI.

Hopefully, this blog made it easier for you to set up and start using Copilot in SSMS.

Regarding privacy it is written here that no prompts will be used by OpenAI to improve or train models so it should be quite safe.

Written by Steven Naudet

L’article Step-by-Step Guide to Enabling Copilot in SSMS est apparu en premier sur dbi Blog.

This year, I’m here in person and fully intend to make the most of the experience.

For context, SQLBits 2025 is one of the largest Microsoft data platform events in Europe. Taking place in London from June 18 to 21, it features dozens of sessions covering SQL Server, Azure, Power BI, AI, and data engineering. The event brings together experts, MVPs, and Microsoft product team members for intense learning and networking. Known for its high-quality content and friendly atmosphere, SQLBits is a must-attend event for anyone working in the Microsoft data ecosystem.

As you can see on my badge, I’ve chosen to attend a wide range of sessions during the first two days, and on Friday I’ll be taking part in a full-day training on “Real-World Data Engineering: Practical Skills for Microsoft Fabric” led by Prathy Kamasani.

On the agenda, agenda, you’ll see that the trending topics stand out. Personally, I’m very interested in sessions on SQL Server 2025, which has just been released in public beta, or sessions on the SQL Server engine in general, but of course these aren’t the majority of sessions.
The trends are now:

• AI and AI Agents, with multiple sessions exploring how AI is being integrated into the Microsoft data ecosystem including intelligent assistants, automation, and real-world use cases.

• Microsoft Fabric is front and center, everywhere.

• Analytics and Power BI continue to be key pillars, with advanced sessions on DAX, large-scale reporting, data modeling, and real-time analytics.

• Data Transformation and DataOps are well represented, especially through practical sessions on orchestration, data pipelines, automation, and governance.

Today I attended the following sessions:

• How Microsoft’s new, open-source Data API builder fits into Solution Architecture

• Unlocking the Power of Open Mirroring in Microsoft Fabric
• AI Agents in Action: Enhancing Applications with Azure AI Services

• Azure SQL Managed Instance Demo Party

• Empower Your Data with Real-Time Intelligence leveraging Data Activator within Fabric
• From B-Trees to V-Order. Told differently than usual

• Resolving Deadlocks in SQL Server: Practical Demo

It’s a huge amount of information, subjects to explore and ideas for solving real-life problems for our customers.

That was only the first day, but I’m already extremely satisfied with the conference: from the quality of the sessions and the content to the expertise of the speakers. I’m looking forward to making the most of the upcoming sessions, and I highly recommend that any Microsoft Data professional consider attending SQLBits. If not this year, then why not next year?

Written by Steven Naudet

L’article Experiencing SQLBits 2025 in London est apparu en premier sur dbi Blog.

The LinkedIn post by Bob Ward announcing the news can be found here: Announcing SQL Server 2025 Public Preview

In his post you’ll find a summary of the key changes coming with this new release.

Also note that the recommended version of SSMS for SQL Server 2025 is SSMS 21, which was just announced.

Here is the blog post by Erin Stellato: SQL Server Management Studio (SSMS) 21 is now generally available (GA)

There are many changes between SSMS 20 and 21, notably the fact that it’s now based on Visual Studio 2022, includes built-in Copilot integration, and finally introduces a Dark Theme.

I strongly recommend installing it, starting to use it, and providing feedback if you encounter any bugs or areas for improvement. You can do so here.

Now that SQL Server 2025 is available for testing, other blog posts written by my colleagues or myself will likely follow to showcase some of the new features.
I’m particularly thinking of the following:

• A native vector type and vector index (DiskANN)
• Optimized locking
• REST API ensupport with sp_invoke_external_rest_endpoint
• etc

Written by Steven Naudet

L’article SQL Server 2025 Public Preview and SSMS 21 now available est apparu en premier sur dbi Blog.

At the 2024 edition of Microsoft Ignite, two key themes dominated the stage: Artificial Intelligence (AI) and Security. These pillars reflect the current and future technological landscape. Staying true to its innovative legacy, Microsoft unveiled Microsoft Foundry, a unified platform designed to simplify the design, customization, and management of AI solutions.

In this article, I’ll share my hands-on experience testing this innovative platform through an intelligent application built with my own data.

2        Context

The integration of AI in our daily operational is no longer a possibility, but it is a present reality. However, many misconceptions still surround AI, leading to hesitancy or misuse. Here’s a reality check:

Misconceptions about AI:

• AI will replace human jobs.
• AI freely uses your data to train models.
• AI is only for large corporations.
• AI is too complex for average users
• AI is only useful in tech industries

The Reality:

• AI handles repetitive tasks, freeing up humans for creative work.
• Provides a creative starting point (overcoming “blank page” anxiety)
• Reduces human error in routine tasks
• Enables 24/7 operational capabilities
• Facilitates predictive maintenance
• Your data remains secure and isn’t used to train underlying foundation models without consent.

Recognizing the importance of ethics and safety in AI, Microsoft integrates Responsible AI, Microsoft Purview for data governance, and content safety protocols into its solutions:

• Responsible AI: Microsoft emphasizes fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
• Microsoft Purview: A comprehensive tool to enforce governance policies that dictate what AI can return to end users.
• Content Safety: Prevents harmful outputs by monitoring and filtering responses.

3        Intelligent Application Development

3.1      Use Case

For this demonstration, I built an interactive AI solution based on my own data, showcasing Microsoft Foundry’s capabilities.

3.2      Overview of Microsoft Foundry

Microsoft Foundry is a comprehensive platform for developing and deploying generative AI apps responsibly. As stated by Microsoft here, Microsoft is designed for developers to:

• Build generative AI applications on an enterprise-grade platform.
• Explore, build, test, and deploy using cutting-edge AI tools and ML models, grounded in responsible AI practices.
• Collaborate with a team for the full life-cycle of application development.

Microsoft Foundry is incredibly accessible:

• Free to explore
• No sign-in required for basic features
• Full functionality with an Azure account
• Access via https://int.ai.azure.com/

Its main entry point is as per below picture:

Clicking on Explore models open the model catalog. At the time of writing, the platform boasted an impressive 1,812 models – a number constantly expanding. This vast library means there’s likely a model for almost any use case.

3.3      Prerequisites

Before diving into application development, the following resources were set up:

• Resource Group (rg-foundrytest): Contains all resources for efficient management and deletion to avoid costs.
• AI Search (aisearch-foundry): Enables intelligent search capabilities.
• Azure OpenAI (openai-foundry): Provides foundational AI services.
• Custom Data: For this demo, I uploaded the file Multiturn-ContosoBenefits.pdf.

3.4      Step-by-Step Implementation

3.4.1    Project Creation

Project is where you do most of your development work. Projects are easy-to-manage containers for your work—and the key to collaboration, organization, and connecting data and other services

From the Foundry portal, click Create Project and proceed with default settings.

Hub is a collaborative environment where Team members can share the projects

Project is created

After setting up the project, necessary resources were verified in the Azure portal.

3.4.2    Adding Own data to the project

The bringing own data feature supports diverse sources, including on-premises and cloud data as depicted in the following picture:

But I simplified the process by uploading  the Multiturn-ContosoBenefits.pdf as follows:

From the new created project homepage, click on New Data

Then, upload the mentioned Multiturn-ContosoBenefits.pdf

The following screen confirms that custom data are added to the project:

3.4.3    Model Selection and Integration

I selected the model GPT-35-turbo-16k and integrated it to the project through following steps:

As already said, more than 1800 models are available

The integrated model gpt-35-turbo-16k (version:0613) at this level is tied with pre-trained data. Thanks to that, the following query returns valid result

3.4.4    Connect the Model to the custom data

Now, we are instructing the model to use our custom data

The following picture confirms that our model is now linked to our custom data:

As of now, the model is restricted tied with our custom data, it is configured to exclusively utilize custom data.

Because of that, when querying content outside the data source’s scope, the system returned an error as shown below:

However, queries related to the data yield provides precise results. In this case, the system provides accurate and context-specific responses. This demonstrates strict adherence to the defined dataset.

It’s remarkable how seamlessly it provides relevant information!

3.4.5    Integrate into Client Applications

Once the model is fine-tuned, its capabilities are easily integrated to client applications following the below architecture:

Their capabilities can be embedded into a client application using pre-generated code snippets available in languages like C#, Curl, Go, Java, Javascript, JSON and Python.

The following picture shows how to get the generated code

The integration can also be easily done by following the wizard to deploy an App Service as shown below:

4        In Summary

Microsoft Foundry is a game-changer in AI application development. It democratizes AI by enabling users—regardless of technical expertise—to:

• Leverage an extensive catalog of pre-trained models.
• Tailor applications using custom datasets.
• Seamlessly integrate AI capabilities into enterprise solutions.

Stay tuned for more hands-on insights in upcoming posts!

L’article Microsoft Foundry in Action: Building Smarter AI Solutions with Your Data est apparu en premier sur dbi Blog.

He tried to shrink it, but the system wasn’t doing anything and he didn’t know why.

With the column ‘log_reuse_wait_desc’ you can find it out.

When I asked him what was in the ‘log_reuse_wait_desc’ column for the database, there was silence at first, then the question quickly arose as to where the column could be found at all.

But let’s start at the beginning.

I arranged a session with the customer and asked him to show me what he had in mind.

He tried to reduce the log file to the smallest possible size with the following command:

The output is successfull and nothing has happened.

But why?

I quickly found out that this was a database located in an AlwaysOn group.

Some time before his call, an application had made large changes to the database by mistake. This caused the log file to grow enormously. From there also the desire to restore the old size.

In the well-known system table sys.databases there is a column with the name ‘log_reuse_wait_desc’ which tells us why it does not work.

So I executed a query based on the name of the database and the column:

The reuse_wait ‘AVAILABILITY_REPLICA’ was recognizable.

This means that the log file is replicating the transactions that have been carried out. Until this has happened, the log file cannot be manipulated.

We therefore had to wait until all transactions had been replicated and the status had changed:

On the status ‘ACTIVE_TRANSACTION’, we are already able to shrink the previous enlargement.

Then we could reduce the size of the file.

Are there any other log waits?

The following are listed in the official Microsoft documentation:

• NOTHING
• CHECKPOINT
• LOG_BACKUP
• ACTIVE_BACKUP_OR_RESTORE
• ACTIVE_TRANSACTION
• DATABASE_MIRRORING
• REPLICATION
• DATABASE_SNAPSHOT_CREATION
• LOG_SCAN
• AVAILABILITY_REPLICA
• OLDEST_PAGE
• XTP_CHECKPOINT
• SLOG_SCAN

Customers often ask me exactly the opposite. Why can’t I reduce the size of the log file even though I have only made small changes to the database?

The main problem lies with the VLFs.

When the transaction log is not growing, and you’re taking regular log backups, but the log_reuse_wait_desc stays at LOG_BACKUP, the reason is that the previous log backup did not clear any Virtual Log Files (VLFs).

How can this occur?

Consider a database with minimal insert, update, delete, or DDL activity. In this scenario, only a few log records are generated between your regular log backups, and they all reside in the same Virtual Log File (VLF). When the next log backup runs, it backs up these few log records but cannot clear the current VLF, preventing the log_reuse_wait_desc from changing. Once enough changes are made in the database to fill the current VLF and activate the next one, the subsequent log backup should be able to clear the previous VLF, causing the log_reuse_wait_desc to change to NOTHING. However, if the next log backup cannot clear the current VLF, the log_reuse_wait_desc will revert to LOG_BACKUP again.

If you want to find out more about VFLs, take a look at my colleague’s blog entry. He has covered the topic in great detail:

L’article Help yourself out with the ‘log_reuse_wait_desc’ column est apparu en premier sur dbi Blog.

Hello, Readers!

Recently, I was tasked with preparing a demo for Power BI Report Server. Everything proceeded smoothly until I reached the step of deploying a report to the server, where I encountered an interesting authentication challenge that highlights the complexities of working with mixed domain environments. The report deployment phase revealed an unexpected authentication error that required a deeper understanding of Windows authentication mechanisms and network name resolution.

This blog provides a detailed solution to avoid this error, ensuring seamless deployment.

2        Context

To simulate a production-like environment, I architected my lab environment with the following components:

• Development Environment: A domain-joined laptop running Power BI Desktop
• Database Server: A Hyper-V virtual machine (WIN-LAB1) hosting the database infrastructure
• Power BI Report Server: A separate Hyper-V virtual machine (WIN-LAB3) for report hosting

The workflow was designed to:

1. Develop a report on the laptop using Power BI Desktop.
2. Save the report to the Power BI Report Server to enable on-premises sharing of reports and dashboards.

3        Problem Statement

The implementation involved several steps:

3.1      Development environment

My development environment consisted of a domain-joined laptop running Power BI Desktop. It’s crucial to note that Power BI Desktop comes in two distinct flavors:

Standard Power BI Desktop

• Designed for cloud service deployment
• Features monthly release cycles
• Includes preview features

Power BI Desktop for Report Server

• Specifically optimized for on-premises deployment
• Follows a triannual release schedule (January, May, September)
• Limited to generally available features

The required application was Power BI Desktop for Power BI Report Server (September 2024 version), ensuring compatibility with the installed server version. You can download this version from here.

3.2      Database Server

WIN-LAB1 is a VM running Windows Server 2022, with SQL Server 2022 and SSMS 20.2 installed. This VM is not part of an Active Directory domain. The AdventureWorksDW2022 database backup, available here, was used as the data source for report development.

The Developer Edition of SQL Server 2022, licensed for non-productive use can be downloaded here.

3.3      Power BI Report Server

WIN-LAB3 is another VM running Power BI Report Server in Developer mode, which can be downloaded here. Like the database server, this VM is not part of any Active Directory domain.

The server’s database, that resides in WIN-LAB1, is titled ReportServer_PBIRS (instead of the standard ReportServer) because WIN-LAB1 also stored database of SSRS instance (titled ReportServer_SSRS).

Following pictures describe the settings of the Report Server:

3.4      Report development

The report itself was developed using the AdventureWorksDW2022 sample database, analyzing sales trends by year and weekday.

3.5      Report deployment

The deployment process appeared straightforward: File → Save as → Power BI Report Server. However, this led to an “Unexpected error encountered” message that required further investigation.

4        Root Cause Analysis

On WIN-LAB3, neither the Event Viewer logs nor the server log files (C:\Program Files\Microsoft Power BI Report Server\PBIRS\LogFiles) provided useful details.

However …

The development machine’s Event Viewer exposed two critical issues:

• Name Resolution failure on the domain controller
• Active Directory Replication Latency issues

The error occurs because Power BI Report Server defaults to Windows Authentication. This requires:

1. Both machines to be in a trusted domain.
2. Valid credentials for access.

Since WIN-LAB3 is not part of a domain but my laptop is, the system failed to resolve WIN-LAB3’s name and authenticate using valid credentials.

5        Solution

To resolve the issue, I implemented the following steps:

Step 1: Retrieve the IP Address of WIN-LAB3

Connect to WIN-LAB3 and run ipconfig command

• Connect to WIN-LAB3 and execute the ipconfig command to obtain its IP address.

Step 2: Update the Laptop’s Hosts File

• Add an entry mapping the IP address of WIN-LAB3 to its name in the hosts file (C:\Windows\System32\drivers\etc\hosts).

Step 3: Configure Network Credentials

1. Open Credential Manager (Control Panel > Credential Manager > Windows Credentials).
2. Add a new Windows credential:
   • Internet or network address: WIN-LAB3.
   • Username: An existing account on WIN-LAB3 (e.g., WIN-LAB3\Administrator).
   • Password: Corresponding password for the above account.

Following these steps, I successfully saved the report to the Power BI Report Server.

Finally, the report was available in the Report Server

6        Conclusion

When working with Power BI Report Server in an environment where the development machine and server are not in the same domain, success depends on properly addressing two key technical requirements:

• Name Resolution: Ensure proper DNS resolution between development and server machines
• Authentication: Configure appropriate credential mapping in Windows Credential Manager

This implementation demonstrates how to bridge the authentication gap between domain-joined and standalone systems in a Power BI Report Server deployment.

When developing a Power BI report in a separate machine than the Power BI Report Server, you should ensure that the two machines are able to communicate mainly:

• The Development machine (where Power BI Desktop is installed) can resolve the Power BI Report Server name
• A Power BI Report Server valid account is defined in the Development machine via the Credential Manager to allow a seamless communication between the two machines.

Stay tuned for more insights in future posts!

L’article Save Report to Power BI Report Server: Resolving the ‘Unexpected Error Encountered’ est apparu en premier sur dbi Blog.

Hello, Readers!

While exporting data to Azure Blob Storage using SQL Server Integration Services (SSIS), I encountered the “Unable to create Azure container” error. This article will guide you through a straightforward solution, aiming to save you time and prevent similar frustrations in the future.

2        Context

The logic of the example is quite simple and consists of two main steps:

• Extract data from a local database
• Export the extracted data as a text file to Azure Blob Storage

The prerequisites for this process include:

2.1      Azure Storage

For this example, an Azure Storage Account and a Blob Storage Container were created to store the exported data.

2.2      Local Database

The data source for this export is the Product table in the SalesLT schema of the AdventureWorksLT2022 database, a sample provided by Microsoft. You can access it here.

The structure of table Product is described in the following picture:

2.3      IDE

The development environment is Visual Studio 2022, with the SQL Server Integration Services Projects 2022 extension installed.

3        Problem Statement

The goal is to export the following fields from the Product table to a CSV file in Azure Blob Storage: ProductNumber, Name, Color, StandardCost, ListPrice, Size, Weight, and SellStartDate. Here’s a breakdown of the steps I followed:

3.1      Data Flow Creation

I created a Data Flow task with:

• OLE DB Source for reading data from SQL Server.
• Azure Blob Destination for exporting the data to Azure Blob Storage.

3.2      OLE DB Source Settings

To simplify the process:

• A SQL query was used in the SSIS SQL Command Text instead of a stored procedure (as recommended by best practices), avoiding any updates to the AdventureWorksLT2022 database.
• The SellStartDate formatting was handled within the SQL query, eliminating the need for a Derived Column transformation.

Connection Manager settings

OLE DB Source settings

3.3      Azure Blob Destination Settings

The Azure Storage Connection Manager was configured with the necessary access credentials, enabling data export to Azure Blob Storage.

Azure Storage Connection Manager settings

(Azure storage details are retrieve from Azure as shown below)

Azure Blob Storage Destination settings

3.4      Complete flow

After setting up both components, the Data Flow was ready for execution, as shown in the final configuration image.

3.5      Execution

Upon execution, the package fails with the error: “Unable to create Azure Blob container“

Error details are shown in the following pictures:

4        Root Cause Analysis

This error is often due to an issue with Transport Layer Security (TLS). Azure Blob Storage requires connections to use TLS version 1.2. However, the default configuration for the Azure Feature Pack in SSIS may not enforce TLS 1.2, as it depends on the .NET Framework settings on the system.

5        Solution

To resolve this, the system’s .NET Framework settings must be modified to enforce TLS 1.2. This can be done by adding a REG_DWORD value named SchUseStrongCrypto with a value of 1 under the following registry keys:

Registry key creation

Final values after key creation

This registry update forces .NET to use TLS 1.2 for secure connections, satisfying Azure’s requirement.

After applying this update, I reran the SSIS package, which completed successfully.

The Products.csv file was created and uploaded to the Azure Blob Container as expected.

The content of file Products.csv is shown in the below picture

6        Conclusion

For seamless SSIS integration with Azure Blob Storage, ensure that your system enforces TLS 1.2 by updating the .NET Framework registry settings. This resolves the “Unable to create Azure Blob container” error, facilitating successful data export to Azure Blob Storage.

Stay tuned for further insights in upcoming posts!

L’article Export Data to Azure Storage with SSIS: Solving the ‘Unable to Create Azure Blob Container’ Error est apparu en premier sur dbi Blog.

Recently, a colleague approached me, saying, “Hey Nicolas, I’m thinking of going for the PL-300 (Microsoft Power BI Data Analyst Associate) certification. Do you have any advice on how to pass it with confidence?”

As knowledge sharing is one of the core values at dbi services, both internally and externally, I thought it might be helpful to share the insights I provided. My hope is that this guide will help you not only succeed in your certification but do so by:

• Enhancing your preparation
• Reducing exam-related stress
• Avoiding costly retakes

2        Context

Though I reference Microsoft certifications here, the steps in this guide apply broadly to any certification that validates expertise in specific technologies.

To enhance our team’s visibility and showcase our expertise, we recently invested time in pursuing certifications – (AZ-305) or (PL-300) to mention only them – so I have earned a good vision in Microsoft certifications.

Having navigated numerous certifications, I’ve built a formula for preparation that allows me to approach each exam with confidence. I’m excited to share this tried-and-true approach with you.

3        The Winning Recipe: PDT – Practice, Documentation, Test

The foundation of my approach is what I call the PDT formula, broken down into three key elements:

3.1      Get Practical Experience in the Exam’s Focus Area

Certifications are meant to validate your expertise and hands-on experience with a specific technology or field. Exam success isn’t simply about memorizing content; practical experience is essential. This is important because:

• If the exam is based solely on memorized questions, it devalues the certification since it doesn’t reflect real skill.
• If the exam includes unseen material, it’s difficult to pass without real-world experience.

If you work directly with the technology daily, you’re already well-prepared to dive into the next steps. But if you’re lacking hands-on experience, I suggest using Microsoft’s labs, available at labs.azure.com, to gain familiarity with the exam environment.

3.2      Consult Documentation

I always study documentation in two stages to deepen my understanding of the material.

External Documentation: My first go-to resources are reading platforms, like O’Reilly. Here, you’ll find books and videos related to your certification area. For example, a search for “PL-300” brings up the Exam Ref PL-300 Microsoft Power BI Data Analyst by Daniil Maslyuk, which covers certification content thoroughly. These platforms require a subscription but often offer trial periods that are long enough for thorough preparation.

Microsoft Documentation: Microsoft provides free, focused documentation on their official platform at learn.microsoft.com, which aligns closely with their exams and is an invaluable resource.

3.3      Practice with Mock Exams

Practice tests are crucial to familiarize yourself with the exam format and types of questions. Here are the sources I rely on:

Microsoft’s Practice Options: Within Microsoft’s learning paths, you’ll find “Take the practice assessment” and “Launch the sandbox” options, which offer realistic exam practice.

Reading Platforms: O’Reilly also includes practice tests for some exams. Using the PL-300 example, I found the Pearson Practice Test for Microsoft Power BI Data Analyst (PL-300) among the search results.

Other Online Sites: Additional practice test sites can be helpful, but approach them cautiously since the accuracy of answers varies. With solid preparation in the practice and documentation stages, you’ll have the knowledge to evaluate these answers critically and confidently.

4        In Summary

This guide provides a reliable approach to certification success. Certification exams require a significant time commitment, as they are intended to test more than rote memorization. By following the PDT (Practice, Documentation, Test) strategy, you’ll reduce stress and increase your chances of passing with ease.

I’d be thrilled to hear that this approach has helped you in achieving your certification goals!

L’article Ace Your Certification Exams with Confidence: A Proven Recipe for Success est apparu en premier sur dbi Blog.

Dear Readers,

Recently, I encountered the ‘Microsoft.ACE.OLEDB.16.0 Provider is Not Registered’ error while working on a data extraction task using SSIS. In this article, I’ll share how to fix this issue and also guide you through:

• Migrating SSIS packages from SQL Server 2017 to SQL Server 2022 using Visual Studio 2022.
• Deploying SSIS packages to the SSIS Catalog.
• Installing 32-bit driver when 64-bit version is already present.

2        Context

In a recent project, I had to migrate SSIS packages from SQL Server 2017 to SQL Server 2022. Many of these packages used Excel as a data source. Here’s the environment setup:

• Development Environment: Windows Server 2022 with Visual Studio 2022 installed.
• Production Environment: Windows Server 2022 with SQL Server 2022 and SSMS (SQL Server Management Studio) 20.2.

Migrating these packages was straightforward using Visual Studio 2022. Below are some key steps:

2.1      SSIS Catalog Creation

Using SQL Server in the production environment, I created the SSIS Catalog.

Note: The ‘Create Catalog’ option is disabled because each SQL Server instance can have only one SSIS Catalog, and the instance used in this demo already contains an existing SSIS Catalog.

2.2      Setting up Visual Studio

I Installed the SQL Server Integration Services Projects 2022 extension in Visual Studio 2022.

2.3      SSIS Packages Import

Using Visual Studio 2022’s import wizard, I imported SSIS packages from SQL Server 2017. This wizard simplifies the process by extracting the packages and creating a local project.

2.4      Deploying Packages

After the migration, I used the SSIS Project Deployment feature to deploy all the packages into the new SSIS Catalog.

3        Problem Statement           

After deploying the packages to SSIS Catalog, executing them resulted in the error: ‘Microsoft.ACE.OLEDB.16.0 provider is not registered’

This issue also appeared when attempting to import Excel data through SQL Server Management Studio:

Then I got the same error message

4        Root Cause Analysis

This error occurs because the Access Connectivity Engine (ACE) OLEDB provider required to connect Excel files is not installed.

5        Solution

To resolve this issue, I followed these steps:

5.1      Install the 64-bit Microsoft Access Database Engine 2016 Redistributable

Download Microsoft Access Database Engine 2016 Redistributable 64-bit and install it on the two servers.

5.2      Install the 32-bit Microsoft Access Database Engine 2016 Redistributable

Download Microsoft Access Database Engine 2016 Redistributable 32-bit if necessary.

This is required for 32-bit applications like SQL Server Management Studio’s Excel import feature.

The installation of the 32-bit ended up with below error message because the 64-bit was already installed:

To bypass this, use the Command Prompt to force the installation:

5.3      Install Additional Components (depending on your Excel version)

Download and install other components depending on the Excel version used. Details are provided in the below picture:

6        Conclusion

Connecting to Excel files using SSIS requires the ACE OLEDB provider. By installing the correct Microsoft Access Database Engine, you can fix the ‘Microsoft.ACE.OLEDB.16.0 provider is not registered’ error.

Additionally, I walked you through migrating SSIS packages from SQL Server 2017 to SQL Server 2022 , deploying them to the SSIS Catalog and force install a 32-bit driver when a 64-bit is already installed.

Stay tuned for future insights and tips to avoid similar challenges!

L’article Mastering SSIS with Excel – Resolving the ‘Microsoft.ACE.OLEDB.16.0 Provider is Not Registered’ Error est apparu en premier sur dbi Blog.

Dear Readers,

This post serves as an introduction to a six-part series focused on the Integration Runtime (IR) within Azure Data Factory (ADF). Over the course of these six articles, you’ll gain essential knowledge and practical skills to choose and implement the right Integration Runtime for your specific requirements. Each article will explore different aspects of IR, providing you with a comprehensive guide to handling various integration scenarios effectively.

What is Azure Data Factory (a.k.a. ADF)

Azure Data Factory is a cloud-based data integration service that enables the creation and scheduling of data-driven workflows. Its primary strength lies in its capability to interact with multiple types of data sources and orchestrate data flows between them.

What is Integration Runtime?

The Integration Runtime (IR) is the compute infrastructure that ADF uses to provide data integration capabilities across various network environments. There are three types of integration runtimes offered by Data Factory:

• Azure Integration Runtime: Managed by Microsoft and can only access data sources in public networks.

• Self-hosted Integration Runtime: Deployed and managed by you, allowing access to both public and private networks. You’re responsible for patching, scaling, and maintaining the infrastructure.

• Azure-SSIS Integration Runtime: A Microsoft-managed IR designed to run SQL Server Integration Services (SSIS) packages in ADF. This IR can access both public and private networks.

In this series, we will focus on the custom IR, namely Self-hosted IR and Azure-SSIS IR, which offer greater flexibility, such as the ability to interact with private networks and, in the case of SSIS IR, the execution of SSIS packages.

Context and Case Studies

To guide you through the process of understanding the provided solutions, we’ll explore the following fictive scenarios:

Case Study 1: LucyCheck Ltd.

A Medical analysis laboratory where files are stored locally on a PC connected to the Internet. The medical regulatory authority, which operates on Azure and uses Azure Data Factory for its data integration processes, needs to retrieve specific data from the laboratory’s local system. This data will then be integrated into the regulatory authority’s Data Warehouse hosted on Azure.

How should this integration be implemented?

Case Study 2: Helory Swags Ltd

A luxury children’s clothing retailer is in the process of migrating its infrastructure to Azure. According to company policy, all new development must now take place within Azure.

The management requires reporting that involves data from a Line of Business (LOB) system, which is still stored in an on-premises SQL Server and won’t be migrated until next year.

How can an integration solution be implemented to integrate the on-premises SQL Server in the Azure Data Warehouse?

Case Study 3: Moera LinkedTeen Ltd.

A communications company with a hybrid infrastructure and several ADF instances. To fulfill various requests, these  ADF instances need to extract data from the same on-premises SQL Server instance.

What is the best way to implement this integration?

Case Study 4: Aelynn Money Transfer Ltd.

A financial services company is migrating its entire infrastructure to Azure, except for one Line of Business (LOB) system, which will remain on-premises. The company’s data warehouse is populated with data from this LOB using a complex SSIS package. Due to time constraints, this SSIS package cannot be easily redeveloped in Azure Data Factory (ADF).

How can we ensure the continued use of this complex SSIS package in Azure while maintaining its connection to the on-premises LOB data?

Upcoming Solutions in the Series

The upcoming articles in the series will cover the following topics:

• Part 1: Security prerequisites for ADF access to on-premises environment.

• Part 2: ADF access to on-premises file systems.

• Part 3: ADF access to on-premises SQL Servers.

• Part 4: Implementing a shared ADF Integration Runtime.

• Part 5: Point-to-site (P2S) VPN setup.

• Part 6: Using SSIS IR in ADF to connect with on-premises SQL Servers.

Conclusion

By the end of these series, you will have a comprehensive understanding of both Self-hosted and Azure-SSIS Integration Runtimes in Azure Data Factory. Armed with this knowledge, you will confidently choose and implement the most suitable Integration Runtime for your organization’s needs.

Stay tuned for the next instalment of this exciting journey!

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