Solving Data Problems: The 💙of SQL Freelancer

By bradyupton in SQL Freelancer

At SQL Freelancer, we’re passionate about one thing: solving your data problems. We’re not a sales agency trying to push services you don’t need. We’re a team of experienced SQL Server professionals dedicated to helping you navigate the complexities of your data.

Your Data, Our Expertise

Data is the lifeblood of modern businesses, and we understand how crucial it is to get it right. Whether you’re facing performance bottlenecks, database inefficiencies, or data integrity issues, we’re here to help. Our goal is simple: to make your data work for you, not the other way around.

We Don’t Sell Solutions, We Build Them

Unlike many consulting firms, we don’t approach our clients with a one-size-fits-all solution. We listen to your unique challenges, understand your specific needs, and develop tailored strategies to address them. Our focus is on delivering tangible results that solve real problems, not just selling a service.

A Partnership, Not a Transaction

When you work with SQL Freelancer, you’re not just hiring a consultant—you’re gaining a partner. We take the time to understand your business, your goals, and your challenges. Our success is measured by your success, and we’re committed to being there every step of the way as you grow and evolve.

Why We Love What We Do

Every project we take on is an opportunity to dive deep into a new challenge, to apply our skills, and to make a meaningful difference for our clients. We’re problem solvers at heart, and nothing gives us more satisfaction than seeing our clients overcome obstacles and achieve their objectives.

Let’s Solve Your Data Problems Together

If you’re facing a data challenge and need expert guidance, we’re here to help. At SQL Freelancer, it’s not about selling you a service—it’s about solving your problems and empowering your business to thrive. Let’s work together to unlock the full potential of your data.

Delivering AI capabilities in SQL Server and Azure SQL

By bradyupton in Artificial Intelligence - AI, Azure
Delivering AI capabilities in SQL Server and Azure SQL

As a seasoned database administrator, I can’t help but express my excitement about the latest development in integrating generative AI models into SQL Server and Azure SQL. Microsoft’s recent article sheds light on how we can effortlessly harness the power of AI within our database environments, unlocking a new realm of possibilities.

The seamless integration of generative AI models into SQL Server and Azure SQL is a game-changer. No longer do we need to navigate complex external connections or wrestle with intricate architectures. With a simple SQL query, we can tap into the vast potential of AI, empowering us to generate text-based content, gain insights, and make data-driven decisions like never before.

What truly sets this integration apart is its scalability and performance. These AI capabilities can handle large datasets and high traffic without breaking a sweat, ensuring that our data remains always up-to-date and our operations run smoothly. This level of efficiency and data freshness is crucial in today’s fast-paced business landscape.

But the implications of this integration extend far beyond mere convenience. By bringing AI directly into our database environments, we are paving the way for a future where data and intelligence are seamlessly intertwined. Imagine the possibilities – from automated report generation to predictive analytics and beyond, the boundaries of what we can achieve with our data are being pushed further than ever before.

As database administrators, it is our responsibility to stay ahead of the curve and embrace technologies that can truly revolutionize how we work. The integration of generative AI models into SQL Server and Azure SQL is a prime example of such a transformative advancement.

I encourage all my fellow DBAs to explore this exciting development and start experimenting with the power of AI within their database environments. Let’s collaborate, share our experiences, and collectively shape the future of data management.

To get started, I highly recommend checking out Microsoft’s step-by-step guide on using SQL Server and Azure SQL to generate text-based content with generative AI. It’s a fantastic resource that will walk you through the process and help you unlock the full potential of this groundbreaking integration.

Let’s embrace the future and harness the power of AI to drive innovation, efficiency, and data-driven decision-making in our organizations. The time is now, and the opportunities are boundless.

Get Ahead of Year-End with Bulletproof Database Solutions

By bradyupton in Maintenance, Monitoring, Security, SQL Freelancer

As we approach the final stretch of the year, businesses across all industries are gearing up for the increased demands that come with Q4. From handling higher transaction volumes to meeting year-end reporting requirements, your SQL databases will be put to the test. At SQL Freelancer, we understand the critical role that a robust, secure, and optimized database plays in ensuring your operations run smoothly during this peak season.

Why Your Q4 Success Depends on Your Database

The last quarter of the year can be a make-or-break time for many businesses. Whether you’re dealing with a surge in online orders, closing out the fiscal year, or preparing for tax season, your database needs to perform at its best. Any hiccups in your database can lead to delays, data loss, or even security breaches, all of which can severely impact your bottom line.

This is where SQL Freelancer comes in. Our Q3 marketing campaign, “Prepare for Q4 with SQL Expertise,” is designed to help you proactively address potential issues before they arise. By partnering with us, you can ensure that your SQL solutions are ready to handle whatever Q4 throws your way.

What We Offer:

  • Comprehensive Database Audits: We’ll perform a thorough analysis of your current database setup, identifying any weaknesses or areas for improvement.
  • Security Enhancements: Protect your data from breaches with our advanced security measures tailored to your specific needs.
  • Performance Optimization: Ensure your databases run efficiently under heavy loads, reducing latency and improving user experience.
  • Scalability Planning: Prepare for growth by ensuring your databases can scale seamlessly with your business needs.

Don’t Let Your SQL Databases Fall Behind

As you gear up for Q4, don’t let your SQL databases be an afterthought. With SQL Freelancer by your side, you can be confident that your solutions are secure, optimized, and ready for anything. We’re committed to helping you prepare, protect, and excel—so you can focus on what you do best.

Partner with the Leader in SQL Solutions

At SQL Freelancer, we pride ourselves on being a leader in SQL solutions. Our team of experts is dedicated to providing top-notch service, tailored to your unique business needs. Don’t leave your Q4 success to chance—partner with us and get ahead of the curve.

Ready to get started? Contact us today to learn how we can help you prepare for Q4 with bulletproof database solutions.

Introducing database watcher for Azure SQL

By bradyupton in Azure, Monitoring

Reliable, in-depth, and at-scale monitoring of database performance has been a long-standing top priority for SQL customers. Today, we are pleased to announce the public preview of database watcher for Azure SQL, a managed database monitoring solution to help our customers use Azure SQL reliably and efficiently.

 

Managed monitoring for Azure SQL

To enable database watcher, you do not need to deploy any monitoring infrastructure or install and maintain any monitoring agents. You can create a new watcher and start monitoring your Azure SQL estate in minutes.

 

Once enabled, database watcher collects detailed monitoring data from your databases, elastic pools, and managed instances into a central data store in your Azure subscription. Data is collected with minimal latency – when you open a monitoring dashboard, you see database state as of just a few seconds ago.

Read more here…

The Misconception of Slow Queries in SQL Server

By bradyupton in Performance Tuning, Query Plans

 

As a SQL Server DBA, one of my regular tasks is to monitor and optimize the performance of SQL Server databases. A common tool in my arsenal is a script that retrieves the top 10 slowest queries. However, there’s a crucial misconception that needs to be addressed: just because a query appears in this list doesn’t necessarily mean it’s slow or inefficient.

Understanding the Top 10 Slowest Queries

When we ask SQL Server to provide the top 10 slowest queries, it’s essential to understand what we’re actually requesting. The server is simply returning the queries that have the longest duration or highest resource usage among those that were executed during the monitoring period. This does not automatically imply that these queries are poorly written or unoptimized.

Why Optimal Queries Can Appear in the Top 10

There are several reasons why perfectly optimized queries might show up in your top 10 slowest queries list:

  1. High Frequency of Execution: A query that is executed very frequently may have a cumulative duration that places it in the top 10, even if each individual execution is fast.
  2. Data Volume: Queries that operate on large datasets may naturally take longer to execute, even if they are optimized for efficiency.
  3. System Load: External factors such as system load, network latency, or resource contention can temporarily increase the execution time of queries.
  4. Nature of the Task: Some queries are inherently time-consuming due to the complexity of the task they perform, such as complex joins, aggregations, or calculations.

Interpreting the Results

When you identify a query in the top 10 slowest list, it’s important to analyze it in context. Consider the following:

  • Execution Plan: Review the execution plan to check for any inefficiencies or areas for improvement.
  • Frequency of Execution: Determine how often the query is executed and whether its cumulative impact is significant.
  • Data Volume: Assess whether the query is handling large volumes of data and if there are ways to reduce the dataset size.
  • Resource Usage: Look at the resources the query is consuming and explore ways to optimize resource utilization.
  • Comparison with Baselines: Compare the query’s performance with historical baselines to identify any anomalies or trends.

In summary, appearing in the top 10 slowest queries list doesn’t automatically condemn a query as slow or inefficient. It’s a starting point for further investigation and analysis. As a SQL Server DBA, my role is to dig deeper, understand the context, and make informed decisions about optimization. By doing so, we can ensure that our databases run smoothly and efficiently, supporting the needs of our organizations.

Transforming Roles: The Evolution of SQL Database Administrators

By bradyupton in Uncategorized

The role of a SQL Database Administrator (DBA) has evolved significantly over the years as technology and business needs have advanced. This evolution can be traced back to the early days of databases and continues to adapt to the latest trends in data management. In this post, we will explore how the SQL Database Administrator position has changed over the years, focusing on key developments and shifts in responsibilities.

Early Days of SQL DBA (1970s-1980s)

The history of SQL databases can be traced back to the 1970s when the concept of relational databases was first introduced by Edgar F. Codd. During this era, SQL databases were primarily used by large organizations for data storage and retrieval. The role of a SQL DBA was relatively straightforward, involving tasks such as data modeling, schema design, and query optimization.

SQL DBAs in the early days were responsible for managing physical storage, ensuring data integrity, and optimizing database performance. They worked closely with developers to design efficient database schemas and tune SQL queries for better performance. However, the scope of their responsibilities was limited compared to what it would become in the future.

The Rise of Enterprise Databases (1990s)

The 1990s saw the proliferation of enterprise databases, with Microsoft SQL Server, Oracle, and IBM DB2 gaining popularity. This period marked the beginning of a significant shift in the role of SQL DBAs. As organizations increasingly relied on databases to store critical business data, SQL DBAs became more integral to the IT infrastructure.

During the 1990s, SQL DBAs were tasked with database installation, configuration, and maintenance. They had to ensure high availability and data backup strategies to prevent data loss. Additionally, security became a more prominent concern, with SQL DBAs responsible for implementing access controls and encryption to protect sensitive data.

Internet Boom and E-Commerce (Late 1990s-2000s)

The late 1990s and early 2000s witnessed the explosion of the internet and the rise of e-commerce. This had a profound impact on the role of SQL DBAs. Databases became the backbone of online applications, and uptime and scalability became paramount.

SQL DBAs had to adapt to the demands of 24/7 availability and handle large volumes of data. They were now responsible for performance tuning on a massive scale, employing techniques like indexing, caching, and partitioning to ensure fast query response times. Scaling databases horizontally and vertically to accommodate growing workloads became a common challenge.

Cloud Computing Era (2010s)

The 2010s brought about a significant transformation in the IT landscape with the advent of cloud computing. Cloud-based databases, such as Amazon RDS, Azure SQL Database, and Google Cloud SQL, became popular choices for organizations looking to reduce infrastructure costs and increase scalability.

SQL DBAs had to adapt to managing databases in the cloud, which introduced new challenges and opportunities. They had to master cloud-specific database services and learn how to optimize costs while maintaining performance and security. Automation and scripting also became crucial skills as cloud providers offered tools for infrastructure as code (IAC) and database management.

Data Explosion and Big Data (2010s-Present)

The explosion of data in the 2010s, driven by social media, IoT devices, and increased digitization, posed another major shift in the role of SQL DBAs. Traditional relational databases were no longer sufficient to handle the sheer volume of data being generated.

SQL DBAs had to adapt to the world of big data, which included technologies like Hadoop, NoSQL databases, and distributed data processing frameworks. They needed to understand when to use traditional SQL databases and when to leverage alternative solutions for specific use cases. This required a broader skill set and the ability to work with a variety of data storage and processing technologies.

Data Security and Compliance (2010s-Present)

With data breaches becoming more prevalent, data security and compliance became a top priority for organizations. SQL DBAs found themselves taking on additional responsibilities related to securing data, implementing encryption, and ensuring compliance with regulations such as GDPR and HIPAA.

SQL DBAs also had to stay updated on the latest security threats and vulnerabilities and implement best practices to protect databases from unauthorized access and data breaches. This aspect of the role required a deep understanding of cybersecurity principles and the ability to work closely with security teams.

Automation and DevOps (2010s-Present)

In recent years, automation and DevOps practices have transformed the way SQL DBAs work. DevOps principles emphasize collaboration between development and operations teams, with a focus on automating repetitive tasks and achieving continuous integration and continuous delivery (CI/CD).

SQL DBAs have embraced automation tools and scripting languages to streamline database deployment, configuration management, and monitoring. They now play a critical role in enabling the rapid release of database changes while maintaining stability and reliability. This shift has also led to a more proactive approach to database management, with DBAs actively participating in the development process.

Data Analysis and Business Intelligence (2010s-Present)

As organizations recognize the value of data-driven decision-making, SQL DBAs have expanded their roles to include data analysis and business intelligence (BI) tasks. They are now involved in creating data warehouses, designing data models for analytics, and supporting BI tools like Tableau, Power BI, and QlikView.

SQL DBAs work closely with data analysts and data scientists to ensure that data is available, accurate, and accessible for reporting and analysis. This shift highlights the need for SQL DBAs to have a broader understanding of the business context and the ability to translate data into actionable insights.

Machine Learning and AI Integration (2020s-Present)

The integration of machine learning (ML) and artificial intelligence (AI) into applications has further expanded the role of SQL DBAs. They are now tasked with managing databases that store and serve data for ML and AI models. This includes optimizing database performance for real-time inference, handling large datasets for training, and ensuring data quality for ML/AI algorithms.

SQL DBAs may also collaborate with data scientists to deploy ML models within databases and establish data pipelines that feed data to these models. This intersection of traditional database management and emerging technologies highlights the evolving nature of the role.

Conclusion

In conclusion, the role of a SQL Database Administrator has undergone significant changes over the years, reflecting advancements in technology, the growth of data, and evolving business needs. From its humble beginnings as a data storage and retrieval specialist, the SQL DBA has become a multifaceted professional responsible for ensuring the availability, performance, security, and strategic use of data within organizations.

As we move into the future, SQL DBAs will continue to adapt to emerging trends, including cloud-native databases, data analytics, AI/ML integration, and the evolving cybersecurity landscape. The ability to learn and evolve with the ever-changing technology landscape will remain a key characteristic of successful SQL DBAs, ensuring their continued relevance in the world of data management and IT infrastructure.

Enhanced patching for SQL Server on Azure VM with Azure Update Manager

By bradyupton in Azure, Security 0 Comment

With Azure Update Manager, unlike with the existing Automated Patching feature, you’ll be able to automatically install SQL Server Cumulative Updates (CUs), in addition to updates that are marked as Critical or Important.    

Azure Update Manager is a unified service that helps manage updates for all your machines. By enabling Azure Update Manager, customers will now be able to:   

  • Perform one-time updates (or maybe Patch on-demand): Schedule manual updates on demand 
  • Update management at scale: patch multiple VMs at the same time 
  • Configure schedules: configure robust schedules to patch groups of VMs based on your business needs:  
  • Periodic Assessments:  Automatically check for new updates every 24 hours and identify machines that may be out of compliance  

thumbnail image 1 of blog post titled Announcing public preview of enhanced patching for SQL Server on Azure VM with Azure Update Manager

 

Read more here…

SSIS Series: How to use Conditional Split

By bradyupton in Business Intelligence, Integration Services (SSIS), SSIS Series 0 Comment

From Microsoft, the Conditional Split transformation can route data rows to different outputs depending on the content of the data. The implementation of the Conditional Split transformation is similar to a CASE decision structure in a programming language. The transformation evaluates expressions, and based on the results, directs the data row to the specified output. This transformation also provides a default output, so that if a row matches no expression it is directed to the default output.

You can configure the Conditional Split transformation in the following ways:

  • Provide an expression that evaluates to a Boolean for each condition you want the transformation to test.
  • Specify the order in which the conditions are evaluated. Order is significant, because a row is sent to the output corresponding to the first condition that evaluates to true.
  • Specify the default output for the transformation. The transformation requires that a default output be specified.

Let’s take a look at how this transformation might be used in the real world.

Open Visual Studio and drag a Data Flow task into the design pane. Open the Data Flow task and drag in an OLE DB Source task. For this post, I’m going to use the AdventureWorks2019 database and the HumanResources.vEmployeeDepartment view.

This view has some good data to play around with, but we’re going to focus on the Department and Start Date columns. Let’s pretend the bossman needs to see all of the Employees in the Quality Assurance (QA), Production and Sales Department in a separate database table. Bonus, he needs to see all of the Production employees split up into two more tables based on who started before and after Jan 1, 2010. All other employees can go into their own table. Got it? Great! That’s 5 total tables. QA=1, Production=2, Sales=1, Leftovers=1 Let’s go.

Back in Visual Studio, drag in a Conditional Split task and connect it to our OLE DB Source.

Open the Conditional Split task editor and you’ll see a few options (from left to right, top to bottom):

  1. We can use columns and/or variables and parameters in our expressions that define how to split the data flow.
  2. We can use functions such as Date/Time, NULL and String in our expressions that define how to split the data flow.
  3. These are the conditions that define how to split the data flow. These need to be set in priority order; any rows that evaluate to true for one condition will not be available to the condition that follows.


Let’s start adding some conditions for our data. First, we’ll add a condition for all of our QA Department Employees. I’ll name the output “QA” and my condition is pretty simple whereas Department == “Quality Assurance”.

I’ll do the same for Production, Sales and Leftovers (everything else that doesn’t satisfy a condition). Since Leftovers is everything else we’ll just change the name of the Default Output name to identify it.

Let’s go ahead and add our destination tasks (except for Production since we need another condition) and link them to the appropriate condition. See below for QA as an example. When we drag our connector to our destination task we get prompted with an Input Output Selection box. Here is where we choose our Condition that will match up with our table. For the screenshot below, we’ll choose QA output for our QA destination.

Now that we have QA mapped, go ahead and map Sales and Leftovers.

Looks great!  QA, Sales and Leftovers are mapped successfully. Let’s take a look at adding another Conditional Split task for Production. Drag a Conditional Split task into the design pane and connect it to the current Conditional Split. It automatically maps to Production since it’s the only output left.

From our new Conditional split task, let’s open the editor and configure the date conditions for Production. We’ll leave the Default output name box as is since we shouldn’t have any leftover data from this split.

Now we can map the two new conditions to their appropriate destinations.

Cross fingers and hit Execute.

Yay, no red X! Let’s take a look at our SQL tables to make sure everything exported correctly.

Boom. Let’s go grab a bourbon!

Using ChatGPT with SQL Server

By bradyupton in Artificial Intelligence - AI, Azure, Development 0 Comment

If you’re in the tech field you’ve probably heard the buzz around ChatGPT and Artificial Intelligence.

ChatGPT is a pre-trained language model developed by OpenAI. It uses deep learning techniques, specifically a transformer neural network architecture, to generate human-like text. It can be fine-tuned on specific tasks such as language translation, question answering, and text summarization. ChatGPT can be used to generate a wide range of text, including conversation, news articles, stories, and more. Because it is pre-trained on a large corpus of text, it can generate text that is often indistinguishable from text written by a human.

To preview ChatGPT, shoot over to https://openai.com, create a login and you can try it for free (at the time this post was written).

For this post, since I’m a DBA, I want to test how it performs when I give it questions about SQL statements.

So let’s start! I asked the following question:

“How can ChatGPT be used to learn TSQL?”

And here’s a great response:

ChatGPT can be used to learn TSQL in several ways:

  1. You can use ChatGPT to generate TSQL code snippets and queries based on natural language prompts. For example, you can ask ChatGPT to "generate a TSQL query to select all columns from a table named 'customers'" and it will respond with the appropriate TSQL code.
  2. You can also use ChatGPT to explain TSQL concepts and provide examples. For example, you can ask ChatGPT to "explain how to use the JOIN clause in TSQL" and it will provide a detailed explanation and examples of how to use the JOIN clause in TSQL.
  3. You can also train ChatGPT model on TSQL text data and fine-tune the model for generating TSQL queries, this way you can generate more accurate and complex TSQL queries.
  4. You can use ChatGPT as a code completion tool to assist you in writing TSQL code.

 

Not a bad start. Let’s see if it can create a database.

“Can you write SQL code to create a SQL database named AI?”

Look correct. Let’s test in SQL Management Studio.

OK, OK, that was too easy ChatGPT. Let’s see if it can create a table in our new database and insert some dummy data.

“Can you write SQL code to create a table called Orders in my AI database and populate with some test data?”

Let’s test the code.

Looks good. How about a basic SELECT statement?

“Can you write SQL code to give me all data from my Orders table?”

Again, looks good. It even goes as far as explaining it and giving an example by only selecting specific rows. This is great for learning.

Let’s see if it can create an aggregate query.

“Can you write SQL code to give me the Customer that has the highest TotalAmount from the Orders table?”

From my question, I was hoping for something a little different, but I can’t get mad at the results. The query uses LIMIT 1 instead of SELECT TOP 1 which would work in MySQL, but doesn’t exist in SQL Server. It was really close though and it even gives me an example and explanation of how this would be used in a JOIN.

Let’s be more specific. If I change the question to specify SQL Server:

“Can you write SQL code to give me the Customer that has the highest TotalAmount from the Orders table in SQL Server?”

Again, not really what I was looking for, but it works. ChatGPT ended up writing more code than it needed and even wrote this statement in a common table expression (CTE). I was looking for something more along the lines of this:

SELECT TOP 1 CustomerID, MAX(TotalAmount) as MaxAmount
FROM Orders
GROUP BY CustomerID
ORDER BY MaxAmount DESC

Either way, both statements work and produce the same results.

How about something a little more difficult such as creating a partition. Partitions are heavily used in other database platforms so I’m going to specify SQL Server again in this question.

“Can you write T-SQL code to partition my Orders table, OrderDate column by year on SQL Server?”

This was a little more challenging and it wrote out the Partition Function and Partition Scheme statements correctly, but it added a OrderDateRange column as an integer and then it tries to create a clustered index where OrderDate is datetime and OrderDateRange is int so the end result is a failure.

All in all, I think this is a great tool for learning basic (and even some advanced) SQL, but it still has some bugs to work out before it tries to replace me. 😉

 

SSIS Series: How to use SSIS Balanced Data Distributor

By bradyupton in Business Intelligence, Integration Services (SSIS), SSIS Series 0 Comment

From Microsoft, the Balanced Data Distributor (BDD) transformation takes advantage of concurrent processing capability of modern CPUs. It distributes buffers of incoming rows uniformly across outputs on separate threads. By using separate threads for each output path, the BDD component improves the performance of an SSIS package on multi-core or multi-processor machines.

The Balanced Data Distributor transformation helps improve performance of a package in a scenario that satisfies the following conditions:

  1. There is large amount of data coming into the BDD transformation. If the data size is small and only one buffer can hold the data, there is no point in using the BDD transformation. If the data size is large and several buffers are required to hold the data, BDD can efficiently process buffers of data in parallel by using separate threads.
  2. The data can be read faster than the rest of the data flow can process it. In this scenario, the transformations that are performed on the data run slowly compared to the rate at which data is coming. If the bottleneck is at the destination, the destination must be parallelizable though.
  3. The data does not need to be ordered. For example, if the data needs to stay sorted, you should not split the data using the BDD transformation.

Let’s dive in and take a quick look at how the Balanced Data Distributor works.

Go ahead and open Visual Studio, it might take a minute to load.

We’re going to use AdventureWorks2019 database for this example. The SalesOrderDetail table has over 121k records so that’s a good candidate.

SELECT * FROM Sales.SalesOrderDetail

Now that we have data let’s go back over to Visual Studio and see if it’s still spinning.

Drag in a Data Flow task, double click to open and then let’s drag in an OLE DB Source and a Flat File Destination task.

I’m going to configure the source to point to my AdventureWorks2019 database and the destination to point to a .csv file on my local laptop.

Easy enough. Let’s go ahead and run this and see what happens.

We can see that our 121k rows were written to our CSV file and the CSV file ended up being 12.3 MB. That’s pretty large for an email file attachment and might crash your laptop even trying to open this file. BDD not only offers performance benefits by using multiple threads, but it can also break up large files into smaller ones. IMO, this is what makes this task great.

We need to get this file down to less than 4MB so we’ll need to break this up 4 times. With that said,  let’s add the BDD task between the source and destination tasks. There is nothing configurable *in* this task, however, there are some properties that may need to be tweaked. After adding the BDD task, we’ll need to add 3 more flat file destination tasks and 3 more flat file connection managers. End result should look something like this:

That’s really about it. Let’s fire it off and see the results of our flat files.

It worked! 121k rows were written across 4 flat files. Our flat file size is less than 4MB each and we can send these very easily via email. This is a very quick and easy way to split data between files, however, there is no order to these records so an ORDER BY is not helpful here. If the goal is to separate data by a category or condition then you’ll need to use a Conditional Split task which I wrote about in this post.