Senior Vice President | Managing Director of Data and Architecture | 7-time Microsoft Data Platform MVP | Microsoft Regional Director | RedGate Ambassador | Friend of Redgate Program
In the rush to adopt artificial intelligence, many organizations have quickly built ethical frameworks, compliance protocols, and technical safeguards. These “guardrails” are necessary, but not sufficient.
Because AI isn’t just about algorithms and outputs. It’s about choices, power, and humanity. And that’s where leadership steps in.
True responsible AI doesn’t begin with code; it begins with character.
The Illusion of Safety Through Policy Alone
“Guardrails” suggest containment: as long as the framework stays between the lines, all is well. But AI systems aren’t static; they learn, evolve, and engage in dynamic contexts.
While guardrails help prevent obvious failures like bias, hallucinations, or data misuse, they don’t address the deeper questions:
Why are we deploying this model?
Who benefits, and who might be left behind?
What values are being encoded in the AI’s design?
These aren’t just technical questions, and they demand leaders who think beyond checklists.
From Technical Stewards to Ethical Visionaries
Responsibility in AI means building the right systems; not just safe ones. That takes leaders who:
Model humility – AI can feel like a superpower. But responsible leaders embrace its limits and admit what they don’t know.
Cultivate diverse input – Inclusive design starts with inclusive dialogue. Visionary leaders invite voices from every facet of society.
Champion transparency – AI systems shouldn’t be black boxes. Leaders must push for explainability, auditability, and openness.
“Guardrails are reactive. Leadership is proactive.”
Culture Is the Operating System
Even the most rigorous policies mean little without the right culture behind them. Culture drives how AI is actually deployed in practice.
Leaders must foster cultures rooted in:
Ethical reflexes – Encouraging teams to ask “should we?” – not just “can we?”
Continuous learning – AI ethics isn’t a one-time checklist. It evolves as the technology evolves.
“Culture eats policy for breakfast. And leaders set the tone.”
The Mandate of Human-Centered Innovation
Responsible AI isn’t just about minimizing risk. It’s about elevating the human experience. That includes:
Using AI to enhance access and equity across industries
Prioritizing models that serve the public good; not just profit
Redefining success metrics to include autonomy, wellbeing, and dignity
The future isn’t shaped by technology alone. It’s shaped by the values of those who wield it.
Leadership Beyond the Line
Guardrails help keep us safe. But leadership helps us steer.
In this transformative age, the leaders who stand out won’t be those who simply avoid disaster. They’ll be the ones courageous enough to define what good looks like, and bold enough to pursue it.
Responsible AI isn’t a destination. It’s a daily decision.
In today’s enterprise landscape, agility and reliability go hand-in-hand. As organizations modernize legacy infrastructure and scale operations across borders, the challenge is no longer just about moving fast – it’s about moving smart. That’s where the combination of Redgate’s powerful database DevOps tools and Microsoft Azure’s cloud-native ecosystem shines brightest.
At the intersection of robust tooling and scalable infrastructure, building a framework that supports high-volume conversions, minimizes risk, and empowers continuous delivery across database environments; the addition of Redgate’s Flyway has strengthened the ability to manage schema changes through versioned, migration-centric workflows.
Let’s unpack what this looks like behind the scenes.
Core Architecture: Tools That Talk to Each Other
Flyway Enterprise and Redgate Test Data Manager: Flyway Enterprise supports build and release orchestration, lightweight schema versioning and traceability, while giving rollback confidence, and Test Data Manager supports privacy compliance..
Azure SQL + Azure DevOps: Targeting cloud-managed SQL environments while using Azure DevOps for CI/CD pipeline orchestration and role-based access controls.
The architecture aligns development and ops teams under a unified release process while keeping visibility and auditability at every stage.
Versioned Migrations with Flyway
Flyway introduces a migration-first mindset, treating schema changes as a controlled, versioned history. It’s especially valuable during conversions, where precision and rollback capability are paramount.
A typical Flyway migration script looks like this:
— V3__add_conversion_log_table.sql CREATE TABLE conversion log ( id INT IDENTITY(1,1) PRIMARY KEY, batch_id VARCHAR(50), status VARCHAR(20), created_on DATETIME DEFAULT GETDATE() );
This is tracked by Flyway’s metadata table (flyway_schema_history), allowing us to confirm applied migrations, detect drift, and apply changes across environments consistently.
CI/CD Pipelines: From Code to Cloud
With the use Azure DevOps to orchestrate full database build and deployment cycles. Each commit triggers Flyway Enterprise and Redgate Test Data Manager stages that:
Confirm schema changes.
Package migration scripts.
Mask sensitive data before test deployment.
Deploy to staging or production environments based on approved gates.
This integration allows engineers to treat their database as code – reliable, scalable, and versioned – without losing the nuance that data systems demand.
Compliance, Transparency & Trust
Redgate tools also ensure that conversion efforts meet enterprise-grade audit and compliance standards:
Drift Detection & Undo Scripts via Flyway Enterprise for rollback precision.
Immutable Audit Trails captured during each migration and deployment step.
Masked Test Data with Redgate Data Masker ensures sensitive info is protected during QA stages.
Performance Gains & Operational Impact
Implementing this strategy, I’ve seen:
Deployment velocity increase 3x.
Conversion accuracy improves with automated validation steps.
Team alignment improves with shared pipelines, version history, and documentation.
Most importantly, database deployment is no longer a bottleneck – it’s a competitive advantage.
Getting Back to the Basics
While the tools are powerful, the continued focus stays on strengthening foundational discipline:
Improve documentation of schema logic and business rules.
Standardize naming conventions and change control processes.
Foster cultural alignment across Dev, Ops, Data, and Architecture teams.
Database DevOps is both practice and a mindset. The tools unlock automation, but the people and processes bring it to life.
Final Takeaway
Redgate + Azure, now powered by Flyway, isn’t just a tech stack; it’s a strategic framework for high-impact delivery. It lets you treat database changes with the same agility and discipline as application code, empowering teams to work faster, safer, and more collaboratively.
For global organizations managing complex conversions, this approach provides the blueprint: automate fearlessly, confirm meticulously, and scale intelligently.
In today’s data-driven economy, organizations are no longer asking if they should invest in data, they are asking how fast they can turn data into decisions. The answer, increasingly, points to Microsoft Fabric.
Fabric is not just another analytics tool – it is a strategic inflection point. It reimagines how data is ingested, processed, governed, and activated across the enterprise. For CIOs, data leaders, and architects, Fabric represents a unified, AI-powered platform that simplifies complexity and unlocks agility.
Strategic Vision: From Fragmentation to Fabric
For years, enterprises have wrestled with fragmented data estates – multiple tools, siloed systems, and brittle integrations. Microsoft Fabric flips that model on its head by offering:
OneLake, a single, tenant-wide data lake that eliminates duplication and simplifies governance.
Copilot-powered intelligence, enabling users to build pipelines, write SQL, and generate reports using natural language.
This convergence is not just technical – it is cultural. Fabric enables organizations to build a data culture where insights flow freely, collaboration is frictionless, and innovation is democratized.
Technical Foundations: What Makes Fabric Different?
Microsoft Fabric is built on a robust architecture that supports every stage of the data lifecycle:
Unified Workloads
Fabric offers specialized experiences for:
Experience
Purpose
Data Engineering
Spark-based processing and orchestration
Data Factory
Low-code data ingestion and transformation
Data Science
ML model development and deployment
Real-Time Intelligence
Streaming analytics and event processing
Data Warehouse
Scalable SQL-based analytics
Power BI
Visualization and business intelligence
Each workload is natively integrated with OneLake, ensuring consistent access, governance, and performance.
Open & Flexible Architecture
Fabric supports open formats like Delta Lake and Parquet, and allows shortcuts to external data sources (e.g., Amazon S3, Google Cloud) without duplication. This means:
Seamless multi-cloud integration, reduced storage costs, and faster time-to-insight
Real-Time & Predictive Analytics
With Synapse Real-Time Analytics and Copilot, Fabric enables both reactive and proactive decision-making. You can monitor live data streams, trigger automated actions, and build predictive models – all within the same environment.
Business Impact: Efficiency, Governance, and Scale
Fabric is not just a technical upgrade – it is a business enabler. Consider these outcomes:
Lumen saved over 10,000 manual hours by centralizing data workflows in Fabric, enabling real-time collaboration across teams.
Organizations using Fabric report faster deployment cycles, improved data quality, and stronger compliance alignment through built-in Microsoft Purview governance tools.
Fabric’s serverless architecture and auto-scaling capabilities also ensure that performance scales with demand – without infrastructure headaches.
For most of my career, I have lived in the tension between data potential and operational reality. Countless dashboards, disconnected systems, and the constant refrain of “Why can’t we see this all-in-one place?” – these challenges were not just technical; they were strategic. They held back decisions, slowed down innovation, and clouded clarity.
When Microsoft Fabric was introduced, I will be honest: I was cautiously optimistic. Another tool? Another shift? But what I have found over the past few months has genuinely redefined how I think about data strategy – not just as a concept, but as an everyday capability.
Stitching It All Together
Fabric does not feel like another tool bolted onto an existing stack. It is more like a nervous system – a unified platform that brings Power BI, Azure Synapse, Data Factory, and real-time analytics into one seamless experience. The moment I began exploring OneLake, Microsoft’s single, tenant-wide data lake, I realized the gravity of what Fabric enables.
No more juggling data silos or manually reconciling reports across teams. The clarity of having one source of truth, built on open formats and intelligent orchestration, gave my team back time we did not know we were losing.
AI as an Accelerator, not a Distraction
I have also leaned into Copilot within Fabric, and the shift has been tangible. Tasks that once required hours of scripting or SQL wrangling are now powered by natural language – speeding up prototype pipelines, unlocking what-if analysis, and even supporting junior teammates with intuitive guidance.
Fabric AI features did not just boost productivity, they democratized it. Suddenly, it was not just the data engineers who had power; analysts, business leaders, and even non-tech users can participate meaningfully in the data conversation.
Whether you are navigating data mesh architectures, scaling AI initiatives, or tightening governance through tools like Microsoft Purview, Fabric lays the foundation to lead with data – efficiently, securely, and intelligently.
For me, this journey into Fabric has been about more than technology. It is a shift in mindset – from reacting to data, to owning it. And as I step more into writing and sharing what I have learned, I am excited to help others navigate this transformation too.
The Future of Data Strategy Starts Here
Microsoft Fabric signals a shift from tool-centric data management to a platform–centric data strategy. It empowers organizations to:
Break down silos and unify data operations. Embed AI into every layer of analytics. Govern data with confidence and clarity. Enable every user – from engineer to executive – to act on insights.
In short, Fabric is not just the next step, it is the next wave.
AI is no longer a niche capability – it is a leadership catalyst. As Microsoft continues to push boundaries with tools like Azure and Fabric, the demands on today’s tech leaders are shifting from execution to orchestration.
Gone are the days when leadership was about optimizing operations or protecting the status quo. Modern leaders must be architects of adaptability, guiding their teams through complexity with vision, responsibility, and digital fluency.
“The measure of intelligence is the ability to change.” — Albert Einstein
Microsoft Fabric exemplifies this evolution. By weaving together data, governance, and analytics into a unified ecosystem, it challenges leaders to rethink how information flows, how decisions are made, and how innovation scales.
According to IDC, by 2027, 75% of enterprises will operationalize AI across their business processes, citing platforms like Azure and Fabric as critical enablers.
Real-Life Impact: Lumen’s Leadership in Action Lumen, a global enterprise connectivity provider, faced fragmented data systems and manual processes that slowed decision-making. By adopting Microsoft Fabric, they unified data ingestion, storage, and analytics; cutting 10,000 hours of manual effort and enabling near real-time insights across departments.
Marketing and sales teams now collaborate seamlessly, dashboards refresh every 10 seconds, and executives gain instant clarity on campaign ROI. Fabric did not just improve efficiency – it redefined how Lumen leads with data.
“Instead of wrestling with systems, our teams are focused on impact.” — Jerod Ridge, Director of Data Engineering, Lumen
Are you ready to lead in the new era of innovation? Start by exploring Fabric’s design philosophy, rethinking your data strategy with Azure, and considering how your leadership style can evolve alongside the technology.
Keep building the future – one insight, one decision, and one bold move at a time.
Let’s be honest – data is the backbone of modern business success. It is the fuel that drives smart decisions, sharp strategies, and competitive edge. But there is a hidden problem quietly draining productivity: data silos.
What is the Big Deal with Data Silos?
Picture this – you have teams working hard, digging into reports, analyzing trends. But instead of sharing one centralized source of truth, each department has its own stash of data, tucked away in systems that do not talk to each other. Sound familiar? This disconnect kills efficiency, stifles collaboration, and makes decision-making way harder than it should be.
How Data Silos Wreck Productivity
Blurry Vision = Ineffective Decisions Leadership decisions based on incomplete data lead to assumptions rather than informed facts.
Wasted Time & Redundant Work Imagine multiple teams unknowingly running the same analysis or recreating reports that already exist elsewhere. It is like solving a puzzle with missing pieces – frustrating and unnecessary.
Slower Processes = Missed Opportunities When data is not easily accessible, workflows drag, response times lag, and the business loses agility. In fast-moving industries, those delays can mean lost revenue or stalled innovation.
Inconsistent Customer Data = Poor Experiences When sales, marketing, business units, and support teams are not working off the same customer data, you get mixed messages, off-target campaigns, and frustrated customers.
Breaking Free from Data Silos
To break free from stagnation, proactive action is essential:
Integrate Systems – Invest in solutions that connect data across departments effortlessly. Encourage Collaboration – Get teams talking, sharing insights, and working toward common goals. Leverage Cloud-Based Platforms – Make real-time access to critical data a priority. Standardize Data Practices – Guarantee accuracy and consistency with company-wide data policies.
Data silos are not obvious at first, but their impact is massive. Fixing them is not just about technology, it is about a smarter, more connected way of working. When businesses focus on integration and accessibility, they unlock real efficiency and stay ahead of the game.
In the intricate world of business, where precision and efficiency are paramount, managing database dependencies can often feel like navigating a labyrinth. Imagine having a tool that not only simplifies this process but also uncovers hidden efficiencies, ensuring your institution remains agile and error-free. Enter Redgate’s SQL Search – a game-changer for database administrators striving to maintain robust and responsive systems. Discover how this powerful tool can revolutionize your approach to database management and propel your institution toward unparalleled operational excellence.
Understanding SQL Search
Redgate’s SQL Search is a free tool that integrates seamlessly with SQL Server Management Studio (SSMS) and Visual Studio. It allows us to search for SQL code across multiple databases and object types, including tables, views, stored procedures, functions, and jobs. The tool is designed to help database administrators and developers find fragments of SQL code quickly, navigate objects, and identify dependencies with ease.
Use Case: Finding Dependencies Within Tables
One of the most valuable features of SQL Search is its ability to find dependencies within tables. Dependence can include references to columns, foreign keys, triggers, and other database objects. Identifying these dependencies is essential for tasks such as schema changes, performance optimization, and impact analysis.
Scenario: An institution needs to update a column name on a critical table but is unsure of all the stored procedures, views, and functions that reference this column.
Solution: Using SQL Search, we can perform a comprehensive search to identify all dependencies related to the column. Here is how:
Install SQL Search: Ensure SQL Search is installed and integrated with SSMS or Visual Studio.
Search for Dependencies: Open SQL Search and enter the column name in the search bar. SQL Search will return a list of all objects that reference the column, including stored procedures, views, functions, and triggers.
Analyze Results: Review the search results to understand the scope of dependencies. This helps in assessing the impact of the column name change and planning the necessary updates.
Update References: Make the required changes to the column name and update all dependent objects accordingly. SQL Search ensures that no dependencies are overlooked, reducing the risk of errors and downtime.
Benefits for Enterprise Institutions
Implementing SQL Search offers several benefits:
Efficiency: SQL Search significantly reduces the time required to find and manage dependencies, allowing us to focus on more strategic tasks.
Accuracy: By providing a comprehensive view of dependencies, SQL Search helps prevent errors that could arise from overlooked references.
Impact Analysis: The tool enables thorough impact analysis before making schema changes, ensuring that all affected objects are identified and updated.
Performance Optimization: Identifying and managing dependencies can lead to better database performance, as redundant or inefficient references can be optimized.
Redgate’s SQL Search is an invaluable tool for teams looking to enhance their database management practices. By leveraging its powerful search capabilities, we can efficiently find and manage dependencies within tables, ensuring accuracy and optimizing performance. Whether it is for routine maintenance or major schema changes, SQL Search provides the insights needed to make informed decisions and maintain a robust database system.
Implementing SQL Search can transform the way one manages database management, leading to improved operational efficiency and reduced risk of errors. Consider integrating this tool into your workflow to experience its benefits firsthand.
In the bustling world of sports, leadership appears as a beacon of hope and direction. Imagine a football team, standing on the brink of a crucial match. The players, each with their unique strengths and weaknesses, look towards their captain for guidance. This captain, embodying the essence of leadership, understands that success is a collective effort. The leader knows that every player must feel valued and motivated to contribute their best. Through open communication and a collaborative spirit, the leader fosters a culture where teamwork thrives.
As the game progresses, challenges arise. The opposing team scores, and the morale of our team dips. Yet, the captain stays undeterred. The leader has faced setbacks before and knows the importance of resilience. With a positive outlook and unwavering determination, the leader inspires teammates to persevere. They rally together, driven by the belief that they can overcome any obstacle.
Behind the scenes, the captain’s journey is marked by discipline and commitment. The leader’s training regimen is rigorous, demanding both physical and mental fortitude. The leader adheres to a strict schedule, continuously striving to improve skills. This dedication is mirrored in the leader’s approach. The leader is committed to the vision and dedicated to the team’s growth, setting the tone for collective success.
Strategic thinking is another hallmark of the leader’s approach. On the field, the leader devises game tactics, predicting challenges and adapting strategies in real-time. This ability to think critically and make informed decisions guides the team towards their goals. Off the field, the leader analyzes situations, ensuring that every move is calculated and purposeful.
Emotional intelligence plays a crucial role in the leader’s approach. The captain experiences intense emotions, from the thrill of victory to the agony of defeat. Yet, the leader manages these emotions with grace, supporting composure and empathy. The leader understands teammates’ feelings, creating a supportive environment where everyone feels understood and valued.
Leading by example, the captain proves hard work, dedication, and sportsmanship. The leader’s actions speak louder than words, earning respect and loyalty from the team. The leader embodies the values preached, setting a powerful precedent that motivates others to follow suit.
Through the lens of sports, we see that leadership is not just about directing others; it is about inspiring them. The qualities developed through sports-minded discipline are essential for effective leadership in any domain. These qualities include teamwork, resilience, discipline, strategic thinking, emotional intelligence, and leading by example. By adopting these principles, individuals can become inspiring leaders. They drive their teams to success. This applies both on and off the field.
“Leaders aren’t born, they are made. And they are made just like anything else, through hard work. And that’s the price we’ll have to pay to achieve that goal, or any goal.” — Vince Lombardi
Microsoft Fabric is transforming real-time analytics for financial institutions. It provides a unified data platform. This platform integrates various data sources into a single, cohesive system. This integration breaks down data silos. It enhances decision-making and customer insights. Fabric’s real-time intelligence capabilities allow financial institutions to extract insights from data as it flows. This enables immediate decision-making. It supports critical functions like fraud detection, risk management, and market trend analysis.
With AI embedded throughout the Fabric stack, routine tasks are automated. Valuable insights are generated quickly. This boosts productivity and keeps organizations ahead of industry trends. Additionally, Fabric ensures data quality, compliance, and security. These elements are crucial for handling sensitive financial information. They also help in adhering to regulatory requirements. The architecture is scalable to support the needs of financial institutions. They are dealing with gigabytes or petabytes of data. It integrates data from various databases and cloud platforms. This creates a coherent data ecosystem.
Real-time analytics allow financial institutions to respond swiftly to market changes, making informed decisions that drive competitive advantage. By adopting Fabric, financial institutions can unlock new data-driven capabilities that drive innovation and keep a competitive edge.
Moreover, Microsoft Fabric’s ability to deliver real-time analytics is particularly beneficial for fraud detection and prevention. Financial institutions can track transactions as they occur, identifying suspicious activities and potential fraud in real-time. This proactive approach not only protects the institution but also enhances customer trust and satisfaction. The speed of real-time analytics allows immediate addressing of potential threats, reducing the risk of financial loss and reputational damage.
Besides fraud detection, real-time analytics powered by Fabric can significantly improve risk management. Financial institutions can continuously assess and manage risks by analyzing market trends, customer behavior, and other relevant data in real-time. This dynamic risk management approach allows institutions to make informed decisions quickly, mitigating potential risks before they escalate. The ability to respond to changing market conditions is a critical advantage. Addressing emerging risks in real-time is vital in the highly volatile financial sector.
Furthermore, the integration of AI within Microsoft Fabric enhances the predictive analytics capabilities of financial institutions. By leveraging machine learning algorithms, institutions can forecast market trends, customer needs, and potential risks with greater accuracy. This foresight enables financial institutions to develop more effective strategies, improve their operations, and deliver personalized services to their customers. The predictive power of AI is significant. It, joined with real-time data processing, helps financial institutions stay ahead of the competition. They also meet the evolving demands of the market.
Microsoft Fabric’s technical architecture is designed to support complex data operations seamlessly. The integration structures like Data Engineering, Data Factory, Data Science, Real-Time Intelligence, Data Warehouse, and Databases into a cohesive stack. OneLake, Fabric’s unified data lake, centralizes data storage and simplifies data management and access. This integration eliminates the need for manual data handling, allowing financial institutions to focus on deriving insights from their data.
Fabric also leverages Azure AI Foundry for advanced AI capabilities. It utilizes machine learning efficiently. This enables financial institutions to build and deploy AI models seamlessly. This enhances their predictive analytics and decision-making processes. The AI-driven features, like Copilot support, offer intelligent suggestions and automate tasks, further boosting productivity. Additionally, Fabric’s robust data governance framework, powered by Purview, ensures compliance with regulatory standards. It centralizes data discovery and administration. It governs by automatically applying permissions and inheriting data sensitivity labels across all items in the suite. This seamless integration ensures data integrity and transparency, essential for building trust with customers and regulators.
Lastly, Fabric’s scalability is a key technical advantage. It supports on-demand resizing, managed private endpoints, and integration with ARM APIs and Terraform. This ensures that financial institutions can scale their operations efficiently. They can adapt to changing business requirements without compromising performance or security.
Long-term, Fabric will play a crucial role in the future of data analytics. It offers a unified platform that seamlessly integrates various data sources, enabling more efficient and insightful analysis. It handles large-scale data with high performance and reliability. This ability makes it indispensable for driving innovation. It also supports informed decision-making in the analytics landscape.
I am excited to cover the Microsoft Keynote on Day 2: Redgate Keynote: Simplifying Complexity – Making the Database Work in the Real World. As the database landscape grows increasingly complex and the pace of change accelerates, robust database practices are essential to manage this complexity effectively. However, fully leveraging the value of databases remains a significant challenge.
In this keynote, Redgate will present real-life stories, insights, and solutions, highlighting both the human and technical challenges associated with databases. We will be joined by a respected industry expert from IDC Europe and a fellow IT leader who is at the forefront of addressing these challenges. This session will feature the latest research, best practice advice, personal anecdotes, and demonstrations of new product offerings designed to help you harness the benefits of mature database practices and unlock the full potential of your data estate.
Updates to follow:
98 sessions, 50 clinic meetings, and a massive amount of networking events.
Day 2 will bring forth more sessions, expo expedition, community zone, community experts clinic and over 70 more sessions starting today.
Women in Technology Luncheon featuring Jes Chapman as the Keynote speaker will be in Ballrooms 2 and 3 over lunch.
Kellyn Gorman takes the stage providing some stats such as 21 % of organizations are using synthetic data for testing.
71% of organizations in the survey were using manual methods to create testing data, which is incredibly time-consuming.
Graham McMillan, CTO at Redgate, talks about all things releases and how the complexity is.
Digital Technology spend will expand seven times faster than the global economy in 2024
Speed (46%), Quality (43%), Efficiency (43%), and Productivity (28%) to help deliver excellence by 4 strategic priorities.
Excessive technical debt forces overspending on infrastructure
Average hours for DBAs to deploy databases is a thing
Time spent on new app functionality is part of slowing business.
Infrastructure environments are changing, bringing forth some additional challenges in today’s world which lends issues to Operational and Governance challenges. Security and Control, Cost Control, Cloud Sprawl, Visibility, Skills…..
Best practices to help get out of a messy middle, but Sharing the Pain: Core challenges for DevOps and DBAs (Operational challenges, evolving DevOps and business pressures, Heterogeneity, and Data Governance).
Six Core Data Desires – Data Mobility, Data Integrity and Quality, Data Availability, Cyber and Ransomware Resilience, Data Integration, Secure Data Access from anywhere.
One of the big discussion points is breaking down silos, automation, and making things go.
Building automation on deployments and supplying blueprints for specific configurations that will help provision Infrastructure Provisioning including databases, application servers, cloud services, and web or file services……brings forth ease of use, independence, efficiency, compliant by design, and securely by design. – APG
“Increased automation does not sidestep controls”
Data needs to become part of the deployment process where applicable
Be efficient, be innovative, and be secure.
74% of IT teams are now using more than one data platform.
25% are using more than four data platforms.
18% are making daily changes
50% increase in changes at short notice between 2022-24
84% who utilize AI say it delivers improved productivity to reduce time spent on DB deployments, Amplify the signal in the noise, and accelerate time-to-market
68% don’t collaborate between developers and operations. Bridging the gap between development and database operations.
I’m thrilled to be covering the Microsoft Keynote: Fuel AI Innovation with Azure Databases on Day 1 of the PASS Data Community Summit. Data is the driving force behind innovation, powering the development of transformative AI applications. In this keynote, join Shireesh and the Microsoft engineering team as they delve into harnessing the power of data across the Azure databases portfolio to drive groundbreaking advancements. Embark on a journey with vector search, multi-agent apps, and more, and discover how to unlock new patterns like retrieval augmented generation (RAG). Explore the latest database innovations in SQL Server, Azure SQL, Azure Cosmos DB, Azure Database for PostgreSQL, and Azure Database for MySQL that enhance operational efficiency, deliver personalized user experiences, and revolutionize our interaction with technology.
Be sure to check back for live blogging updates throughout the keynote to stay informed on all the latest developments!
Live Updates:
Over 1700 attendees from 46 countries are in attendance this week.
5 topic tracks
200+ sessions and speakers.
45% first timers
Steve Jones did a great welcome and initial overview of the conference, stats, and talking about the pressures of work and how we deal with those pressures. Continuous learning about what people go through and how it interacts with data.
53% say upskilling is their biggest challenge.
A huge welcome to the scholars. Multiple scholarship programs with a round of applause from the audience.
November 17th, 2025 will be the next date for the PASS Data Summit here at the same location.
Shireesh Thota takes the stage next……
Fuel AI innovation with Azure Databases – a data compass that you can trust.
Shireesh honoring the tradition of PASS Data Summit back from early 2000 through each decade.
135+ user groups and 126,000 members of Azure Groups worldwide.
SSMS 21 and Copilot in SSMS is here!!!!!!
Bring cloud manageability to SQL Server anywhere….manage, govern, and protect your SQL Server from Azure.
Preview – Migration Assessment from SQL Server enabled by Azure Arc.
General Available – bi-directional disaster recovery with link feature in Azure SQL managed Instance.
In Preview is the next gen general purpose on Azure SQL managed Instance. 32 TB of storage, 500 dbs, lower storage latency, improved storage performance, and customizable I/O performance.
Build modern, AI-ready applications on cloud-scale databases – Azure SQL Database, Azure Cosmos DB, Azure Database for Postgres.
Preview – Native vector type and functions in Azure SQL Database
Coming soon – Vector Index with DiskANN
Announcing – Azure SQL Database Hyperscale enhancements – Storage up to 128 TB
Generally Available – Serverless auto-pause delay to 15 minutes.
Preview – Vector indexing and search in Azure Cosmos dB for NoSQL
Generally Available – flat and quantized flat vector indeed in Azure Cosmos dB for NoSQL,
Preview – DiskANN indexing in Azure Database for PostgreSQL
"You are the CEO of your destiny" 