What made the event particularly useful was the combination of technical content and direct conversations with speakers, DBAs, consultants and other data professionals.

I had also submitted sessions for Data Saturday Rheinland myself. Although I attended as a participant this time, I was prepared to step in at short notice when one of the scheduled speakers had to cancel.

In the end, the organisers found another replacement, but being available was important to me. Community events depend on people who are willing to contribute, whether through a planned session, an informal discussion or by helping when the original agenda can no longer be maintained.

That also changed my perspective on the event. I was not only there to consume content. I had prepared topics of my own, was ready to speak and paid close attention to how other speakers structured their sessions, explained technical details and interacted with the audience.

Eight hours of PostgreSQL for SQL Server DBAs

The event started on Friday with Grant Fritchey’s full-day pre-conference session:
What a SQL Server DBA Needs to Know About PostgreSQL

For someone who has spent many years working primarily with SQL Server, PostgreSQL is an interesting subject for several reasons.

More organisations are using multiple database platforms. Applications are no longer automatically built on one preferred relational database system, and SQL Server professionals increasingly encounter PostgreSQL in migrations, cloud environments or mixed technology stacks.

That does not mean that every SQL Server DBA needs to become a PostgreSQL specialist. It does mean that we should understand the main differences.

Administration concepts, configuration, indexing, execution plans, maintenance and troubleshooting may look familiar at first, but the underlying behaviour and operational assumptions are not always the same.

An eight-hour session provided enough time to move beyond a simple feature comparison. It allowed Grant to explain where SQL Server experience transfers well, where it can create false assumptions and which PostgreSQL concepts require a different mental model.

That distinction matters. Familiar terminology can sometimes be more dangerous than completely unfamiliar technology because it encourages us to assume that two systems behave in the same way.

Saturday at Data Saturday Rheinland 2026

Saturday offered several sessions that approached data platforms from very different directions.

GenAI and data literacy

Marco Nielinger presented:

GenAI for Data & Analytics: What’s Hot and What’s Not?

Generative AI is currently included in almost every technology discussion, but the practical value varies considerably between use cases. Sessions like this help separate capabilities that can already support real work from ideas that are still mainly demonstrations, experiments or marketing.

Oliver Engels and Katharina Ricken discussed:

Building Data Literacy in the Microsoft Data Platform Era

Technical platforms alone do not solve data problems. An organisation may have modern tools, centralised data and sophisticated reporting while users still struggle to interpret information correctly or make decisions from it.

Data literacy is therefore not a side topic. It influences whether investments in analytics and data platforms produce useful results outside the technical teams that build them.

Grant Fritchey returned with:

SQL Server performance and window functions

Query Performance Enhancements in SQL Server 2025

SQL Server 2025 introduces new options and changes that can affect how we analyse and improve query performance. For DBAs and consultants, the relevant question is not merely which features are new. We also need to understand when they apply, what problems they address and how they change existing troubleshooting practices.

New performance features can support diagnosis, but they do not replace the need to connect query behaviour, waits and infrastructure signals. I discussed that distinction in more detail in SQL Server Monitoring Is Not Diagnosis.

Thomas Hütter closed my personal agenda with:

Window Functions in SQL Server: Brush Up Your Skills

Window functions have been available in SQL Server for years, but they are still frequently underused or used without a clear understanding of their behaviour. Revisiting established functionality is often just as useful as learning about a new feature.

A better understanding of existing SQL capabilities can simplify queries, reduce unnecessary processing and make code easier to maintain.

The value is not limited to the sessions

The sessions were interesting, but events like Data Saturday Rheinland offer something that online training, documentation and recordings cannot fully reproduce.

The speakers are physically present.

When a detail is unclear, you can ask immediately. When a demonstration raises another question, you can discuss it directly with the person presenting it. Speakers can explain why they recommend a certain approach, where it has failed and which constraints are often missing from a simplified technical example.

That direct access changes the quality of the conversation.

The same applies to discussions between sessions.

Talking with other DBAs, consultants, service providers and platform specialists gives me a broader view of how other organisations work:

• Which problems are they currently seeing?
• How are they operating their SQL Server environments?
• Which tools and processes have worked for them?
• Where have technically reasonable approaches failed in practice?
• Why did they choose one solution instead of another?

These conversations do not always produce an immediate technical answer. Often, their value is in questioning assumptions.

A configuration or process may appear normal because it has been used in the same organisation for years. Meeting people from other environments provides a useful comparison. They may solve the same problem differently, face constraints you have never encountered or have already tested an approach you are only beginning to consider.

Community events remain relevant

Most technical information is available online. Documentation, conference recordings, blogs and training platforms make it possible to learn almost anything without travelling to an event.

But access to information is not the same as access to experience.

An in-person community event creates room for follow-up questions, disagreement, practical examples and conversations that were not planned as part of the official agenda.

Having submitted sessions myself and being ready to step in also reminded me that these events only work because people contribute their time, experience and willingness to help.

For me, that remains one of the strongest reasons to attend.

Data Saturday Rheinland 2026 offered strong speakers, relevant topics and plenty of opportunities to exchange experiences with people working on similar problems from different perspectives.

I returned with new technical input, a better understanding of several topics and, equally important, new questions to follow up on.

That is a good result from two days at a community event.

The post What I Took Away from Data Saturday Rheinland 2026 appeared first on CraftedSQL.

This matters because AI does not run in isolation. Once AI systems move closer to real business workflows, the surrounding platform becomes decisive: the data, the permissions, the integration paths, the observability, and the operational model behind it.

Agentic AI is often discussed through demos, assistants, and automation scenarios. But the more relevant question for database and infrastructure professionals is what has to be in place before those scenarios become reliable in production.

Behind every impressive AI workflow, there is still a less visible foundation: data quality, permissions, latency, integration, cost control, observability, and operational discipline.

The interface may become more intelligent. The underlying systems still have to be trustworthy.

Agentic AI data platforms change the conversation, but not the fundamentals

The term “Agentic AI” was clearly central to the event narrative.

That is not surprising. The industry is moving from AI systems that answer questions toward systems that can plan, call tools, interact with applications, and execute tasks within defined boundaries.

From a business perspective, that sounds powerful. From an operational perspective, it immediately raises harder questions.

• What data is the agent allowed to access?
• Which systems can it interact with?
• How are decisions logged?
• Who owns the outcome if an automated action is wrong?
• How do we test the behavior before it touches production processes?

These are not new concerns. They already exist in data platforms, integration systems, database operations, and enterprise applications. Agentic AI simply makes them harder to ignore.

An agent that works with weak data will produce weak decisions faster. An agent with unclear permissions becomes a security risk. An agent connected to unreliable systems will amplify operational noise. And an agent without observability becomes another black box in an already complex environment.

The AI layer may be new. The operational discipline behind it is not.

The data layer is still where reality starts

For database professionals, one of the most relevant takeaways is that Agentic AI data platforms do not reduce the importance of structured, trustworthy data.

It increases it.

Large language models can reason over text, summarize information, and generate responses. But enterprise use cases usually depend on something more concrete: customer records, transactions, policies, logs, telemetry, inventory, financial data, operational workflows, and business rules.

That data lives somewhere.

Often it lives in relational databases. Sometimes in data lakes, warehouses, search indexes, queues, APIs, or application-specific stores. In real environments, usually in a mix of all of them.

This is where the practical work begins.

Before an AI system can support decisions, the organization needs to know which data is authoritative. Before an agent can automate a process, the boundaries of that process need to be understood. Before analytics can support strategy, the underlying data model must be stable enough to trust.

This is not only an AI problem.

In practice, it is a data platform problem. In many companies, it is still very much a database operations problem.

From dashboards to actions

One of the more interesting shifts in the AI discussion is the move from showing information to helping people act on it.

That sounds small, but it changes the responsibility of the platform.

Traditional dashboards visualize signals. Monitoring tools raise alerts. Reports show trends. Query tools expose data. But in many environments, the difficult part is not seeing that something happened. The difficult part is understanding what it means and deciding what should happen next.

This is already familiar in database operations.

A dashboard can show high CPU. That does not explain whether the cause is a missing index, parameter-sensitive execution, parallelism, memory pressure, storage latency, bad application behavior, or a reporting workload running at the wrong time.

A monitoring system can show blocking. That does not automatically explain whether the real problem is transaction design, isolation level, missing indexes, deployment behavior, or application retries.

AI can help connect signals, context, and possible next steps. But only if the platform provides enough reliable context, and only if recommendations stay within clear operational guardrails.

The value is not in replacing expertise. It is in reducing the gap between signal and diagnosis.

This is also why monitoring alone is not enough. Signals only become useful when they are connected to diagnosis, context, and operational decisions. I covered this distinction in more detail in SQL Server Monitoring Is Not Diagnosis.

Cloud platforms are becoming control planes

AWS Summit Japan also showed how cloud platforms are increasingly positioning themselves as operational control planes.

Infrastructure, application runtime, data services, AI services, security, monitoring, cost management, and automation are no longer separate conversations. They are being connected into broader platform stories.

For SQL Server and database professionals, this is worth watching closely.

Even if a system remains on-premises or runs outside AWS, the direction is clear: organizations expect more automation, more integration, more observability, and faster feedback loops. They expect platforms to reduce manual work, not only host workloads.

But there is a risk.

A more automated platform is not automatically a better-operated platform.

If the underlying architecture is fragile, automation can make failure faster. If data ownership is unclear, AI can make confusion more scalable. If monitoring is noisy, agents may simply produce more noise. If cost visibility is weak, cloud-native architectures can become expensive before anyone notices.

The operational model has to mature together with the technology.

What this means for SQL Server environments

At first glance, an AWS Summit may seem far away from day-to-day SQL Server work.

The connection is stronger than it appears.

Many SQL Server environments are still at the center of critical business processes. They hold transactional data, operational history, reporting structures, application state, and years of accumulated business logic. Even when new cloud services and AI tools enter the architecture, those systems do not simply disappear.

They become part of the data supply chain.

That means classic database topics remain highly relevant:

• Performance still matters, because AI-driven workflows often depend on timely access to data.
• Security still matters, because agents and automation increase the importance of permissions and auditability.
• High availability still matters, because intelligent workflows do not help when the authoritative data source is unavailable.
• Data modeling still matters, because poor structure creates ambiguity downstream.
• Observability still matters, because automated decisions require traceable inputs and measurable outcomes.

The future does not remove the need for database expertise. It changes where that expertise connects.

A SQL Server specialist does not need to become an AI researcher. But it becomes increasingly useful to understand how databases, APIs, analytics layers, event-driven systems, and AI services interact in real architectures.

That is where many future troubleshooting and architecture questions will appear.

The uncomfortable truth: AI depends on boring reliability

The most important takeaway is also the least fashionable one: Agentic AI depends on boring reliability.

• Stable platforms.
• Clear data ownership.
• Good logging.
• Sensible permissions.
• Known recovery paths.
• Cost awareness.
• Operational discipline.
• People who understand what happens below the interface.

This is not the language of hype, but it is the language of production.

The more AI moves into business workflows, the more important it becomes to understand the systems that provide the context, execute the actions, and store the results.

That is where databases still matter.

Not because every future system will be built around a relational database. But because every serious business system still depends on reliable state, trustworthy data, controlled change, and operational accountability.

Looking beyond the SQL Server ecosystem

For a SQL Server-focused consultancy, events like AWS Summit Japan are useful because they widen the perspective.

They show where the broader platform market is moving. They make visible how cloud providers frame AI, infrastructure, analytics, developer experience, and operations as connected topics. And they help separate short-term excitement from the structural changes that are likely to affect real environments.

The practical conclusion is simple: AI will not make database operations irrelevant. It will make weak data platforms harder to hide.

Organizations that want to use AI seriously will need more than models and demos. They will need reliable systems, understood data flows, clean operational boundaries, and people who can diagnose what is happening when the abstraction leaks.

That is not new work. But it is becoming more visible.

And for those working close to databases and production systems, that may be the most important signal behind the current wave of Agentic AI data platforms.

The post AWS Summit Japan 2026: Agentic AI, Data Platforms, and the Operational Reality Behind the Hype appeared first on CraftedSQL.

This is one of the reasons why I use AI in SQL Server consulting for many parts of my work. Not as a replacement for SQL Server knowledge, and not as an authority that decides what is true, but as a tool that helps me structure thoughts, review arguments, prepare technical communication, and challenge assumptions before a recommendation is made.

That distinction is important. Using AI intensively does not mean outsourcing judgement. It does not mean replacing operational experience with generated answers. And it certainly does not mean accepting plausible text as verified truth. In my work, AI is useful because it sits next to experience, evidence, and technical review, not instead of them.

This matters especially in areas such as SQL Server performance, security, high availability, migrations, troubleshooting, and infrastructure design. The right answer rarely comes from a generic description of the technology. It depends on workload, data volume, history, risk, operating model, team capability, maintenance windows, business constraints, and the actual state of the system. AI can help organise this complexity, but it cannot understand the environment unless someone provides the relevant context and checks the result against evidence from the actual system.

Where AI helps in SQL Server consulting work

In practice, I find AI helpful in several areas that often sit between the raw technical findings and the final recommendation. It can help structure a vague problem, turn scattered notes into a clearer line of reasoning, compare possible explanations, prepare documentation, review scripts from a readability or risk perspective, or translate technical findings into language that different audiences can understand.

That can be valuable in real troubleshooting work. Many technical problems do not fail because nobody knows the syntax of a command. They fail because the situation is framed too narrowly, the wrong question is asked, visible symptoms are mistaken for root causes, or the explanation to stakeholders is too vague to support a responsible decision. In that space, AI can act as a useful sparring partner. It can help separate observations from assumptions, structure open questions, and make it easier to see where a conclusion is not yet supported by evidence.

After a SQL Server performance analysis, for example, the technical findings may include wait patterns, execution plans, blocking chains, storage latency, workload timing, and application behaviour. AI can help turn those observations into a clearer explanation: what is known, what is still uncertain, which assumptions need validation, and which recommendation is defensible. But the judgement behind that explanation still has to come from the person who understands the environment and has verified the evidence.

Why AI does not replace technical expertise

The dangerous part of AI is not simply that it can be wrong. Experienced technical people are used to incomplete information, misleading first impressions, and assumptions that need to be corrected during analysis. The more serious risk is that AI can be wrong in a very fluent and convincing way.

In technical work, that becomes relevant quickly. A generated answer may sound reasonable while ignoring the SQL Server version, edition, patch level, workload shape, HA/DR design, backup strategy, security model, or operational limits of the team that has to maintain the system afterwards. It may suggest an option that is technically valid but still a poor operational decision. It may also simplify a trade-off that should not be simplified.

This is where technical knowledge, experience, and critical review become more important, not less. AI can propose a line of reasoning, but it cannot be allowed to become the final authority. In a production environment, recommendations need to be tested against logs, metrics, configuration, architecture, dependencies, maintenance windows, rollback options, and business risk. Without that validation, the output may be polished, but it is not reliable.

The same applies to the questions we ask. Good questions are not just a matter of prompt wording. They require an understanding of the system, the problem, the boundaries, and the risk. If the environment is misunderstood, the answer may still read well, but it will be built on the wrong foundation.

Better questions lead to more useful AI results

One practical lesson from working with AI is that useful results rarely come from short, generic requests. They come from context, constraints, and clear review criteria. There is a significant difference between asking AI to “explain a SQL Server issue” and asking it to review a specific line of reasoning with information about workload behaviour, observed symptoms, timing, uncertainty, audience, and acceptable risk.

That is not just a prompt engineering detail. It reflects the same discipline that good troubleshooting requires. Before changing a setting, rebuilding an index, increasing resources, or drawing conclusions from a single metric, the first step is to understand what changed, who is affected, when the problem appears, what the system is waiting on, and which assumptions are still unsupported. AI works better when it is guided with the same kind of clarity.

In that sense, AI rewards people who can think precisely about a problem. The stronger the technical framing, the more useful the response tends to become. The weaker the framing, the easier it is to receive an answer that sounds helpful but remains too generic for the actual environment.

Where AI becomes risky in technical decisions

AI becomes risky when it is used as a shortcut for thinking. That often happens quietly: a generated explanation reads well, the structure looks professional, and the recommendation seems reasonable. Because the output is fluent, it may receive less scrutiny than it deserves.

In technical environments, that is not good enough. A recommendation for SQL Server configuration, indexing, migration, security, monitoring, or high availability must be judged against the real system. It needs to survive questions such as: Does this fit the workload? What are the side effects? What happens during failure? Can the team operate it? How do we verify improvement? What is the rollback plan? Which assumptions are still unproven?

Another important boundary is confidentiality. AI tools can support analysis and communication, but they must be used responsibly. Customer data, internal details, credentials, proprietary information, and sensitive operational context require clear handling rules. Microsoft provides dedicated data, privacy, and security documentation for Azure AI and OpenAI-related services, but convenience is never a valid reason to weaken professional judgement around information security.

The same applies to content. AI can help draft, structure, refine, and challenge technical writing, but it should not replace the experience behind the message. If a text about SQL Server operations could have been written without any real contact with production systems, it may be readable, but it does not build much trust. Technical content needs a point of view, and that point of view has to be earned in practice.

How I use AI in SQL Server consulting without handing over responsibility

My practical rule is simple: AI may support the work, but it does not own the conclusion. I use it to prepare, structure, compare, question, and refine. I do not use it as a substitute for checking the system, reading the evidence, understanding the risk, or making the final technical judgement.

That approach is especially important in consulting. Customers do not need a generated answer that sounds confident. They need a defensible assessment based on their environment. They need someone who can distinguish between a visible symptom and a root cause, between a quick improvement and a sustainable change, between a technically possible option and an operationally responsible one.

Used carefully, AI can improve the quality of that work. It can make incident summaries clearer, customer communication more precise, documentation faster to prepare, and assumptions easier to challenge before they become recommendations. But the value does not come from treating AI as an oracle. It comes from combining the tool with experience, context, evidence, and a habit of verification.

AI in SQL Server consulting makes expertise more visible

I do not see AI as a replacement for strong technical people. In many ways, I see the opposite. The more capable these tools become, the more important it is to know what to ask, what to ignore, what to verify, and where a plausible answer is still not good enough.

In SQL Server work, this distinction is essential. A production database environment is not a theoretical exercise. It has history, dependencies, operational pressure, business impact, and technical debt. Good decisions require more than a clean explanation. They require evidence, context, and judgement.

AI in SQL Server consulting can support that judgement, but it cannot replace it. Used naively, it creates confidence without enough evidence. Used carefully, it becomes a strong tool for clearer analysis, better preparation, and more precise communication. The responsibility, however, remains with the person who understands the system, validates the facts, and has to stand behind the recommendation in a real production environment.

The post Using AI in SQL Server Work Without Handing Over Responsibility appeared first on CraftedSQL.