The Databricks ecosystem is evolving at an incredible pace.

What started as a Lakehouse platform has expanded into a powerful environment for building data-intensive AI applications, intelligent agents, enterprise knowledge systems, and AI-driven solutions.

New capabilities are emerging across the Databricks AI ecosystem. Agent development, Vector Search, Model Serving, Mosaic AI, Unity Catalog, AI/BI, and enterprise AI architectures are changing how we design and build modern data platforms.

For Data Engineers, Architects, Technical Leads, and aspiring AI Engineers, this creates both an opportunity and a challenge.

How do you keep learning when the technology is changing so quickly?

Watching isolated tutorials is not enough. Reading documentation without implementation is not enough. Understanding individual features without knowing how they fit into an enterprise architecture is not enough.

What you need is structured, practical learning. That’s why I’ve brought together two learning initiatives.

1. Forward Deployed Engineer on Databricks

Move beyond building pipelines. Learn to engineer enterprise AI solutions.

The role of a Data Engineer is evolving.

Organizations increasingly need engineers who can work closely with business teams, understand real-world problems, design data and AI architectures, and deliver working solutions.

This is where Forward Deployed Engineering becomes important.

The Forward Deployed Engineer learning path focuses on building the practical engineering foundation required to deliver solutions using Databricks.

What will you explore?

• Python and SQL foundations for modern data engineering.
• Data ingestion, transformation, and processing.
• Apache Spark and Delta Lake.
• Medallion architecture and enterprise data engineering.
• Databricks platform capabilities.
• Building production-oriented data solutions.
• Integrating data engineering with AI capabilities.
• Practical exercises and implementation-oriented learning.

The objective is not simply to learn Databricks features. It is to understand how to engineer solutions that solve real business problems.

Who should explore this?

• Data Engineers looking to move into AI Engineering.
• Developers transitioning into the Databricks ecosystem.
• Cloud and Big Data professionals.
• Technical Leads and Solution Architects.
• Professionals looking to build end-to-end engineering capabilities.

Explore Forward Deployed Engineering on Databricks:

Databricks AI Context... with Akash A Wadhankar | Topmate

2. Context Engineering on Databricks

AI is only as good as the context it receives.

One of the biggest challenges in Enterprise AI is not simply choosing the right model.

It is providing the model with the right information, knowledge, business definitions, relationships, and operational context. Enterprises already have enormous amounts of data.

But data in its raw form is rarely sufficient for intelligent decision-making.

Data must evolve into information. Information must become knowledge. Knowledge must be organized into meaningful context. This is the foundation of Context Engineering.

The journey looks something like this:

Data → Information → Knowledge → Context → Intelligence → Action

The Context Engineering learning path explores how to build this foundation using Databricks. What will you explore?

• Context Engineering fundamentals and architecture.
• Context acquisition and preparation.
• Context modeling and domain ontology.
• Building enterprise knowledge structures.
• Context intelligence and retrieval.
• Context-aware execution.
• Context validation and learning.
• Connecting context with AI agents.
• Designing context-driven enterprise AI solutions.

The focus is on understanding how enterprise data becomes usable context for AI applications and intelligent agents.

Who should explore this?

• Data Engineers entering the AI ecosystem.
• AI Engineers and Agent Developers.
• Enterprise and Data Architects.
• Professionals building RAG and agentic AI applications.
• Technical leaders exploring enterprise AI transformation.

Explore Context Engineering on Databricks:

Databricks AI Context... with Akash A Wadhankar | Topmate

Why learn these two together?

These learning paths address two complementary dimensions of modern AI engineering.

Forward Deployed EngineeringContext EngineeringHow to engineer solutionsHow to engineer contextData and application foundationsKnowledge and context foundationsPractical implementationEnterprise intelligenceEnd-to-end solution deliveryContext-driven AI execution

One helps you understand how to build and deliver solutions. The other helps you understand how to make those solutions intelligent, contextual, and relevant to enterprise needs.

Together, they provide a broader learning journey from Data Engineering towards Enterprise AI Engineering.

My objective: Make structured Databricks learning accessible

I have been building these learning materials with a practical focus.

The digital learning products include:

• Structured eBooks.
• Concepts explained progressively.
• Practical implementation examples.
• Exercises to reinforce learning.
• Quizzes to assess understanding.
• Learning paths designed to connect concepts rather than teach isolated features.

The intention is to help professionals learn systematically instead of spending hours trying to connect information scattered across documentation, tutorials, and announcements.

Technology will continue to change. New features will arrive. Architectures will evolve.

But strong engineering fundamentals, architectural thinking, and the ability to implement solutions will remain valuable.

The next step in your learning journey

If you are a Data Engineer, don’t wait until your current skills become outdated.

Start exploring how data engineering connects with AI engineering, agent development, and enterprise context.

Two learning paths. One direction: Enterprise AI Engineering.

• Forward Deployed Engineer... with Akash A Wadhankar | Topmate
• Databricks AI Context... with Akash A Wadhankar | Topmate

Learn systematically. Build practically. Stay relevant in the AI era.

The future belongs to engineers who can connect data, knowledge, context, and intelligence to solve real business problems.

Databricks AI Is Moving Fast. Are You Keeping Up? was originally published in Towards Data Engineering on Medium, where people are continuing the conversation by highlighting and responding to this story.

We usually organise technical documentation around pages; there are tutorials, how-to guides, reference material and explanations, or whatever artifacts each team deems as necessary.

Each piece serves a different purpose, and this structure helps readers find information while giving authors a framework for organising content. But it can obscure something important.

Documentation is also a representation of the product itself. A product has capabilities, dependencies, constraints, configuration options and behaviours. These characteristics interact. They also change over time as features are introduced, modified or removed.

My take is:

What if we treated these characteristics as a connected product model, and documentation pages as different views into that model?

I think this offers a useful way to reconsider documentation architecture, information consistency and the role of AI in maintaining technical content. I have prepared a breakdown of every aspect of this matter to fuel relevant discussion.

A product has an underlying model

Consider a software feature. This feature gives the wider solution a capability - something it allows a user to do.

It may depend on another component, require a particular configuration or operate under specific constraints. Its behaviour may also differ between product versions.

These are connected facts about the product. We can represent them conceptually as:

• Capabilities: What can the product do?
• Dependencies: What does a capability rely on?
• Constraints: Under which conditions does it operate?
• Configuration: Which settings influence its behaviour?
• Feature facts: What behaviour is associated with a particular feature and version?

These characteristics describe different dimensions of the product. Feature facts connect them to observable behaviour across versions.

The important point is that the model is not static: a feature may acquire a dependency in one release; a configuration option may become mandatory in another; a constraint may be removed or a default behaviour may change.

The resulting product model evolves with the product.

This is a conceptual model, not a requirement to implement every documentation system as a relational database. Its value is in making relationships explicit. Once we think this way, documentation becomes more than a set of descriptions. It becomes an interface through which readers access connected product knowledge.

Git history is the historical record of a solution

A versioned product model needs a historical record. For software projects, Git provides much of the information needed to reconstruct product evolution: commits record changes; pull requests provide review context; release tags identify versions; issues can explain the problems or requirements that led to an implementation.

Together, these records can help answer questions such as:

• When was a capability introduced?
• Which release changed its behaviour?
• Why was a dependency added?
• Was a configuration option deprecated?
• Which implementation change addressed a reported limitation?

However, a repository does not automatically provide a complete or reliable account of product evolution. That depends on the development team maintaining its history with discipline.

Meaningful commit messages, descriptive pull requests, consistent release tagging and traceable links between issues and changes all contribute to the quality of the historical record.

Without these practices, relationships become difficult to reconstruct. When these are not in place, an agent cannot reliably identify why a feature changed if nobody recorded the reason. It cannot establish which release introduced a behaviour if release history is incomplete.

This creates an important dependency:

The quality of a versioned product model depends partly on the quality of the development history from which it is reconstructed.

Git history can provide evidence of change, but does not necessarily provide a complete explanation of intent. That distinction matters when we start using AI to interpret it.

Documentation pages as dashboards

What if we treated each documentation page as a dashboard into the product model?

A dashboard does not display every available data point. It selects and brings together the information relevant to a particular question or audience.

A documentation page can work in a similar way: one page might describe a capability and its configuration, while another might explain a dependency or operational constraint. A reference page might expose the relationship between exact parameters, properties and behaviours.

This way, the underlying product knowledge is connected, and each page presents different views of it.

This also explains why documentation should not be organised solely around the internal structure of a codebase. If we’re talking about artifacts facing the public (aka, users of the product or decision makers that control their adoption), different individuals in various roles approach a product with different goals.

A new user wants to understand how to get started. An experienced operator needs to configure a specific behaviour. A developer may need to inspect an API parameter or understand an architectural constraint.

As a result, each reader needs a different selection of product knowledge. The page should therefore be shaped by three things:

1. Audience: Who is reading?
2. Intent: Why are they reading?
3. Task: What do they need to understand or accomplish?

These determine which parts of the product model matter, how much detail is appropriate and how the information should be presented.

The analogy with dashboards is useful because it separates the underlying knowledge from its presentation. A dashboard does not become a different data model because it displays a different selection of metrics. Similarly, a tutorial and a reference page do not need to represent different versions of the product simply because they serve different purposes.

Diátaxis: different views for different needs

The Diátaxis framework provides a particularly useful way to understand this distinction. It organises documentation into four types based on the reader’s needs:

• Tutorials: help readers learn.
• How-to guides: help readers accomplish a task.
• Reference: provides information to look up.
• Explanation: helps readers understand concepts and context.

These are different modes of presenting knowledge, not four disconnected bodies of product information.

A tutorial might introduce a capability through a guided example. A how-to guide might explain how to configure it for a particular task. Reference documentation might describe the exact configuration parameters. An explanation might discuss the architectural reasons behind the feature.

All four can draw on the same underlying product knowledge. This is where the product-model analogy becomes useful for information architecture:

We can ask whether the documentation pages present the right information for their intended readers, rather than simply checking whether every feature has a page.

A documentation set can contain hundreds of pages and still leave important user needs unanswered. For example, a project might have comprehensive API reference material but no useful tutorials. Or it might have introductory explanations that never provide the task-oriented guidance users need.

Diátaxis helps identify these gaps. It also helps prevent a common problem: combining several documentation purposes into one page until it becomes difficult to use for any of them.

The distinction is not merely stylistic. It affects how readers navigate information and how authors maintain it.

Maintaining connections between views

If different pages represent different views of the same product, we need ways to maintain their connections. This is where documentation tooling becomes important.

Tools such as Sphinx provide mechanisms for structured documentation, semantic object references and cross-references between pages.

These mechanisms help authors connect related concepts and help readers navigate between them. Build-time checks can also identify unresolved references and certain structural problems.

However, there remains an important limitation. Specifically, a successful documentation build does not establish that the content is accurate, complete or consistent with the product.

A reference might resolve correctly while describing an outdated behaviour. A page might build without errors while omitting a newly introduced configuration constraint.

Structural validity and factual validity are different things.

This distinction becomes even more important when we consider documentation as a representation of a changing product.

We need to know not only whether pages are connected, but whether the information they present still reflects the underlying product.

AI as an evidence-review layer

This is where AI could add another layer of review.

Imagine an agent examining the relationship between documentation and development history.

It could:

• Identify documented capabilities and their related configuration options.
• Trace claims to relevant commits, pull requests and release records.
• Compare how a feature is described across different versions.
• Inspect related pages for potentially inconsistent descriptions.
• Flag references to deprecated behaviour.
• Identify changes that may require documentation updates.

The purpose would not be to rewrite documentation automatically, but to make relationships and potential inconsistencies visible. An agent might identify that a feature’s configuration changed in a recent release while an older tutorial still describes the previous behaviour.

That is a useful finding. But it is not proof that the tutorial is wrong.

Perhaps the tutorial deliberately documents an older version. Perhaps the feature behaves differently under a particular configuration.

The author still needs to investigate.

The agent should distinguish between an observed change in the repository, a possible documentation discrepancy and a verified factual inconsistency.

This is also why the quality of development history matters so much.

If the relevant change was never recorded properly, an agent may have incomplete evidence. If a pull request describes the implementation but not the intended user-facing behaviour, the agent cannot reliably infer the missing context.

AI can help inspect relationships. It cannot reconstruct evidence that does not exist.

The larger idea

The central idea is that documentation is more than a collection of pages. It is a representation of a versioned product model.

The product model provides connected knowledge about capabilities, dependencies, constraints, configuration and behaviour.

Documentation pages provide purposeful views into that knowledge. Their structure and level of detail depend on the reader’s audience, intent and task.

Git history provides evidence of how the product changes. Documentation tooling helps maintain structural connections. AI could help examine whether the different views remain consistent with the available evidence.

These are related responsibilities, but they are not interchangeable.

The product model establishes what we know about the product. Documentation makes that knowledge useful to readers. Engineering history helps establish how the product evolved. Editorial review determines whether the resulting explanations are accurate and appropriate.

I think this way of looking at documentation opens up an interesting direction for technical writing and knowledge engineering.

Instead of asking only whether a page is well written, we can also ask:

Does this page present the right view of the product, for the right reader, using information that still reflects the product’s actual behaviour?

That is a much broader question than page quality alone.

What If We Treated Product Documentation as a Data Model? was originally published in Towards Data Engineering on Medium, where people are continuing the conversation by highlighting and responding to this story.

I’ve spent years as a data engineer. I build pipelines, process large datasets, design data platforms, and help businesses make better decisions with data.

But there was always one wall I kept hitting.

I could build the data. I could build the intelligence. Turning it into a beautiful, working application was a different story.

A frontend. API connections. Authentication. Screens. Making it all work together. Too often, building the application took more effort than the idea itself, and good ideas stayed stuck in notebooks.

Then I started experimenting with Lovable. It changed how I build Data + AI applications.

What if English could be your programming language?

Imagine describing an idea in a single sentence:

“Build an application that recommends movies based on someone’s mood.”“Build an application that helps grocery stores spot inventory risks and decide what to reorder.”

Instead of starting with hundreds of lines of frontend code, I start with a conversation. Lovable turns that description into a working application. I can see the screens, test the interactions, refine the design, and keep building in plain English.

To be clear, it’s not magic. You still need to understand your data, design the architecture, connect services, test everything, and validate the results.

But one thing has changed for good:

The distance between having an idea and seeing it work has become dramatically shorter.

My first big moment: rethinking movie discovery

One of my favorite projects is Future of Movie Discovery.

The idea was simple. Why do people spend so much time scrolling for something to watch when AI could find it based on how they feel?

What if you could say, “I’m feeling relaxed and want something funny,” and get recommendations that actually fit?

Here’s how I built it:

• Databricks Free Edition to work with the movie dataset
• PySpark to process and prepare the data
• Embeddings so the application could understand movie descriptions and user preferences
• Lovable to turn that intelligence into an interactive product people could explore

That last step made the difference. Instead of presenting another notebook or technical demo, I could show a working product.

I submitted it to the Databricks Free Edition Hackathon, and something wonderful happened.

Future of Movie Discovery received an honorable mention in the inaugural Databricks Free Edition Hackathon, recognized alongside other innovative Data + AI solutions. You can read the official Databricks announcement.

That recognition meant a lot to me. It was proof that innovation doesn’t require a large team, a huge budget, or months of development. Sometimes it starts with a simple idea, the right tools, and the willingness to experiment.

One application gave me the confidence to build more

After that, I started looking for real problems across different industries.

Grocery Data Intelligence

Grocery Data Intelligence explores how grocery stores can make better inventory decisions. It combines Databricks, data engineering, analytics, and AI to flag stockout risks, potential waste, and recommended actions.

The interesting part isn’t displaying inventory numbers. It’s helping someone understand what those numbers mean and what to do next.

Analytics App

My analytics app asks a different question: what if people could ask about their data in plain English instead of writing SQL or waiting for someone to build a report?

Lovable handled the application experience while I focused on the analysis, calculations, and verification behind each answer. This project taught me the most important lesson of all:

Getting AI to generate an answer is easy. Making that answer trustworthy is the real work.

DwaraOne

DwaraOne is an HR-focused AI application concept. It explores how employees and managers could interact with enterprise data, policies, and AI agents through one simple interface, instead of jumping between several systems.

Lovable let me experiment quickly with how that experience should look and feel.

Different industries. Different problems. One common thread:

I spent more time thinking about the problem and less time building every screen from scratch.

What I really like about Lovable

After building multiple POCs (proofs of concept), a few capabilities stand out:

• Conversational building. I can discuss an idea, plan the changes, and build the application through conversation.
• The visual editor. I can refine the interface directly, without writing a new prompt for every small design tweak.
• Connectors and integrations. They make it easier to see how external services, data platforms, and application components work together.
• Backend options. Lovable Cloud and integrations like Supabase simplify the early stages of building an MVP (minimum viable product).

But my favorite part is bigger than any single feature:

Lovable gives me room to experiment.

I can try an idea, see how it works, find what’s missing, and improve it. Not every idea becomes a product. Not every prompt produces the right output. Sometimes the app needs debugging, better architecture, stronger security, or a complete redesign.

That’s fine. The difference is that I discover those problems in days, not months.

What I learned along the way

If you’re a data person thinking about building with AI tools, here’s what four projects taught me:

1. Your data is still the product. A great interface on top of bad data is just a prettier way to be wrong.
2. Be specific in English. The more clearly you describe users, screens, and outcomes, the better the first version.
3. Verify everything. Treat AI-generated answers and code as a strong first draft, not the final word.
4. Ship early, then iterate. A working prototype teaches you more than a perfect plan.
5. Respect the fundamentals. Security, performance, testing, and data quality still matter, especially for enterprise applications.

Data engineers have a new opportunity

This is an exciting time to be a data engineer.

For years, our work has focused on moving data, transforming it, maintaining pipelines, and delivering trusted information to downstream systems. That work is still essential.

But AI opens another door. We can now take our understanding of data and build intelligent applications around it:

• Databricks helps prepare, process, and govern the data.
• AI models and agents help interpret information and support decisions.
• Lovable turns those capabilities into experiences people can actually use.

This doesn’t mean traditional software engineering is going away. It means more people can take part in building software.

A data engineer can explore product development. An analyst can prototype an idea. A student can build a working application. And someone with deep industry knowledge can finally turn that knowledge into something interactive.

English may not replace programming languages, but it is becoming a powerful new way to build with them.

My advice is simple: start building

Don’t wait until you know every framework or have the perfect architecture.

1. Pick a problem you understand.
2. Describe what you want to build in plain English.
3. Use Lovable to create the first version.
4. Connect your data, test the functionality, and challenge the AI-generated results.
5. Improve it one step at a time.

You might be surprised how far you get.

When I started with Lovable, I just wanted to try a few Data + AI ideas. Those experiments became multiple working POCs, lessons I couldn’t have learned any other way, and a Databricks Hackathon honorable mention.

And I’m still exploring what else I can build.

If you want to try it yourself, you can get started with my Lovable invite link:

Start building with Lovable

You don’t need a big idea. You just need a problem worth solving.

In this new era of software development, one of the most valuable skills may be knowing how to explain what you want to build.

The next great Data + AI application might begin with a single sentence in English.

I can’t wait to see what you build.

I Built 4 Data + AI Apps Using Plain English with Lovable. One Earned Recognition from Databricks. was originally published in Towards Data Engineering on Medium, where people are continuing the conversation by highlighting and responding to this story.

For years, enterprises have invested heavily in data engineering, analytics, machine learning and, more recently, generative AI.

We have built data lakes, lakehouses, dashboards, feature stores, knowledge graphs, RAG applications and AI agents.

Yet one fundamental challenge remains:

How do we consistently convert enterprise data into trusted, explainable and actionable business decisions?

A dashboard tells us what happened. A machine learning model predicts what might happen. An AI agent can reason, interact with tools and recommend what to do.

But an enterprise needs something bigger: an architecture that brings all these capabilities together, governs them, and makes decisions repeatable, measurable and actionable.

This is the idea behind Decision Fabric. I’m excited to introduce my new eBook:

Databricks AI Context... with Akash A Wadhankar | Topmate

Decision Fabric on Databricks

Trusted Decisions at Scale

Building a reusable Decision Fabric with Lakehouse data, knowledge graphs, Mosaic AI agents and Unity Catalog governance.

The objective is simple: demonstrate how to architect and build a reusable decision infrastructure using the Databricks Data Intelligence Platform.

1. Why do enterprises need a Decision Fabric?

Consider these business problems:

• A retailer wants to determine the next best offer for every customer.
• A bank wants to automate personal loan decisions while maintaining explainability and regulatory compliance.
• An insurance company wants to intelligently prioritize claims.
• A financial institution wants to identify suspicious transactions and recommend appropriate actions.
• A business wants to improve customer retention through intelligent interventions.

These appear to be different use cases.

But underneath, they share a common architecture:

1. Acquire data from enterprise systems.
2. Resolve entities and establish trusted identities.
3. Build business context using semantics, relationships and knowledge graphs.
4. Apply rules, machine learning, optimization and AI agents.
5. Govern, evaluate and monitor decisions.
6. Deliver decisions through applications, APIs, workflows and human approval.

Why should every project rebuild these capabilities independently?

Why should every new AI initiative start with another architecture diagram?

The opportunity is to build the underlying decision machinery once and adapt it across industries and business use cases. That is the foundation of Decision Fabric.

2. Introducing the Five-Layer Decision Fabric Architecture

The eBook introduces a five-layer architecture on Databricks, supported by a cross-cutting Governance and Observability foundation. Each layer has a distinct responsibility.

Layer 0 — Governance and Observability

The trust foundation across the entire architecture. Unity Catalog, access controls, lineage, audit, MLflow tracing, evaluation, monitoring and AI Gateway help establish security, accountability and operational trust.

Layer 1 — Data

The foundation for reliable business facts. Delta Lake, Lakeflow, Auto Loader, Structured Streaming and medallion architecture establish trusted, quality-controlled data pipelines.

Layer 2 — Knowledge

Transforming enterprise data into decision-ready context. Entity resolution, business semantics, knowledge graphs, vector search, hybrid retrieval and context engineering help systems understand business entities, relationships and meaning.

Layer 3 — Reasoning and Intelligence

The intelligence behind business decisions. Business rules, policies, machine learning, optimization and Mosaic AI agents work together to evaluate possibilities and recommend decisions.

Layer 4 — Experience

Taking decisions to the people and systems that need them. Databricks Apps, Model Serving, Lakebase, APIs, AI/BI Dashboards and Genie enable decision delivery, interaction and human approval.

An important architectural principle: agents are not treated as an independent data layer. They operate across Knowledge, Reasoning and Experience, depending on their responsibilities.

The outcome is an architecture that connects enterprise data, knowledge, intelligence and business action.

3. From Retail to Banking: One Fabric, Multiple Industries

One of the most important ideas explored in this eBook is reusability. The book follows two business scenarios.

UrbanCart — Retail Next Best Offer

An online retailer wants to recommend the next best product offer to its customers. The system needs to understand customer identities, purchase history, product relationships, inventory, margins, eligibility policies and customer preferences.

It must recommend an offer and provide evidence explaining why that offer was selected.

Harbor Bank — Personal Lending Decisions

A regional bank wants to accelerate personal loan decisions while maintaining explainability, governance and auditability. The system needs applicant identity, financial context, exposure, affordability, credit policies, models and decision evidence.

Different industries. Different business rules. Different decision models. But much of the underlying architecture remains reusable.

The eBook introduces a three-level reuse model:

Level 1 — Reusable Foundation: Common ingestion, entity resolution, graph frameworks, retrieval, agent frameworks, decision records and governance.

Level 2 — Industry Templates: Retail ontology, banking ontology, industry policies, models and decision agents.

Level 3 — Customer Solutions: Customer-specific data, thresholds, workflows, integrations and experiences.

The principle is: Build once. Template by industry. Configure for the customer.

This is how we move from delivering individual projects to building reusable enterprise capabilities.

Databricks AI Context... with Akash A Wadhankar | Topmate

4. What will you learn from this eBook?

This is designed as a practitioner’s guide rather than a conceptual overview. Across 13 chapters, the eBook takes you through the complete architecture and implementation journey.

Some of the key areas include:

• Designing the Decision Fabric reference architecture.
• Building a medallion architecture designed for business decisions.
• Implementing entity resolution and knowledge graphs.
• Building semantic and hybrid retrieval.
• Combining deterministic rules, machine learning and AI reasoning.
• Designing agentic decision architectures using Mosaic AI.
• Building decision experiences and human approval workflows.
• Implementing governance, agent security and access controls.
• Evaluating and monitoring decision quality.
• Establishing reusable components, CI/CD and operating models.
• Applying the same foundation to a second industry: banking lending.

The Databricks technology mapping covers Delta Lake, Lakeflow, Unity Catalog, Vector Search, Lakebase, MLflow, Mosaic AI, Agent Framework, AgentBricks, Databricks Apps and other platform capabilities.

5. This is not just an architecture book

The eBook is structured to help practitioners move from architecture to implementation.

Inside, you will find:

• 13 detailed chapters.
• 15 architecture diagrams.
• Runnable SQL, Python and YAML setup code.
• 13 hands-on exercises.
• 60 practice questions with answers.
• Retail and banking implementation scenarios.
• Reference architecture and component reuse patterns.

The chapters combine architecture discussions, implementation examples, practical exercises and lessons learned through a running enterprise consulting scenario.

The intention is to help readers understand not only what to build, but also how to structure, implement, govern and reuse it.

6. Who should read this?

This eBook is particularly relevant to:

• Solution Architects designing enterprise data and AI platforms.
• Senior Data Engineers expanding into AI engineering.
• Machine Learning and GenAI Engineers building enterprise agents.
• Enterprise Architects defining reusable technology blueprints.
• Databricks practitioners exploring advanced platform capabilities.
• Technology leaders building data and AI practices.
• Pre-sales and delivery leaders designing scalable enterprise offerings.

If you are already working with Databricks and want to understand how its data, knowledge, AI and governance capabilities can come together into a complete decision architecture, this guide is for you.

7. The bigger vision: Trusted Decision Infrastructure

I believe the next evolution of enterprise data platforms will be about much more than analytics and AI adoption.

It will be about building infrastructure that enables enterprises to make better decisions continuously.

We are moving from:

• Data platforms to knowledge platforms.
• Dashboards to decision experiences.
• Standalone ML models to integrated decision intelligence.
• Individual AI agents to governed agentic architectures.
• Project-specific implementations to reusable enterprise capabilities.

The Decision Fabric brings these ideas together.

Data provides the facts. Knowledge provides the context. Intelligence evaluates the possibilities. Governance establishes trust. Experience turns decisions into action.

This is the architecture I believe enterprises need as they move towards AI-native operations.

Get the eBook: Decision Fabric on Databricks

I’m making this eBook available for architects, engineers and technology professionals who want to explore the next evolution of enterprise data and AI architecture.

Explore the complete eBook on Topmate:

Databricks AI Context... with Akash A Wadhankar | Topmate

Get access to the complete 13-chapter guide, architecture diagrams, implementation code, exercises and practice questions.

Let’s build the next generation of enterprise decision systems.

I would love to hear how you are approaching decision intelligence, enterprise AI architecture, knowledge graphs and agentic systems in your organization.

Let’s move beyond building systems that merely process data.

Let’s build systems that enable enterprises to make trusted decisions at scale.

— Akash Anna Wadhankar

#DecisionFabric #Databricks #DataEngineering #EnterpriseAI #DecisionIntelligence #MosaicAI #UnityCatalog #AgenticAI #Lakehouse #AIArchitecture

Introducing Decision Fabric on Databricks: From Data and AI to Trusted Decisions at Scale was originally published in Towards Data Engineering on Medium, where people are continuing the conversation by highlighting and responding to this story.