In this episode, we're joined by Terry Dorsey, Senior Data Architect & Evangelist at Denodo, to unpack the conceptual differences between terms like data fabrics, vector databases, and knowledge graphs, and remind you not to forget about the importance of structured data in this new AI-native world! What You'll Learn: The difference between data fabrics, vector databases, and knowledge graphs — and the pros and cons Why organizing and managing data is still the hardest part of any AI project (and how process design plays a critical role) Why structured data and schemas are still crucial in the age of LLMs and embeddings   How knowledge graphs help model context, relationships, and "episodic memory" more completely than other approaches   If you've ever wondered about different data and AI terms, here's a great glossary to check out from Denodo: https://www.denodo.com/en/glossary 🤝 Follow Terry on LinkedIn!   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Send us a text In this episode, we're joined by Sam Debruyn and Dorian Van den Heede who reflect on their talks at SQL Bits 2025 and dive into the technical content they presented. Sam walks through how dbt integrates with Microsoft Fabric, explaining how it improves lakehouse and warehouse workflows by adding modularity, testing, and documentation to SQL development. He also touches on Fusion’s SQL optimization features and how it compares to tools like SQLMesh. Dorian shares his MLOps demo, which simulates beating football bookmakers using historical data,nshowing how to build a full pipeline with Azure ML, from feature engineering to model deployment. They discuss the role of Python modeling in dbt, orchestration with Azure ML, and the practical challenges of implementing MLOps in real-world scenarios. Toward the end, they explore how AI tools like Copilot are changing the way engineers learn and debug code, raising questions about explainability, skill development, and the future of junior roles in tech. It’s rich conversation covering dbt, MLOps, Python, Azure ML, and the evolving role of AI in engineering.

Send us a text Get ready for an insightful conversation on the future of data integration and real-time decision making with Dima Spivak, Director of Product Management at StreamSets. We cover everything from the “why StreamSets” story to its secret sauce, how it plays in regulated industries, and what makes it a powerful player in data fabric, AI, and streaming use cases. If you’re passionate about the future of data pipelines, governance, and AI-driven insights, this one’s for you! ⏱️ Episode Guide: 02:02 | Meet Dima Spivak04:19 | Why StreamSets?06:00 | What is StreamSets?09:48 | On-Demand Expense11:34 | Regulated Industries12:36 | The Secret Sauce14:41 | A Competitive View15:50 | Data Fabric + StreamSets18:25 | StreamSets + AI21:12 | Use Cases That Matter24:02 | The Future of Streaming25:48 | Quality + Testing31:19 | For Fun 🎉🔗 Connect with Dima: LinkedIn: linkedin.com/in/dmitryspivak Website: https://www.ibm.com/blog/announcement/ibm-acquires-streamSets/ Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Come and join Alcine and Shane as they visit with British Columbia educators John Harris and Denise Augustine. We begin in the realm of story as Denise describes being situated “between generations” in her renowned Coast Salish family of carvers, artists, and leaders and John shares his experiences of growing up on the land and watching his father negotiate treaties as the official liaison for their community. Drawing on her legacy as the Superintendent of Indigenous Education for British Columbia, Denise provides powerful historical context for the work of the Truth and Reconciliation Commission in Canada, which created space for residential school survivors to tell their stories and led to 94 distinct “calls to action” in 2015. She pulls this thread into the fabric of educational change, illuminating how BC is leading the way in reconciliation through a Tripartide Education Agreement and the more recent Declaration of the Right of Indigenous Peoples Act (DRIPA), which requires that school districts create Indigenous Education Councils that view First Nations as “governing bodies”, not just “special interest groups.” From this exploration of reconciliation in education, John takes us into his own family’s legacy of the “Sixties Scoop”, in which his father was taken from his grandparent’s home nearly a dozen times, all the way to his family’s recent visit to the Field Museum of Chicago, which holds over 4,000,000 cultural artifacts, many of which were purchased from Indigenous Nations in the Pacific Northwest. John describes the unsettling experience “as if someone went into your house and took everything.” They end their visit discussing the nuances of place-based versus land-based education and the ways that John has woven his upbringing and community cultural wealth into his pedagogy, which is depicted in the integrative case study which concludes Shane’s forthcoming book, Pedagogies of Voice: Street Data and the Path to Student Agency (Corwin, 2025). Speaking to student agency, John reminds us that “When we give youth opportunities to give back to their communities, they really shine.” Join us for this incredible and luminous conversation reinforcing relationality and reciprocity as core values from Indigenous knowledge systems that hold the potential to transform education everywhere.

For Further Learning:

Learn more about John and his family’s artwork and clothing line at www.aylelum.com Learn more about indigenous ways of knowing and being by reading

Indigenous Storywork: Educating the Heart, Mind, Body, and Spirit by Jo-Ann Archibald Land as teacher: understanding Indigenous land-based education - UNESCO Canadian Commission June 21, 2021

See land-based education in action by following Land-based Education K-12 Plains & Woodland Cree Tanya McCallum on Facebook Learn more about the work of the First Nations Education Steering Committee in British Columbia, Canada Read up on the The United Nations Declaration of the Rights of Indigenous People Act

Many practitioners view data mesh and data fabric as mutually exclusive approaches to data strategy. However, these paradigms complement each other. Data mesh focuses on decentralization and autonomy; Data fabric ensures centralized integration and governance. Let’s dive into how blending elements of both can offer flexibility and control to create the right fit for your organization’s data strategy. Published at: https://www.eckerson.com/articles/blending-data-mesh-and-data-fabric-crafting-a-balanced-data-strategy-2118cd34-e463-4468-b150-bdaf9e1c541d

Send us a text Talking data with Dima Spivak, Director of Product Management, StreamSets.  Data integration and real-time decision making.

02:02 Dima Spivak is Here! 04:19 Why StreamSets?06:00 What is StreamSets?09:48 On Demand Expense11:34 Regulated Industries12:36 Secret Sauce14:41 A Competitive View15:50 Data Fabric and StreamSets18:25 StreamSets and AI21:12 Use Cases24:02 The Future of Streaming25:48 Quality and Testing31:19 For FunLinkedin: linkedin.com/in/dmitryspivak Website: https://www.ibm.com/blog/announcement/ibm-acquires-streamSets/ Want to be featured as a guest on Making Data Simple?  Reach out to us at [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary

Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou

Interview

Introduction How did you get involved in the area of data management? Can you describe what Microsoft Fabric is and the story behind it? Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend? Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution?

What are the elements of Fabric that were engineered specifically for the service? What are the most interesting/complicated integration challenges?

How has your prior experience with Ahana and Presto informed your current work at Microsoft? AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine?

What are the challenges in terms of safety and reliability?

What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically? When is Fabric the wrong choice? What do you have planned for the future of data lake analytics?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

Microsoft Fabric Ahana episode DB2 Distributed Spark Presto Azure Data MAD Landscape

Podcast Episode ML Podcast Episode

Tableau dbt Medallion Architecture Microsoft Onelake ORC Parquet Avro Delta Lake Iceberg

Podcast Episode

Hudi

Podcast Episode

Hadoop PowerBI

Podcast Episode

Velox Gluten Apache XTable GraphQL Formula 1 McLaren

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Starburst:

This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by T

Matt Turck has been publishing his ecosystem map since 2012. It was first called the Big Data Landscape. Now it's the Machine Learning, AI & Data (MAD) Landscape.  The 2024 MAD Landscape includes 2,011(!) logos, which Matt attributes first a data infrastructure cycle and now an ML/AI cycle. As Matt writes, "Those two waves are intimately related. A core idea of the MAD Landscape every year has been to show the symbiotic relationship between data infrastructure, analytics/BI,  ML/AI, and applications." Matt and Tristan discuss themes in Matt's post: generative AI's impact on data analytics, the modern AI stack compared to the modern data stack, and Databricks vs. Snowflake (plus Microsoft Fabric). For full show notes and to read 7+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! In this episode #42, titled "Unraveling the Fabric of Data: Microsoft's Ecosystem and Beyond," we're joined once again by the tech maestro and newly minted Microsoft MVP, Sam Debruyn. Sam brings to the table a bevy of updates from his recent accolades to the intricacies of Microsoft's data platforms and the world of SQL.

Biz Buzz: From Reddit's IPO to the performance versus utility debate in database selection, we dissect the big moves shaking up the business side of tech. Read about Reddit's IPO.Microsoft's Fabric Unraveled: Get the lowdown on Microsoft's Fabric, the one-stop AI platform, as Sam Debruyn gives us a deep dive into its capabilities and integration with Azure Databricks and Power BI. Discover more about Fabric and dive into Sam's blog.dbt Developments: Sam talks dbt and the exciting new SQL tool for data pipeline building with upcoming unit testing capabilities.Polaris Project: Delving into Microsoft's internal storage projects, including insights on Polaris and its integration with Synapse SQL. Read the paper here.AI Advances: From the release of Grok-1 and Apple's MM1 AI model to GPT-4's trillion parameters, we discuss the leaps in artificial intelligence.Stability in Motion: After OpenAI's Sora, we look at Stability AI's new venture into motion with Stable Video. Check out Stable Video.Benchmarking Debate: A critical look at performance benchmarks in database selection and the ongoing search for the 'best' database. Contemplate benchmarking perspectives.Versioning Philosophy: Hot takes on semantic versioning and what stability really means in software development. Dive into Semantic Versioning.

Dan and Jay discussed the concept of Data Fabric, an automated and AI-driven approach to managing modern data environments.

Summary

Data has been one of the most substantial drivers of business and economic value for the past few decades. Bob Muglia has had a front-row seat to many of the major shifts driven by technology over his career. In his recent book "Datapreneurs" he reflects on the people and businesses that he has known and worked with and how they relied on data to deliver valuable services and drive meaningful change.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Bob Muglia about his recent book about the idea of "Datapreneurs" and the role of data in the modern economy

Interview

Introduction How did you get involved in the area of data management? Can you describe what your concept of a "Datapreneur" is?

How is this distinct from the common idea of an entreprenur?

What do you see as the key inflection points in data technologies and their impacts on business capabilities over the past ~30 years? In your role as the CEO of Snowflake you had a first-row seat for the rise of the "modern data stack". What do you see as the main positive and negative impacts of that paradigm?

What are the key issues that are yet to be solved in that ecosmnjjystem?

For technologists who are thinking about launching new ventures, what are the key pieces of advice that you would like to share? What do you see as the short/medium/long-term impact of AI on the technical, business, and societal arenas? What are the most interesting, innovative, or unexpected ways that you have seen business leaders use data to drive their vision? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Datapreneurs book? What are your key predictions for the future impact of data on the technical/economic/business landscapes?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

Datapreneurs Book SQL Server Snowflake Z80 Processor Navigational Database System R Redshift Microsoft Fabric Databricks Looker Fivetran

Podcast Episode

Databricks Unity Catalog RelationalAI 6th Normal Form Pinecone Vector DB

Podcast Episode

Perplexity AI

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Rudderstack:

Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstackSupport Data Engineering Podcast

Data fabric is one of those buzzwords that’s used so much and in so many ways that it often elicits an eyeroll—undeservedly so. The phrase is shorthand for a complex and important set of issues that we’re all working to manage. In this article we’ll review what data fabric is and why it’s important. Published at: https://www.eckerson.com/articles/data-fabric-s-use-of-abstraction-and-metadata

Active metadata is not a type of metadata, it’s a way of using metadata to power systems. Active metadata is a critical feature of modern data architectures such as data fabric and data mesh. It makes things work such as data access management, data classification, and data quality management. Published at: https://www.eckerson.com/articles/active-metadata-the-critical-factor-for-mastering-modern-data-management

The need for adaptable data management architecture has never been more pressing. Yet getting there seems to be more confusing than ever. The field is rampant with buzzwords: data lake, data lakehouse, data fabric, data mesh, data hub, data as a network. Making sense of the confusion begins with sorting out the buzzwords. Published at: https://www.eckerson.com/articles/data-architecture-complex-vs-complicated

Companies are investing in new solutions—such as data fabric, data access governance, and data observability—to keep pace with expanding business appetite for data. Pervasive use of metadata to solve data management problems means that metadata is itself a valuable data asset that we must proactively manage. Published at: https://www.eckerson.com/articles/metadata-is-data-so-manage-it-like-data

Nothing has galvanized the data community more in recent months than two new architectural paradigms for managing enterprise data. On one side there is the data fabric: a centralized architecture that runs a variety of analytic services and applications on top of a layer of universal connectivity. On the other side, is a data mesh: a decentralized architecture that empowers domain owners to manage their own data according to enterprise standards and make it available to peers as they desire.

Most data leaders are still trying to ferret out the implications of both approaches for their own data environments. One of those is Srinivasan Sankar, the enterprise data & analytics leader at Hanover Insurance Group. In this wide-ranging, back-and-forth discussion, Sankar and Eckerson explore the suitability of the data mesh for Hanover, how the Data Fabric might support a Data Mesh, whether a Data Mesh obviates the need for a data warehouse, and practical steps Hanover might to take implement a Data Mesh built on top of a Data Fabric.

Key Takeaways: - What is the essence of a data mesh?
- How does it relate to the data fabric? - Does the data mesh require a cultural transformation? - Does the data mesh obviate the need for a data warehouse? - How does data architecture as a service fit with the data mesh? - What is the best way to roll out a data mesh? - What's the role of a data catalog? - What is a suitable roadmap for full implementation?

Nothing has galvanized the data community more in recent months than two new architectural paradigms for managing enterprise data. On one side there is the data fabric: a centralized architecture that runs a variety of analytic services and applications on top of a layer of universal connectivity. On the other side, is a data mesh: a decentralized architecture that empowers domain owners to manage their own data according to enterprise standards and make it available to peers as they desire.

Most data leaders are still trying to ferret out the implications of both approaches for their own data environments. One of those is Srinivasan Sankar, the enterprise data & analytics leader at Hanover Insurance Group. In this wide-ranging, back-and-forth discussion, Sankar and Eckerson explore the suitability of the data mesh for Hanover, how the Data Fabric might support a Data Mesh, whether a Data Mesh obviates the need for a data warehouse, and practical steps Hanover might to take implement a Data Mesh built on top of a Data Fabric.

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Paul Zikopoulos. Paul is the VP of IBM Technology Sales – Skills Vitality & Enablement Global Markets. Paul is an award winning speaker and author and has been at IBM for 28 years.

Show Notes 3:40 – Is Skills Vitality not the prefect job? 5:08 – What’s been your journey at IBM? 8:29 – Hybrid Cloud Operation and Artificial Intelligence is this the right strategy? 21:13 – What is the new maturity curve? 23:53 – Define Data Fabric 26:53 – Why is the Challenger Seller book so important? 29:14 – What makes a great leader? Books Energy Bus Grit Challenger Sale Challenger Customer Effortless Experience  Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary Gartner analysts are tasked with identifying promising companies each year that are making an impact in their respective categories. For businesses that are working in the data management and analytics space they recognized the efforts of Timbr.ai, Soda Data, Nexla, and Tada. In this episode the founders and leaders of each of these organizations share their perspective on the current state of the market, and the challenges facing businesses and data professionals today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Have you ever had to develop ad-hoc solutions for security, privacy, and compliance requirements? Are you spending too much of your engineering resources on creating database views, configuring database permissions, and manually granting and revoking access to sensitive data? Satori has built the first DataSecOps Platform that streamlines data access and security. Satori’s DataSecOps automates data access controls, permissions, and masking for all major data platforms such as Snowflake, Redshift and SQL Server and even delegates data access management to business users, helping you move your organization from default data access to need-to-know access. Go to dataengineeringpodcast.com/satori today and get a $5K credit for your next Satori subscription. Your host is Tobias Macey and today I’m interviewing Saket Saurabh, Maarten Masschelein, Akshay Deshpande, and Dan Weitzner about the challenges facing data practitioners today and the solutions that are being brought to market for addressing them, as well as the work they are doing that got them recognized as "cool vendors" by Gartner.

Interview

Introduction How did you get involved in the area of data management? Can you each describe what you view as the biggest challenge facing data professionals? Who are you building your solutions for and what are the most common data management problems are you all solving? What are different components of Data Management and why is it so complex? What will simplify this process, if any? The report covers a lot of new data management terminology – data governance, data observability, data fabric, data mesh, DataOps, MLOps, AIOps – what does this all mean and why is it important for data engineers? How has the data management space changed in recent times? Describe the current data management landscape and any key developments. From your perspective, what are the biggest challenges in the data management space today? What modern data management features are lacking in existing databases? Gartner imagines a future where data and analytics leaders need to be prepared to rely on data manage

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Trent Gray-Donald Distinguished Engineer, IBM Data and AI, Dakshi Agrawal IBM Fellow and CTO, IBM AI.  Trent Gray-Donald spend his first 16 years on manage language runtime, then moved over to Data and AI, and then Cloud Pak for Data.  Dakshi Agrawal joined IBM right after his Phd in IBM Research, then Dakshi moved into software development, and in the 6 years in AI.  Show Notes .15 - 5:23 - Repeat of introductions from Part 1 5:50 – What is AI Anywhere? 9:09 – Does it make our development more difficult? 11:22 – Does data virtualization work? 15:31 - How do we get started with AI? 17:41 – Customer success stories Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.