Anurag Kale
AWS Data Hero, Cloud and Data Architect at Aurobay Sweden
Göteborg, Sweden
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I am a Cloud and Data Architect at Aurobay in Sweden and have been recognized as an AWS Data Hero. With over 10 years of experience in the IT industry, I have dedicated the last 7+ years to working extensively with Public cloud technologies. My passion lies in solving business challenges through innovative cloud solutions. In my approach to architecture, I prioritize understanding the business domain, which naturally led me to embrace Domain Driven Design principles. I am actively involved in the AWS community, having presented at notable events such as AWS Reinvent 2023, AWS Community Day Stockholm 2020 and AWS Summit Stockholm 2022/2023. Additionally, I lead the AWS User Group in Gothenburg.
I am committed to sharing my knowledge and experiences through mentoring, social media engagement, and speaking at technical conferences.
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Beyond Rows and Tables: Rewiring Your Database Brain for NoSQL Success
This talk addresses a critical skills gap facing many engineers: the challenging mental shift required when transitioning from relational databases to NoSQL systems. Drawing from extensive experience both consulting and architecting at scale, I've observed a consistent pattern of friction when SQL-trained developers encounter NoSQL paradigms—a hurdle that applies across most NoSQL databases, though we'll use DynamoDB as our primary reference case.
The presentation unpacks the fundamental mindset transformation needed to excel in NoSQL environments. Attendees will discover why traditional relational thinking becomes a liability when working with document, key-value, or column-family databases, and how to develop new mental models that leverage NoSQL strengths rather than fighting against their design principles.
We'll explore three interconnected areas:
First, we'll examine core operational principles of NoSQL databases—where their approaches to partitioning, consistency, and scaling differ fundamentally from relational systems, using DynamoDB to illustrate these concepts. Next, we'll investigate how these differences demand an entirely new approach to data modeling, challenging assumptions about normalization, relationships, and schema design. Finally, we'll present practical cognitive frameworks that help engineers bridge this gap, with specific techniques to reorient thinking for NoSQL success.
The session introduces Domain-Driven Design as a powerful tool for identifying access patterns—the essential building blocks of effective NoSQL implementations. We'll connect these concepts to microservice architecture as an enabler for cloud-native database strategies. The presentation culminates in a live demonstration, transforming a traditional relational schema into an optimized DynamoDB single-table design, illustrating principles that can be adapted to various NoSQL platforms.
Attendees will leave equipped with actionable mental models that make NoSQL capabilities intuitive rather than counterintuitive, enabling them to build more scalable, performant, and cost-effective database solutions regardless of their specific NoSQL technology choice.
Building Data Lake Platform fully serverless
Data platforms are notorious for needing infrastructure that needs to run 24x7x365. We took on a challenge to implement a self service Data Lake platform using only Serverless cloud services.
This session will present a story of the endeavour from business requirements, design to implementation in an enterprise setup.
I share my experience of building this data lake platform at Polestar cars.
The talk has 4 major parts
1. The business context - I will begin with how we uncovered the business use case and created the first blue prints of the solution.
2. Why we chose a data lake over other data paradigms? - I will explain why we choose a data lake to implement this platform. I will break down the data lake concept into it "logical" components and highlight services from AWS that we considered.
3. Architectural walkthrough of our solution - A technical walkthrough of the solution. I will explain why we choose to make it into a self service platform and the operational model. I will highlight how we made this entire solution self service oriented.
4. Tradeoffs and lessons learnt- I will talk about the friction we faced for adoption, challenges around data security and governance, training management to change their way of thinking to adopt this.
Key questions answered from the session as takeaways for the participants -
1. When to platform data lakes aka centralise vs decentralise?
2. How to ensure the data lakes remains usable?
3. How to enhance developer experience via self service mechanisms?
4. When should you use a data lake over other data products like data warehouse?
Building a Serverless Data Lake Platform
This session shares lessons from building a Data Lake Platform using Serverless components in an enterprise setup. We will dig deeper into data lakes from 1st principles, socio technical challenges around adoption and various tradeoffs that were made during the design phase.
I will talk about my experience of building a data lake platform at Polestar cars.
The talk has 4 major parts
1. The business context - I will begin with the use case and cover the technological needs from the project.
2. Why we chose a data lake over other data paradigms? - Here I will explain why we choose a data lake to implement this platform. I will break down the data lake concept into it "logical" components and highlight services from AWS.
3. Architectural walkthrough of our solution
I will explain why we choose to make it into a platform and how the operational model changes with centralisation of this platform over decentralised data lakes.
4. Lessons learnt from the project.
I will talk about the friction we faced for adoption, challenges around data security and governance, training management to change their way of thinking to adopt this.
Key questions answered from the session as takeaways for the participants -
1. When to platform data lakes aka centralise vs decentralise?
2. How to ensure the data lakes remains usable?
3. How to enhance developer experience via self service mechanisms?
4. When should you use a data lake over other data products like data warehouse?
Visualising Data Mesh:A mental model to understand and design effective decentralised data ecosystem
Data Mesh is a revolutionary decentralised approach to data analytics architecture made popular about 5 years ago. However it is notorious for being really difficult to implement and get results out of for the data leaders.
In my option it is due to reasons such as
- Data Mesh is influenced heavily by the DevOps philosophy and most Data people have worked only in centralised teams.
- There is a big gap in the practices followed in Data Engineering and Software engineering space
In this session, I will present Data Mesh in a novel and visual way using DevOps principles as building blocks. The visuals and anecdotes are explained in the language data people can relate to and understand. I will be introducing the 4 pillars of Data Mesh (Domain Ownership, Data as a product, Self Service Data Platform and Federated Governance) and discuss the critical architectural prerequisites essential for the successful implementation of Data Mesh within organisations. These prerequisites encompass technological considerations (Domain Driven Design, IAM, Data Catalogues, Platforms, Taxonomy, Ontology etc. ), organisational alignment (Team structures, leadership structure), and cultural shifts (distributed ownership) necessary to realise the full potential of Data Mesh.
This talk is inspired from my personal journey of using Data mesh as guiding principles for organisations Data Strategy at my current and previous work places. I will also share some intermediary states organisations can take to slowly and gracefully adopt Data Mesh into their data landscape.
Building Data Lake Platform fully serverless
Data platforms are notorious for needing infrastructure that needs to run 24x7x365. Here I share a story of a data platform from requirements, design to implementation using only serverless services in an enterprise setup. This is a real stroppy from my work at Polestar cars.
The talk has 4 major parts
1. The business context
2. Why we chose a data lake over other data paradigms?
3. Architectural walkthrough of our solution - A technical walkthrough of the solution. It highlights how we made this entire solution self service oriented.
4. Tradeoffs and lessons learnt- I will talk about the friction we faced for adoption, challenges around data security and governance etc
Some takeaways for the participants -
1. When to platform a data lakes aka centralise vs decentralise?
2. tips to ensure the data lakes remains usable
3. our take to enhance developer experience via self service mechanisms
4. When should you use a data lake over other data products like data warehouse
Visualising Data Mesh: A Paradigm Shift in Data Management
Data Mesh is a transformative approach to data management that decentralizes ownership and access, treats data as a product. It advocates for the establishment of small, self-serve data domains, each owned and managed by cross-functional teams.
In this presentation, we will delve into the four fundamental pillars of Data Mesh, discussing how these principles revolutionize traditional data management paradigms. We will also explore how the integration of DevOps principles plays a pivotal role in preparing the data management landscape for the AI/ML revolution.
We will discuss the critical architectural prerequisites essential for the successful implementation of Data Mesh within organizations. These prerequisites encompass technological considerations, organizational alignment, and cultural shifts necessary to realize the full potential of Data Mesh.
This talk is influenced by our unsuccessful attempt to make Data Mesh the basis of our Data Strategy at Polestar. Join us as we share our journey and insights, aiming to inspire and guide others on their path to effective and efficient data management.
Building a mindset to embrace NoSQL Databases
In my experience of working as a consultant and then as an Architect in product company, I find a common pattern when it comes to database adoption. A lot of people coming to product companies with relational database experience, find it very difficult to adapt to the way DynamoDB works.
This talk distills down my experiences into actionable insights of up-skilling myself and a lot of colleagues with relational database background to successfully adopt DynamoDB.
This talk focuses on three main topics -
1. The way DynamoDB operates differently
2. The effects of this differences in data modelling
3. Some ways one can adapt their thinking to reap the best benefits from DynamoDB.
I will cover a little theory of DynamoDB, introduce Domain Driven Design as a tool to find access patterns for your use case, touch on the topics of microservices as the method of successful adoption of cloud and DynamoDB and finally then walk though an example where I take a Relational database schema and convert it into Single Table Design for DynamoDB
State of the Public Cloud - featuring AWS, GCP and Azure
In this two hour long session, the three largest cloud providers - AWS, GCP, and Azure - will present overviews of their respective cloud offerings. The focus of the session will be on providing attendees with a general understanding of the features and capabilities of each platform, as well as the potential benefits of using a cloud provider for their business or organization. The session will cover a range of topics, including compute, storage, containerized workloads, messaging, and database options. Attendees will have the opportunity to learn about the different cloud options available and to determine which one best fits their application needs.
Andreas Wänqvist, Tech Lead at Voyado
Will MC and guide us through the session.
Karl-Henrik Nilsson, Cloud Solution Architect at Microsoft
Will give you an overview of Azure's services.
Abdelfettah SGHIOUAR, Senior Cloud Developer Advocate at Google
Will show you how to run your apps safely and securely on the Google Cloud.
Anurag Kale, AWS Data Hero and Cloud Software Architect at Polestar
Will provide an overview of AWS's services.
Decomposing Data Architectures for Cloud
Data is leverage. Fast evolution of cloud is demanding data solutions that can keep up with its pace. In this talk, I will cover the modern data architectures that leverage the cloud and keeps up with the pace. I will cover various data paradigms like Data Lakes, Data LakeHouse, Data Mesh etc from 1st principles and walk through some sample implementations using AWS Serverless Services.
Come To Code 2024 Sessionize Event
AWS Community Day Italy 2024 Sessionize Event
Data Saturday Oslo 2024 Sessionize Event
AWS Community Day ITA Sessionize Event
AWS Community Day DACH 2023 Sessionize Event
Data Saturday Oslo 2023 Sessionize Event
Stockholm TECH Show 2023 Sessionize Event
AWS Community Day DACH 2022 Sessionize Event
Anurag Kale
AWS Data Hero, Cloud and Data Architect at Aurobay Sweden
Göteborg, Sweden
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