Tobias Macey and Sam Kleinman explores the critical role of databases in software engineering. Sam Kleinman discusses the trade-offs in database selection, emphasising alignment with specific needs to avoid over-engineering. He addresses skepticism about database longevity, advocating for leveraging database capabilities rather than shifting logic to the application layer.

Sanjeev Mohan takes us into the world of AI agents, explaining their concept, types, and development lifecycle. He discusses how AI agents differ from chatbots and assistants, focusing on their ability to automate complex tasks and reason through problems. He emphasises the need for human oversight and evaluation to ensure ethical and reliable AI agent deployment.

Faraaz emphasises on the pivotal role of data modeling in data engineering. He compares data modeling to building a blueprint, explaining its types—conceptual, logical, and physical—and their relevance in database design. He highlights benefits like improved data quality, performance optimization, and scalability while addressing the pitfalls of poor modeling.

In this session, Abhinav shares insights on cloud migration, startup vs. enterprise data engineering, gaps in analytics stacks, unified data platforms, and a Data Engineer’s view on data products.

In this episode, Jess shares how she doubled her salary, launched an SQL course, and champions women in STEM, breaking barriers in data.

In her latest bit-sized podcast episode, Nicola Askham answers this preeminent question asked by one of her clients. She says, these assessments depend on the speed of your organisation but if one chooses to do it, 6 monthly or yearly is the ideal time frame.

Adi Polak highlights three key trends in data engineering involving Apache Kafka, Flink, and Iceberg. These technologies are shaping how engineers manage data and applications. Flink is replacing traditional microservices for data processing with lower latency and built-in fault tolerance.

Benjamin Rogojan shares seven critical lessons learned from his 9+ years experience as a data engineer. He emphasised on the importance of reflection and growth. Strong communication, ownership, and fundamentals are key to success, while curiosity and digging deeper foster adaptability and innovation.

Benn Stancil talks about the hype and "vibes" surrounding AI while exploring the evolving significance of data quality in the context of AI. He critiques corporate narratives that frame every major shift as advantageous for their products, applying this lens to AI's growing influence.

Vu Trinh discusses how LinkedIn developed and evolved its data catalog system, eventually creating DataHub, which was open-sourced in 2019. He detailed LinkedIn’s progression from a monolithic architecture to a more flexible, event-driven system in DataHub’s third generation. He highlights DataHub’s real-time metadata management, stream-based API, and federated metadata services.

Hans Christian Ekne outlines a framework for building a Customer Base Management (CBM) system, emphasizing its role in improving customer retention, pricing strategies, and decision-making through data-driven insights. He explains the foundational elements—ELT (Extract-Load-Transform), churn modeling, and dashboards—and how they empower businesses to analyze and act on customer behavior effectively.

The article compares the semantic layer in data architecture to the MVC framework, highlighting how it abstracts complex data structures to enhance accessibility and consistency.

Jens Linden, PhD discusses the fundamental reasons organizations fail to leverage data and AI effectively. He argues that the root cause lies in the absence of a coherent business strategy, which leads to unclear data responsibilities, ineffective initiatives, and poor data quality. He advocates for using the "Playing to Win" framework to create a strategic foundation.

Mark de Jong and Dylan Anderson discuss the critical need for standardizing KPIs to improve organizational performance and data effectiveness. They introduced a four-step process—assess the current state, define a new framework, plan for change, and implement it — emphasizing governance, quality, & transparency.

Felix Huthmacher explores how to enhance ETL pipelines by integrating AWS Glue with Generative AI for automated data quality correction. He outlines common data issues like duplicates and inconsistent formats.

In this session, Frances delves into the critical role of clean, reliable data in streaming strategies, highlighting essential questions businesses must address to ensure their data empowers rather than hinders their objectives.

Matthew Tod reflects on his evolving perspective on data democratization. Initially passionate about providing widespread access to data. Through his work optimizing A&E patient flow, he highlights the transformative potential of AI-driven tools that not only analyze data but offer actionable, strategic recommendations.

In this session, Dominik addresses some critical questions about using legacy data systems, the benefits of using data products, especially to the analysts, and how to consume these data products effectively with the right strategies.

This session reflects on the transition of GenAI in orgs and discusses the best practices to drive optimal business value from AI and data initiatives, with Bill focusing on being value-driven instead of mere data-driveness.

In this video, Brent talks about Effective Data Storytelling, The evolution of data Evolving perspective of finance analytics, Value AI vs Humans, and more!

The article shares the potential of incorporating GreenAI practices into data product approaches for improved sustainability with reduced carbon footprints while leveraging sustainability metrics like emissions and energy usage for responsible innovation.

Host Richie Cotton speaks with Zuzanna Stamirowska and Hélène Stanway about the significance of real-time analytics across industries. They discuss various use cases, from Formula 1 and logistics to insurance and supply chain management, emphasizing how real-time data helps organizations make faster, operational decisions.

Michael Toland chats with Chandan Gadodia, where they delve into the critical role of data product managers. They discuss how data product managers differ from IT and functional product managers, emphasizing their impact on product success. The episode highlights the evolving responsibilities in data management and the unique contributions of data product managers.

Hugo Lu discusses the persistent challenges faced by Data Teams in organizations, where they are often seen as cost centers and struggle to foster a data-driven culture. He emphasizes that robust data quality and stakeholder alignment are crucial for trust and decision-making.