In this post, Brain shares a comprehensive guide on how to use AI prompt with DAX to get best results for Power BI developers.

Joy Victor discusses the transferable skills that data analysts already possess which can aid in a transition to data engineering. She highlights overlapping technical skills like SQL, data wrangling, programming, & data visualization.

Sanjeev Mohan discusses the rise of distributed SQL databases, highlighting Amazon's Aurora DSQL announcement as a validation of this category. He emphasizes that distributed SQL has been crucial for demanding workloads for years.

Andrew Madson MSc, MBA discusses the evolution of data pipeline architectures, from where it started and how it transitioned to the modern ELT. Andrew also attempts to explore the rise of streaming pipelines for real-time data needs, "zero ETL" integrations aiming to eliminate explicit pipelines, and no-copy data sharing.

In this episode, Brian Julius and Sam McKay explore real-world uses of Microsoft Certified Professional (MCP) tools and discuss how AI is reshaping data analytics and business decision-making.

Nicola Askham shares valuable insights on the frequency of Data Governance maturity assessments. She explains that these assessments are key to tracking progress, identifying gaps, and guiding future improvements.

Benjamin Rogojan explains the concept of columnar storage and its advantages for data analytics, particularly in contrast to row-based storage. He details how storing data by column improves query performance, enables better compression, and facilitates vectorized processing and parallelism.

An authentic conversation between two seasoned data leaders who got to industry leading data roles and are now focused on empowering the next generation of data leaders.

Dipankar Mazumdar, M.Sc introduces Multi-Catalog Sync in Apache XTable™ (Incubating). This new feature extends interoperability in data lakehouses beyond open table formats to the catalog layer. He highlights the challenges of fragmented catalog usage and vendor lock-in.

Vipin Jain discusses the journey from descriptive analytics to predictive and prescriptive analytics, emphasizing their significance in unlocking the full potential of AI.

Tobias Macey interviews Sida Shen about StarRocks, a high-performance analytical database that originated as a fork of Apache Doris. Sida explains that StarRocks is designed as a Lakehouse query engine.

Robert Sahlin shares key principles for building reliable data platforms that empower teams and reduce firefighting. He discusses how thinking like a product owner, simplifying operations through software, enabling self-service with built-in safety, can ensure broad usability for engineers and analysts.

Emma Crabtree speaks with Zoe Kelly on The Lets Talk Data Podcast, where Zoe unpacks how leadership in data isn’t just about technical skills but about fostering a human-centered environment. This episode offers a powerful lesson in prioritizing people to unlock data-driven excellence.

Rachael Deacon-Smith shares a concise guide to capturing accurate ingestion time in BigQuery. She explains key methods like application-level timestamps and ingestion-time partitioning that helps data teams avoid late or missing data.

Jessica Talisman joins Joe Reis and discusses Knowledge Graphs, Libraries, and sort of jobs available in tech. They also talk about the value of authentic self-presentation and knowledge sharing.

Demetrios Brinkmann hosts Weidong Yang and Paco Nathan on the MLOps Community podcast and introduce GraphBI to unlock the 80% of enterprise data that’s usually left out of traditional BI tools.

Kasriel Kay challenges the status quo of data governance, urging a shift from rigid control to dynamic, human-centered systems. He advocates for real data democratization, emphasizing data literacy, privacy, and clear responsibilities.

Awadelrahman Ahmed shows how time is often an overlooked but critical axis in data system design. He unpacks the subtle ways time shapes architecture like schema evolution and point-in-time queries.

Niels Claeys shares how to make Kubernetes more efficient for batch data workloads by tuning node setup, scaling strategies, and spot instance reliability. He covers practical adjustments like minimizing resource overhead and choosing the right node sizes, to improving job scheduling and handling spot interruptions.

Yordan Ivanov shares how to prove value early with Minimum Viable Data Products that are lightweight, focused solutions built around one user and one problem. Skip perfect pipelines. Start with simple outputs, real feedback, and fast iterations to build trust and momentum.

Kirill Bobrov calls out the buzzword-driven marketing that overwhelms data engineering. With new "revolutionary" tools like Data Fabric, Medallion Architecture, and Zero ETL being pitched as game-changers, the author cuts through the noise.

In this episode, Christina & Jeff discuss how businesses can prepare for the rise of Agentic AI systems. They explore strategies for adapting the workforce, the role of data readiness, and practical steps to ensure companies gain value from advanced AI adoption.

Phuong Nguyen offers a sobering look into the disconnect between expectations and reality in data analyst roles. From interviews that promise strategic impact to day-to-day work reduced to reporting.

Ananth Packkildurai explores the evolution of data infrastructure, focusing on Lakehouse architectures and vendor lock-in. He advocates for a federated catalog design, proposing a "Catalog Replicator" to ensure metadata synchronization, portability, and system interoperability.