SQL

Datacast Episode 122: The Evolution of Data Visualization, Scaling Data Culture, and The Future of Data Transformation with Gabi Steele

Datacast Episode 122: The Evolution of Data Visualization, Scaling Data Culture, and The Future of Data Transformation with Gabi Steele

Gabi Steele is the co-founder and co-CEO of Preql, a no-code data transformation tool that empowers business users to model and manage their own metrics. She is also the co-founder of Data Culture, a data engineering and visualization consultancy. Previously Gabi led data visualization engineering at WeWork and worked on data storytelling at the Washington Post.

Datacast Episode 113: Data Applications, Real-Time Analytics, and Cloud Product Management with Shruti Bhat

Datacast Episode 113: Data Applications, Real-Time Analytics, and Cloud Product Management with Shruti Bhat

Shruti Bhat leads product management and marketing at Rockset. Before Rockset, she led Product Management for Oracle Cloud, focusing on AI, IoT, and Blockchain. Previously, she was the VP of Marketing at Ravello Systems, where she drove the start-up's rapid growth from pre-launch to hundreds of customers and a successful acquisition. Before that, she was responsible for launching VMware's vSAN and led engineering teams at HP and IBM. Shruti has a bachelor's in computer science engineering and an MBA from UCLA Anderson.

Datacast Episode 112: Distributed Systems Research, The Philosophy of Computational Complexity, and Modern Streaming Database with Arjun Narayan

Datacast Episode 112: Distributed Systems Research, The Philosophy of Computational Complexity, and Modern Streaming Database with Arjun Narayan

Arjun Narayan is the co-founder and CEO of Materialize. Materialize is a streaming database for real-time applications and analytics, built on top of a next-generation stream processor – Timely Dataflow. He was previously an engineer at Cockroach Labs and held a Ph.D. in Computer Science from the University of Pennsylvania.

Datacast Episode 91: Collaborative Data Workspace, The Sharing Gap, and Engineering Management with Caitlin Colgrove

Datacast Episode 91: Collaborative Data Workspace, The Sharing Gap, and Engineering Management with Caitlin Colgrove

Caitlin Colgrove is the Co-Founder and CTO of Hex Technologies, a collaborative data workspace for building and sharing data projects using SQL and Python. Caitlin has spent her career as a software engineer building data analytics tools, first at Palantir and then later at startups including Remix and Hex. As a CTO, her focus has expanded from purely technology to growing and developing diverse and inclusive engineering teams.

Fugue - Reducing Spark Developer Friction

Fugue - Reducing Spark Developer Friction

This is a guest article written by Han Wang and Kevin Kho, in collaboration with James Le. Han is a Staff Machine Learning Engineer at Lyft, where he serves as a Tech Lead of the ML Platform. He is also the founder of the Fugue Project. Kevin is an Open Source Engineer at Prefect, a workflow orchestration framework, and a contributor to Fugue. Opinions presented are their own and not the views of their employers.

An Introduction to Big Data: Data Querying

An Introduction to Big Data: Data Querying

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.

An Introduction to Big Data: Relational Database

An Introduction to Big Data: Relational Database

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.