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Webinar Summary: Data Mesh and Data Products

DataKitchen

Webinar Summary: DataOps and Data Mesh Chris Bergh, CEO of DataKitchen, delivered a webinar on two themes – Data Products and Data Mesh. Finally, Bergh explained that analysts use the data, and they may have a self-service layer where they can access the data they need to analyze. Watch the webinar today!

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Our product vision for analytics in the age of AI

ThoughtSpot

As a result, end users expect faster response times and easier access to meaningful insights. Not only does ThoughtSpot not store your sample data or metadata, or use this information for model training, but we are also investing in bring-your-own and host-your-own model capabilities for both generative AI and machine learning models.

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Data Engineering Weekly #162

Data Engineering Weekly

Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects. Pradheep Arjunan - Shared insights on AZ's journey from on-prem to the cloud data warehouses.

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Real-time AI: Live Recommendations Using Confluent and Rockset

Rockset

AI-powered applications almost always need access to real-time data to deliver accurate results in a responsive user experience that the market has come to expect. Metadata filtering is a useful, perhaps even essential, companion to vector search that restricts nearest-neighbor matches based on specific criteria.

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Introducing Cloudera DataFlow Designer: Self-service, No-Code Dataflow Design

Cloudera

This observation further emphasizes the need for universal developer accessibility , which makes sure that developer tooling is easy to use for newcomers while giving power users the advanced options they need. A critical aspect of universal developer accessibility is to provide dataflow development as a self-service offering to developers.

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From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

They simply read the underlying data (not even full read, they just read the parquet headers) and create corresponding Iceberg metadata files. Query engines (Impala, Hive, Spark) might mitigate some of these problems by using Iceberg’s metadata files. Hive creates Iceberg’s metadata files for the same exact table.

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How JetBlue Used Data Observability To Help Improve Internal “Data NPS” By 16 Points Year Over Year

Monte Carlo

This case study is based on information shared in recent Snowflake webinars and Summit presentations. When Monte Carlo identifies an incident, it shows you enough metadata so that you’re able to understand what’s being impacted down the line, including how many people are potentially being impacted by the issuer,” Brian said.

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