Remove Accessibility Remove Building Remove Metadata
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Interesting startup idea: benchmarking cloud platform pricing

The Pragmatic Engineer

A €150K ($165K) grant, three people, and 10 months to build it. The startup was able to start operations thanks to getting access to an EU grant called NGI Search grant. Results are stored in git and their database, together with benchmarking metadata. In this article, we cover: Funding and team size. Tech stack.

Cloud 278
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Octopai Acquisition Enhances Metadata Management to Trust Data Across Entire Data Estate

Cloudera

Cloudera, together with Octopai, will make it easier for organizations to better understand, access, and leverage all their data in their entire data estate – including data outside of Cloudera – to power the most robust data, analytics and AI applications.

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Metadata Management And Integration At LinkedIn With DataHub

Data Engineering Podcast

The key to those solutions is a robust and flexible metadata management system. LinkedIn has gone through several iterations on the most maintainable and scalable approach to metadata, leading them to their current work on DataHub. What were you using at LinkedIn for metadata management prior to the introduction of DataHub?

Metadata 100
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Build Data Products Without A Data Team Using AgileData

Data Engineering Podcast

Summary Building data products is an undertaking that has historically required substantial investments of time and talent. Shane Gibson co-founded AgileData to make analytics accessible to companies of all sizes. Atlan is the metadata hub for your data ecosystem. Can you describe what AgileData is and the story behind it?

Building 130
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Apache Kafka Data Access Semantics: Consumers and Membership

Confluent

Although it is the simplest way to subscribe to and access events from Kafka, behind the scenes, Kafka consumers handle tricky distributed systems challenges like data consistency, failover and load balancing. Every developer who uses Apache Kafka ® has used a Kafka consumer at least once. Data processing requirements.

Kafka 111
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Why Column-Aware Metadata Is Key to Automating Data Transformations

Snowflake

IoT devices in every industry; geolocation information on our phones, watches, cars, and every other mobile device; every website or app we access—all are collecting data. A single organization may have access to millions of attributes. For the future, our automation tools must collect and manage metadata at the column level.

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Building A Data Governance Bridge Between Cloud And Datacenters For The Enterprise At Privacera

Data Engineering Podcast

In this episode Balaji Ganesan shares how his experiences building and maintaining Ranger in previous roles helped him understand the needs of organizations and engineers as they define and evolve their data governance policies and practices. Acryl]([link] The modern data stack needs a reimagined metadata management platform.