Remove Accessible Remove Definition Remove Metadata
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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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A Definitive Guide to Using BigQuery Efficiently

Towards Data Science

With on-demand pricing, you will generally have access to up to 2000 concurrent slots, shared among all queries in a single project, which is more than enough in most cases. Choosing the right model depends on your data access patterns and compression capabilities. GB / 1024 = 0.0056 TB * $8.13 = $0.05 in europe-west3.

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Data logs: The latest evolution in Meta’s access tools

Engineering at Meta

Here we explore initial system designs we considered, an overview of the current architecture, and some important principles Meta takes into account in making data accessible and easy to understand. Users have a variety of tools they can use to manage and access their information on Meta platforms. feature on Facebook.

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Ready-to-go sample data pipelines with Dataflow

Netflix Tech

Workflow Definitions Below you can see a typical file structure of a sample workflow package written in SparkSQL. ??? In every sample workflow package there are three workflow definition files that work together to provide flexible functionality. Attributes are set via Metacat , which is a Netflix internal metadata management platform.

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The last (but not least)”ops” you need for your data : DataGovops

François Nguyen

In every step,we do not just read, transform and write data, we are also doing that with the metadata. Every data governance policy about this topic must be read by a code to act in your data platform (access management, masking, etc.) Who has an access to this Data ? Last part, it was added the data security and privacy part.

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The View Below The Waterline Of Apache Iceberg And How It Fits In Your Data Lakehouse

Data Engineering Podcast

Your host is Tobias Macey and today I'm interviewing Ryan Blue about the evolution and applications of the Iceberg table format and how he is making it more accessible at Tabular Interview Introduction How did you get involved in the area of data management?

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Keeping Small Queries Fast – Short query optimizations in Apache Impala

Cloudera

Unlike traditional planners that need to consider accessing a table via a variety of types of index, Impala’s planner always starts with a full table scan and then applies pruning techniques to reduce the data scanned. Metadata Caching. See the performance results below for an example of how metadata caching helps reduce latency.

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