Remove Data Lake Remove Data Science Remove Data Transformation Remove Metadata
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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

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Cloudera’s Open Data Lakehouse Supercharged with dbt Core(tm)

Cloudera

Using these adapters, Cloudera customers can use dbt to collaborate, test, deploy, and document their data transformation and analytic pipelines on CDP Public Cloud, CDP One, and CDP Private Cloud. The Open Data Lakehouse . This variety can result in a lack of standardization, leading to data duplication and inconsistency.

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Exploring the AI and data capabilities of watsonx

IBM Big Data Hub

By supporting open-source frameworks and tools for code-based, automated and visual data science capabilities — all in a secure, trusted studio environment — we’re already seeing excitement from companies ready to use both foundation models and machine learning to accomplish key tasks.

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Accelerate Your Data Mesh in the Cloud with Cloudera Data Engineering and Modak NabuTM

Cloudera

The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.

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How to modernize data lakes with a data lakehouse architecture

IBM Big Data Hub

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.

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Lay the groundwork now for advanced analytics and AI

CIO Business Intelligence

“You had to be an expert in the programming language that interacts with that data, and understand the relationships of each data element within each data source, let alone understand its relation to elements in other data sources,” he says. Without those templates, it’s hard to add such information after the fact.”