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Introducing Amazon MWAA support for the Airflow REST API and web server auto scaling

AWS Big Data

First, the Airflow REST API support enables programmatic interaction with Airflow resources like connections, Directed Acyclic Graphs (DAGs), DAGRuns, and Task instances. Furthermore, the user’s permissions for interacting with the REST API are determined by the Airflow role assigned to them within Amazon MWAA. small instance class.

Testing 86
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Introducing Cloudera DataFlow Designer: Self-service, No-Code Dataflow Design

Cloudera

They value NiFi’s visual, no-code, drag-and-drop UI, the 450+ out-of-the-box processors and connectors, as well as the ability to interactively explore data by starting individual processors in the flow and immediately seeing the impact as data streams through the flow. . Interactivity when needed while saving costs.

Testing 96
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The Cycle of Change

TDAN

One very influential factor that can potentially undermine your data and document strategies is the natural and emotional reactions of people when things change. Interactions between hardware and software are cautiously investigated, operating systems and network connections are carefully tested, […].

Testing 52
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Cloudera DataFlow Designer: The Key to Agile Data Pipeline Development

Cloudera

Allows them to iteratively develop processing logic and test with as little overhead as possible. With the general availability of DataFlow Designer, developers can now implement their data pipelines by building, testing, deploying, and monitoring data flows in one unified user interface that meets all their requirements.

Testing 81
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Top 5 Data Catalog Benefits: Understanding Your Organization’s Data Lineage

erwin

With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects. At each stage, the discovered pieces of lineage are documented.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

It involves: Reviewing data in detail Comparing and contrasting the data to its own metadata Running statistical models Data quality reports. These processes could include reports, campaigns, or financial documentation. Accuracy should be measured through source documentation (i.e., 2 – Data profiling. date, month, and year).

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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Some of the best lessons are captured in Ron Kohavi, Diane Tang, and Ya Xu’s book: Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing.

Marketing 362