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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

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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Data transformation takes flight at Atlanta’s Hartsfield-Jackson airport

CIO Business Intelligence

Jon Pruitt, director of IT at Hartsfield-Jackson Atlanta International Airport, and his team crafted a visual business intelligence dashboard for a top executive in its Emergency Response Team to provide key metrics at a glance, including weather status, terminal occupancy, concessions operations, and parking capacity.

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Simplify Metrics on Apache Druid With Rill Data and Cloudera

Cloudera

Co-author: Mike Godwin, Head of Marketing, Rill Data. Cloudera has partnered with Rill Data, an expert in metrics at any scale, as Cloudera’s preferred ISV partner to provide technical expertise and support services for Apache Druid customers. Deploying metrics shouldn’t be so hard. Cloudera Data Warehouse).

Metrics 91
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Deploy and Scale AI Applications With Cloudera AI Inference Service

Cloudera

This service supports a range of optimized AI models, enabling seamless and scalable AI inference. Enterprise developers began exploring proof of concepts (POCs) for generative AI applications, leveraging API services and open models such as Llama 2 and Mistral. By 2023, the focus shifted towards experimentation.

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The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

DataKitchen

Azure Databricks, a big data analytics platform built on Apache Spark, performs the actual data transformations. The cleaned and transformed data can then be stored in Azure Blob Storage or moved to Azure Synapse Analytics for further analysis and reporting. Some tools are excellent for batch processing (e.g.,

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? Building Models. A common task for a data scientist is to build a predictive model. You know the drill: pull some data, carve it up into features, feed it into one of scikit-learn’s various algorithms.

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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.