Remove Data Quality Remove ETL Remove Explainability
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ETL Process Explained: Essential Steps for Effective Data Management

Pickl AI

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. What is ETL? ETL stands for Extract, Transform, Load.

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How Formula 1® uses generative AI to accelerate race-day issue resolution

AWS Machine Learning Blog

To handle the log data efficiently, raw logs were centralized into an Amazon Simple Storage Service (Amazon S3) bucket. An Amazon EventBridge schedule checked this bucket hourly for new files and triggered log transformation extract, transform, and load (ETL) pipelines built using AWS Glue and Apache Spark.

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When Scripts Aren’t Enough: Building Sustainable Enterprise Data Quality

Towards AI

Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models. Author(s): Richie Bachala Originally published on Towards AI.

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Transitioning off Amazon Lookout for Metrics 

AWS Machine Learning Blog

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. You can review the recommendations and augment rules from over 25 included data quality rules.

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The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

There are various architectural design patterns in data engineering that are used to solve different data-related problems. This article discusses five commonly used architectural design patterns in data engineering and their use cases. Finally, the transformed data is loaded into the target system.

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The Role of RTOS in the Future of Big Data Processing

ODSC - Open Data Science

With the advent of big data in the modern world, RTOS is becoming increasingly important. As software expert Tim Mangan explains, a purpose-built real-time OS is more suitable for apps that involve tons of data processing. The Big Data and RTOS connection IoT and embedded devices are among the biggest sources of big data.

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Build Data Pipelines: Comprehensive Step-by-Step Guide

Pickl AI

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.