2021, Apache Kafka and Data Engineering

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2021

Apache Kafka

Data Engineering

Discover the Most Important Fundamentals of Data Engineering

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering

Data Engineering Data Engineering Data Engineer Data Engineering

Transitioning off Amazon Lookout for Metrics

AWS Machine Learning Blog

OCTOBER 9, 2024

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. To learn more, see the documentation.

AWS

AWS ML ML Data Quality

ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)

The MLOps Blog

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g., 1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g.,

ML ML Machine Learning Machine Learning

Webinars

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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Discover the Most Important Fundamentals of Data Engineering

Transitioning off Amazon Lookout for Metrics

ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)

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