Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. What is data engineering?

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Big Data Big Data Data Engineer Data Engineering 91

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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Women in Big Data

OCTOBER 9, 2024

After this, the data is analyzed, business logic is applied, and it is processed for further analytical tasks like visualization or machine learning. Big data pipelines operate similarly to traditional ETL (Extract, Transform, Load) pipelines but are designed to handle much larger data volumes.

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Big Data Big Data Apache Kafka Data Pipeline 59

Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Data Science Blog

JUNE 27, 2023

This architectural concept relies on event streaming as the core element of data delivery. In practical implementation, the Kappa architecture is commonly deployed using Apache Kafka or Kafka-based tools. Applications can directly read from and write to Kafka or an alternative message queue tool.

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Big Data Big Data Apache Kafka Database 130

AWS Machine Learning Blog

OCTOBER 9, 2024

With ML-powered anomaly detection, customers can find outliers in their data without the need for manual analysis, custom development, or ML domain expertise. Using Amazon Glue Data Quality for anomaly detection Data engineers and analysts can use AWS Glue Data Quality to measure and monitor their data.

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AWS ML ML Data Quality 90

ODSC - Open Data Science

OCTOBER 9, 2024

Efficient Incremental Processing with Apache Iceberg and Netflix Maestro Dimensional Data Modeling in the Modern Era Building Big Data Workflows: NiFi, Hive, Trino, & Zeppelin An Introduction to Data Contracts From Data Mess to Data Mesh — Data Management in the Age of Big Data and Gen AI Introduction to Containers for Data Science / Data Engineering (..)

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Apache Kafka AI AI Machine Learning 40