Women in Big Data

OCTOBER 9, 2024

Big data pipelines operate similarly to traditional ETL (Extract, Transform, Load) pipelines but are designed to handle much larger data volumes. Refer to Unlocking the Power of Big Data Article to understand the use case of these data collected from various sources.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Mlearning.ai

MAY 16, 2023

This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. The events can be published to a message broker such as Apache Kafka or Google Cloud Pub/Sub.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

This article explores the key fundamentals of Data Engineering, highlighting its significance and providing a roadmap for professionals seeking to excel in this vital field. Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

The MLOps Blog

MARCH 15, 2023

Typical examples include: Airbyte Talend Apache Kafka Apache Beam Apache Nifi While getting control over the process is an ideal position an organization wants to be in, the time and effort needed to build such systems are immense and frequently exceeds the license fee of a commercial offering.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

DagsHub

APRIL 7, 2024

Adopted from [link] In this article, we will first briefly explain what ML workflows and pipelines are. By the end of this article, you will be able to identify the key characteristics of each of the selected orchestration tools and pick the one that is best suited for your use case! Programming language: Airflow is very versatile.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Dataconomy

FEBRUARY 23, 2023

This involves working with various tools and technologies, such as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, to move data from its source to its destination. If you want to learn more about data engineers, check out article called: “ Data is the new gold and the industry demands goldsmiths.”

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

DagsHub

OCTOBER 23, 2024

This article will discuss managing unstructured data for AI and ML projects. Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. is similar to the traditional Extract, Transform, Load (ETL) process. How to properly manage unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52