Remove Apache Kafka Remove Clustering Remove Data Warehouse Remove ETL
article thumbnail

The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

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. Finally, the transformed data is loaded into the target system.

article thumbnail

Comparing Tools For Data Processing Pipelines

The MLOps Blog

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. Credits can be purchased for 14 cents per minute.

article thumbnail

How data engineers tame Big Data?

Dataconomy

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.