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 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Precisely

MARCH 9, 2023

Processing speeds were considerably slower than they are today, so large volumes of data called for an approach in which data was staged in advance, often running ETL (extract, transform, load) processes overnight to enable next-day visibility to key performance indicators. It is often used as a foundation for enterprise data lakes.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

Pickl AI

JULY 25, 2023

Skills and Tools of Data Engineers Data Engineering requires a unique set of skills, including: Database Management: SQL, NoSQL, NewSQL, etc. Data Warehousing: Amazon Redshift, Google BigQuery, etc. ETL Tools: Apache NiFi, Talend, etc. Data Modeling: Entity-Relationship (ER) diagrams, data normalization, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

DagsHub

OCTOBER 23, 2024

NoSQL Databases NoSQL databases do not follow the traditional relational database structure, which makes them ideal for storing unstructured data. They allow flexible data models such as document, key-value, and wide-column formats, which are well-suited for large-scale data management.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52