Knowledge Hut

NOVEMBER 17, 2023

You must be able to create ETL pipelines using tools like Azure Data Factory and write custom code to extract and transform data if you want to succeed as an Azure Data Engineer. Master data integration techniques, ETL processes, and data pipeline orchestration using tools like Azure Data Factory.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

ProjectPro

SEPTEMBER 26, 2021

Basic knowledge of ML technologies and algorithms will enable you to collaborate with the engineering teams and the Data Scientists. It will also assist you in building more effective data pipelines. It then loads the transformed data in the database or other BI platforms for use. Hadoop, for instance, is open-source software.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Edureka

FEBRUARY 9, 2023

In this blog on “Azure data engineer skills”, you will discover the secrets to success in Azure data engineering with expert tips, tricks, and best practices Furthermore, a solid understanding of big data technologies such as Hadoop, Spark, and SQL Server is required.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

Knowledge Hut

SEPTEMBER 26, 2023

Programming languages like Python, Java, or Scala require a solid understanding of data engineers. Data is transferred into a central hub, such as a data warehouse, using ETL (extract, transform, and load) processes. Learn about well-known ETL tools such as Xplenty, Stitch, Alooma, etc.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

ProjectPro

JULY 27, 2021

Top 100+ Data Engineer Interview Questions and Answers The following sections consist of the top 100+ data engineer interview questions divided based on big data fundamentals, big data tools/technologies, and big data cloud computing platforms. Briefly define COSHH.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

DECEMBER 7, 2021

AWS Glue You can easily extract and load your data for analytics using the fully managed extract, transform, and load (ETL) service AWS Glue. To organize your data pipelines and workflows, build data lakes or data warehouses, and enable output streams, AWS Glue uses other big data tools and AWS services.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

JANUARY 19, 2022

ETL (extract, transform, and load) techniques move data from databases and other systems into a single hub, such as a data warehouse. Get familiar with popular ETL tools like Xplenty, Stitch, Alooma, etc. Different methods are used to store different types of data.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52