ProjectPro

SEPTEMBER 30, 2014

Hadoop’s significance in data warehousing is progressing rapidly as a transitory platform for extract, transform, and load (ETL) processing. Mention about ETL and eyes glaze over Hadoop as a logical platform for data preparation and transformation as it allows them to manage huge volume, variety, and velocity of data flawlessly.

Hadoop 52

Hadoop ETL Tools Unstructured Data ETL System 52

Knowledge Hut

NOVEMBER 28, 2023

Traditional data transformation tools are still relevant today, while next-generation Kafka, cloud-based tools, and SQL are on the rise for 2023. NoSQL If you think that Hadoop doesn't matter as you have moved to the cloud, you must think again. Knowledge of requirements and knowledge of machine learning libraries.

Data Engineer 52

Data Engineer Data Engineering Engineering Generalist 52

ProjectPro

NOVEMBER 30, 2021

Incremental Extraction Each time a data extraction process runs (such as an ETL pipeline), only new data and data that has changed from the last time are collected—for example, collecting data through an API. Your data will be immediately accessible and available for the ETL data pipeline once this process is over.

Process 52

Process Data Pipeline Data Warehouse AWS 52