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 Unstructured Data ETL Tools ETL System 52

Knowledge Hut

NOVEMBER 28, 2023

NoSQL If you think that Hadoop doesn't matter as you have moved to the cloud, you must think again. Big resources still manage file data hierarchically using Hadoop's open-source ecosystem. An effective ETL system should also be designed to ingest data from potentially many different sources.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Knowledge Hut

MAY 2, 2024

Compatibility MapReduce is also compatible with all data sources and file formats Hadoop supports. Spark is developed in Scala language and it can run on Hadoop in standalone mode using its own default resource manager as well as in Cluster mode using YARN or Mesos resource manager. Spark is a bit bare at the moment.

Scala 94

Scala Hadoop Datasets Java 94

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. Hive makes it easier for those familiar with SQL and who work with standard RDBMS databases to access and modify data in Hadoop.

Process 52

Process Data Pipeline Data Warehouse AWS 52