What you would learn in AWS Cloud Data Engineering Tech Stack course?
This course can be helpful,
The process involves gathering and mixing data for analysis, machine learning, or application creation. It involves a range of tasks like locating and removing data from multiple sources; cleaning, enriching, and normalizing data; and loading and managing data into data warehouses, databases, and data lakes. These tasks are typically done by users who use various software.
Our team is working on data platforms; for example,
Data stores
Amazon S3
Amazon Relational Database Service (Amazon RDS)
Third-party JDBC-accessible databases
Data streams
Amazon Kinesis Data Streams
Firehose
Glue data catalog
Classifiers
Crawlers
AWS Data Engineering ensures fast processing of queries to run Data Analytics on a massive amount of data and feed data to various Business Intelligence Tools, Dashboards, and other software.
Data engineers operate in various settings to create systems that gather, manage and transform raw data into helpful information for business analysts and data scientists to analyze. Their primary goal is to make information accessible to businesses so that they can evaluate and improve their performance.
ETL, an acronym for extract transform and load, is a data integration procedure that combines data from several data sources into one reliable data store that is then loaded into a data warehouse or any other system that is a target.
Course Content:
- AWS Cloud Data Engineering
- Data Engineering Tools
- ETL and Querying, as well as Analytical and Streaming
- Complete Code Data Engineering tools within AWS Cloud Infrastructure
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