AWS Cloud Data Engineering Tech Stack
pavan
This course includes:
- 36 mins on-demand video
- 3 articles
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
What you'll learn
- AWS Cloud Data Engineering Tech Stack
- AWS Cloud Data Engineering
- Data Engineering Tools
- ETL, Analytical, Querying, and Streaming
- Complete code Data Engineering tools in AWS Cloud Infrastructure
- No programming experience needed
pavan
Course Description :
This course is useful for,
ETL Developers
Data Engineers
ETL Architects
Data Migration Specialists
Database Administrators
Database Developers
Data integration is the process of preparing and combining data for analytics, machine learning, and application development. It involves multiple tasks, such as discovering and extracting data from various sources; enriching, cleaning, normalizing, and combining data; and loading and organizing data in databases, data warehouses, and data lakes. These tasks are often handled by different types of users who each uses different products.
We will be working with the data platforms such as,
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 querying to run Data Analytics on a massive volume of data and feed data to different Business Intelligence Tools, Dashboards, and other applications.
Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.
ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.
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