Description
This course is useful for, ETL DevelopersData EngineersETL ArchitectsData Migration SpecialistsDatabase AdministratorsDatabase 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 that each use different products. We will be working with the data platforms such as, Data storesAmazon S3Amazon Relational Database Service (Amazon RDS)Third-party JDBC-accessible databasesData streamsAmazon Kinesis Data StreamsFirehoseGlue data catalogClassifiersCrawlersAWS 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.
