ETL Training
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ETL
ETL (Extract, Transform, Load) is a critical process for businesses that need to collect data from various sources, transform it into a structured format, and load it into a data warehouse or other data storage system. ETL training can be beneficial for IT professionals involved in data integration and management at different stages of their careers.
Training Objective
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Learn the basics of ETL (Extract, Transform, Load) process, including its tools and techniques.
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Understand the role of data warehousing in ETL, and the various types of data sources.
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Discover the different ETL tools and their features, including Talend, Informatica, and Apache NiFi.
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Understand the importance of data quality and validation in ETL, and the various methods for data cleansing.
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Learn about the concepts of data profiling and metadata management, and their role in ETL.
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Explore the use of ETL in big data processing, cloud-based ETL, and real-time data integration.
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Understand the various data integration techniques such as batch processing, message queuing, and change data capture.
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Learn about the importance of data governance and compliance in ETL, and the various standards and regulations that apply.
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Discover different techniques for monitoring and managing ETL processes, including logging, alerts, and performance tuning.
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Understand the role of ETL in the overall data architecture, including the data warehouse and data marts.
Training Content
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Understanding the ETL process and its components - Extract, Transform, and Load.
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Introduction to ETL tools such as Talend, Informatica, and Apache NiFi.
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Understanding data sources and data integration techniques, including batch processing and change data capture.
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Data quality, validation, and cleansing techniques for ETL.
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Metadata management, including data profiling and data lineage in ETL.
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Techniques for big data processing, cloud-based ETL, and real-time data integration.
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Understanding the different types of data warehousing and their role in ETL.
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Exploring data governance and compliance in ETL.
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Performance tuning and monitoring of ETL processes, including logging and alerts.
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Data architecture and its role in ETL, including data warehouse and data marts.
