Enroll Course: https://www.coursera.org/learn/etl-and-data-pipelines-shell-airflow-kafka
In the ever-expanding universe of data, the ability to efficiently process and prepare information for analysis is paramount. The Coursera course, “ETL and Data Pipelines with Shell, Airflow and Kafka,” offers a comprehensive journey into two fundamental approaches: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).
This course expertly navigates the distinctions between ETL, typically used for data warehouses and data marts, and ELT, which is favored for data lakes where transformation happens on demand. It highlights the increasing demand for raw data access, driving the evolution from ETL to ELT. You’ll gain insights into various data extraction techniques, including database querying, web scraping, and APIs, and understand the importance of data transformation for application compatibility. The course also covers the nuances of batch loading versus continuous streaming.
A significant portion of the course is dedicated to the practicalities of building data pipelines. You’ll learn how to leverage Bash scripts and cron for scheduling ETL processes, understanding the critical aspects of scheduling, monitoring, maintenance, and optimization. The distinction between batch and streaming pipelines is clearly explained, with a focus on when to use streaming for real-time data needs. You’ll also explore how parallelization and I/O buffers can help overcome performance bottlenecks and learn to quantify pipeline performance using latency and throughput metrics.
Apache Airflow is introduced as a powerful tool for managing data pipelines. The course emphasizes Airflow’s code-as-DAGs (Directed Acyclic Graphs) approach, which enhances maintainability, testability, and collaboration. You’ll explore Airflow’s intuitive UI for visualizing DAGs in graph or tree modes, understand the key components of DAG definition files, and learn how Airflow manages logs, sending them to various cloud storage and analysis tools.
For those interested in real-time data processing, Apache Kafka is covered in depth. Recognized as a leading open-source event streaming platform, Kafka is crucial for handling observable state updates over time. The course details its core components like brokers, topics, partitions, replications, producers, and consumers. You’ll also delve into the Kafka Streams API, understanding its client library for stream processing and exploring the roles of source and sink processors.
The course culminates in a robust final assignment, offering hands-on labs to solidify your learning. You’ll have the opportunity to build ETL data pipelines using Apache Airflow and create streaming data pipelines with Kafka in real-world scenarios. This includes extracting, transforming, and loading data into a CSV file, creating a Kafka topic, customizing a streaming data consumer, and verifying data collection.
Overall, “ETL and Data Pipelines with Shell, Airflow and Kafka” is an exceptional course for anyone looking to build robust, scalable, and efficient data processing systems. It strikes an excellent balance between theoretical concepts and practical application, making it a highly recommended resource for data engineers, analysts, and anyone involved in data management.
Enroll Course: https://www.coursera.org/learn/etl-and-data-pipelines-shell-airflow-kafka