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via Udemy |
Go to Course: https://www.udemy.com/course/data-engineering-serverless-elt-bi-on-amazon-cloud/
Certainly! Here's a detailed review and recommendation for the course on Coursera: --- **Course Review and Recommendation: Mastering Data Warehousing and BI Infrastructure on AWS** If you're a data scientist, analyst, or business analyst looking to strengthen your cloud data engineering skills, this Coursera course offers an invaluable, practical introduction to building data warehouses and BI solutions on AWS. Despite AWS's reputation for being vast and complex, this course breaks down that intimidating ecosystem into manageable, hands-on modules designed for learners with foundational knowledge of Python and SQL. **What You Will Learn:** - Setting up a scalable data warehouse in Redshift from scratch. - Core data warehousing concepts to understand the architecture. - Building serverless ETL processes using AWS Glue with PySpark and Python shell. - Performing ad-hoc data analysis with AWS Athena. - Automating data pipelines and syncing using AWS Data Pipeline and Lambda functions. - Visualizing data through Amazon QuickSight, creating analyses and dashboards. **Strengths:** - **End-to-end Lifecycle Focus:** The course is thoughtfully structured around real-world data engineering projects, guiding learners through the entire process from data ingestion and transformation to visualization. - **Hands-on Approach:** Practicing with AWS's free tiers for Redshift and RDS ensures you gain practical experience without worrying about upfront costs. - **No Heavy Coding:** With only around 35% coding involved, this course emphasizes understanding and execution over complex programming, making it accessible to those with basic Python and SQL skills. - **Use of AWS UI:** The course relies on AWS's intuitive browser interface, removing the need for bash scripting or advanced command-line knowledge, and allowing flexibility across operating systems. **Who Should Enroll:** - Anyone with a basic understanding of cloud concepts, SQL, and Python. - Data professionals eager to expand their data warehousing and BI infrastructure skills. - Learners willing to explore and invest effort into mastering AWS tools. **Recommendations:** - **Prior Preparation:** Brush up on SQL, Python, and basic PySpark scripting to maximize learning. - **Active Engagement:** Use the tips provided, such as watching videos at increased speed and researching alternative tools (like Snowflake, BigQuery, Power BI) to deepen understanding. - **Practice Extensively:** Make the most of the free tiers by completing all lab exercises and experimenting beyond the course material. **Final Verdict:** This course is an excellent stepping stone for anyone looking to demystify AWS's data ecosystem and acquire practical skills in data warehousing and BI setup. Its real-world focus, user-friendly approach, and comprehensive coverage make it highly recommended for aspiring data engineers or analysts aiming to be well-rounded in cloud data infrastructure. **Rating:** ★★★★☆ (4 out of 5 stars) Enroll now if you're ready to take your data engineering skills to the cloud—this course will empower you with the knowledge and confidence to handle modern data projects efficiently! ---
AWS Cloud can seem intimidating and overwhelming to a lot of people due to its vast ecosystem, but this course will make it easier for anyone who wants a hands-on expertise in setting up a data-warehouse in Redshift or setup a BI infrastructure from scratch.Data Scientists/Analysts/Business Analysts will soon be expected to (if not already) become all-rounders and handle the technical aspect of data ingestion/engineering/warehousing. Anyone who has the basic understanding of how cloud works can benefit from this course because: - This course is designed keeping in mind end to end life cycle of a typical data engineering project - Provides a practical solution to real-world use-cases This Course covers: Setting up a data warehouse in AWS Redshift from scratch Basic Data Warehousing Concepts Writing server-less AWS Glue Jobs (pyspark and python shell) for ETL and batch processing AWS Athena for ad-hoc analysis (when to use Athena) AWS Data Pipeline to sync incremental data Lambda functions to trigger and automate ETL/Data Syncing processes QuickSight Setup , Analyses and Dashboards Prerequisites for this course are: Python / Sql (Absolute must)PySpark (should know how to write some basic Pyspark scripts)Willingness to explore ,learn and put in the extra effort to succeed An active AWS Account Important Note - This course makes use of the free tiers for Redshift and RDS , so you will not be billed for them unless you exceed the free tier usage which should be more than enough to get enough practice from this course . Also , this course makes use of AWS UI on the browser for creating clusters and setting up jobs , there is no bash scripting involved. One can use any operating system to perform the lab sessions in this course. This course is not code-intense or code-heavy ,there is only 35% coding involved , the rest is execution,understanding and chaining different component together. The whole purpose of this course is to make everyone aware of and feel comfortable with all the tools/features used in this course. Some Tips: Try to watch the videos at 1.2X speed Every time you work on a new component or feature , do some research on the other tools that are meant for the same purpose and see how they differ and in what aspects , For Eg Redshift/Athena vs Snowflake or Bigquery , QuickSight vs PowerBi vs Microstrategy