As part of this course, you will learn all the Data Engineering Essentials related to building Data Pipelines using SQL, Python as Hadoop, Hive, or Spark SQL as well as PySpark Data Frame APIs. You will also understand the development and deployment lifecycle of Python applications using Docker as well as PySpark on multinode clusters. You will also gain basic knowledge about reviewing Spark Jobs using Spark UI. About Data EngineeringData Engineering is nothing but processing the data depending on our downstream needs. We need to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETLDevelopment, Data Warehouse Development, etc. Here are some of the challenges the learners have to face to learn key Data Engineering Skills such as Python, SQL, PySpark, etc. Having an appropriate environment with Apache Hadoop, Apache Spark, Apache Hive, etc working together. Good quality content with proper support. Enough tasks and exercises for practiceThis course is designed to address these key challenges for professionals at all levels to acquire the required Data Engineering Skills (Python, SQL, and Apache Spark).To make sure you spend time learning rather than struggling with technical challenges, here is what we have done. Training using an interactive environment. You will get 2 weeks of lab access, to begin with. If you like the environment and acknowledge it by providing ratings and feedback, the lab access will be extended to additional 6 weeks (2 months). Feel free to send an email to support@itversity.com to get complementary lab access. Also, if your employer provides a multi-node environment, we will help you set up the material for the practice as part of the live session. On top of Q & A Support, we also provide required support via live sessions. Make sure we have a system with the right configuration and quickly set up a lab using Docker with all the required Python, SQL, Pyspark as well as Spark SQLmaterial. It will address a lot of pain points related to networking, database integration, etc. Feel free to reach out to us via Udemy Q & A, in case you struck at the time of setting up the environment. You will start with foundational skills such as Python as well as SQL using a Jupyter-based environment. Most of the lecturers have quite a few tasks and also at the end of each and every module, there are enough exercises or practice tests to evaluate the skills taught. Once you are comfortable with programming using Python and SQL, then you will ensure you understand how to quickly set up and access Single Node Hadoop and Spark Cluster. The content is streamlined in such a way that, you use learner-friendly interfaces such as Jupyter Lab to practice them. If you end up signing up for the course do not forget to rate us 5* if you like the content. If not, feel free to reach out to us and we will address your concerns. Highlights of this courseHere are some of the highlights of this Data Engineering course using technologies such as Python, SQL, Hadoop, Spark, etc. The course is designed by 20+ years of experienced veteran (Durga Gadiraju) with most of his experience around data. He has more than a decade of Data Engineering as well as Big Data experience with several certifications. He has a history of training hundreds of thousands of IT professionals in Data Engineering as well as Big Data. Simplified setup of all the key tools to learn Data Engineering or Big Data such as Hadoop, Spark, Hive, etc. Dedicated support where 100% of questions are answered in the past few months. Tons of material with real-world experiences and Data Sets. The material is made available both under the Git repository as well as in the lab which you are going to set up. Complementary Lab Access for 2 Weeks which can be extended to 8 Weeks.30 Day Money back guarantee. Content DetailsAs part of this course, you will be learning Data Engineering Essentials such as SQL, and Programming using Python and Apache Spark. Here is the detailed agenda for the course. Data Engineering Labs - Python and SQLYou will start with setting up self-support Data Engineering Labs on Cloud9 or on your Mac or PC so that you can learn the key skills related to Data Engineering with a lot of practice leveraging tasks and exercises provided by us. As you pass the sections related to SQLand Python, you will also be guided to set up Hadoop and Spark Lab. Provision AWS Cloud9 Instance (in case your Mac or PC does not have enough capacity)Setup Docker Compose to start the containers to learn Python and SQL (using Postgresql)Access the material via Jupyter Lab environment setup using Docker and learn via hands-on practice. Once the environment is set up, the material will be directly accessible. Database Essentials - SQLusing PostgresIt is important for one to be proficient with SQLto take care of building data engineering pipelines. SQLis used for understanding the data
