Part 1 - Roadmap to Become a Data Engineer for ETL, PL/SQL, Data Warehouse, Mainframes Developers
As part of these 2-part series of videos, we will cover how to become a Data Engineer if one is an experienced ETL or PL/SQL or Data Warehouse or Mainframes Developer.
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📌 Part 1: 🔗 • Part 1 - Roadmap to Become a Data Eng...
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If you are an experienced Oracle PL/SQL Developer or an Informatica Developer or Talend Developer or Abinitio Developer or Microsoft SSIS/SSRS Developer or Data Stage Developer, then it is inevitable for you to transition to Data Engineer. In these sessions most of your questions related to why and how you need to transition to Data engineering with examples based on our vast experience.
🚨Here is the program link related to Data Engineering using AWS Data Analytics -
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As this will be a very detailed session, we will cover all the below topics in 2 1.5-hour sessions. This is the 2nd session which is the continuation of the previous one.
0:00:00 Agenda of Data Engineering Roadmap
0:05:03 How to rate our @itversity courses on Udemy?
0:06:43 Introduction about @itversity
0:13:45 Recap of conventional Data Warehousing
0:25:45 What is Data Engineering and why ETL, PL/SQL, Data Warehouse, and Mainframes Developers should take it seriously?
Conventional Data Warehousing + Modern Analytics
Data Engineering on Cloud Platforms - AWS, GCP, Azure, Databricks, Snowflake, CDP, etc
Why Data Engineering using AWS Data Analytics?
What are all the different systems Data Engineer deal with?
Variety of source or upstream systems - Purpose Built Databases, Files, REST APIs
0:41:02 Data Lake
Downstream systems such as Data Warehouses or MPP, NoSQL, External Systems
1:07:19 What are the key skills and up to what level ETL, PL/SQL, Data Warehouse, and Mainframes Developers should know the skills?
REST APIs and JSON with Demo
SQL with Demo
Orchestration with example or demo
Python with demo
Key Integrations with demo
Cloud and Serverless with demo
Performance Tuning with demo
1:20:22 Details about our Guided Program on AWS (others in the future). Here are some of the highlights of the program.
Python and SQL
AWS Essentials for Data Engineers for Data Lake, Distributed Compute, Data Warehouse, and other purpose-built services
Mastering AWS Lambda Functions for Data Engineers - to build or enhance data pipelines
Mastering AWS Elastic Map Reduce for Data Engineers - to build data pipelines to process large-scale data using distributed computing
Mastering AWS Redshift as Data Warehouse for Data Engineers - to build Data Marts or Data Warehouse to serve enterprise reports or dashboards
Mastering AWS Athena and Glue Data Catalog - for ad-hoc analysis of Data as well as to build data pipelines for large-scale data
Mastering Amazon Managed Streaming for Apache Kafka (MSK) - to build streaming data pipelines integrating with Spark and other purpose-built AWS Services
Performance Tuning Guide for Data Engineers on AWS - Data Ingestion, Data Lake, Data Processing, Loading Data
Other Details related to the course
Cost and Timelines for the course
Delivery Mode (Hybrid) - Self-Paced with continuous support
Labs and Additional Costs
Refund Policy
Placement Assistance or Support
Alumni Club
#DataEngineering #BigData #AWS #mapreduce #ETL #cloudcomputing #Roadmap #mainframes
#DataWarehouse #dataengineeringessentials
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