Data Engineering
Master data engineering from pipelines to storage and build robust, scalable systems for big data processing.Preview Data Engineering course
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Build end-to-end data pipelines using Python and SQL
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Use tools like Apache Airflow for workflow orchestration
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Process data in real-time with Kafka and batch with Spark
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Design scalable storage systems with cloud-based services
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Implement ETL and data validation best practices
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Developers looking to specialize in data engineering
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Data analysts transitioning into backend data roles
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Computer science students exploring big data systems
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Professionals working in cloud, devops, or analytics
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Tech enthusiasts building data-driven applications
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Follow modules in order to build foundational understanding
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Practice hands-on using cloud and open-source tools
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Experiment with real-world datasets from Kaggle or open APIs
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Keep notes on architectures, tools, and error-handling
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Participate in community Q&A and apply what you learn in mini-projects
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Review architecture diagrams and deployment strategies regularly
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
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Understand the role and responsibilities of a Data Engineer
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Build batch and streaming data pipelines
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Implement data modeling and data warehousing techniques
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Use Airflow for orchestration and scheduling
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Handle data quality, security, and governance
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Deploy pipelines using Docker and cloud services like AWS/GCP
Course Syllabus
Module 1: Introduction to Data Engineering
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What is Data Engineering
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Roles vs. Data Scientists vs. Analysts
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Tools & Ecosystem Overview
Module 2: Databases & SQL for Engineers
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Relational and NoSQL databases
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Writing advanced SQL queries
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Indexing, optimization, and joins
Module 3: Data Modeling & Warehousing
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Dimensional modeling
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Star & snowflake schemas
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Data marts and OLAP
Module 4: Batch Data Pipelines
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Python and Pandas
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Writing ETL scripts
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Scheduling with cron and Airflow
Module 5: Stream Processing with Kafka
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Kafka architecture and setup
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Streaming vs. batch
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Data ingestion pipelines
Module 6: Big Data Processing with Spark
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RDDs and DataFrames
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PySpark and distributed computing
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Spark SQL
Module 7: Data Lakes and Storage
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S3, HDFS, Delta Lake
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File formats: CSV, JSON, Parquet, Avro
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Partitioning and data lifecycle
Module 8: Cloud Data Engineering
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AWS Redshift, GCP BigQuery, Azure Synapse
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Serverless data workflows
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Infrastructure as Code (IaC)
Module 9: Data Quality & Validation
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Data testing tools (Great Expectations)
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Logging and alerting for failures
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Schema enforcement
Module 10: Capstone Project
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Build and deploy a full ETL pipeline
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Present data architecture
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Document workflows and configurations
Completing this course prepares you for high-demand roles such as:
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Data Engineer
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Big Data Developer
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Cloud Data Engineer
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ETL Developer
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Data Platform Specialist
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What is the difference between batch processing and stream processing?
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How do you optimize a SQL query for performance?
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What is the role of Apache Airflow in data engineering?
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Explain partitioning in data lakes and why it’s useful.
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What are the key differences between Redshift and BigQuery?
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How does Kafka ensure message durability and ordering?
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What are common file formats used in big data, and when to use each?
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Describe the difference between a star schema and a snowflake schema.
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How do you handle data quality issues in ETL pipelines?
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What are the benefits of using Spark over traditional ETL tools?