Scalable Data Processing in Data Lakes
( 24 Modules )

Module #1
Introduction to Data Lakes
Overview of data lakes, their benefits, and their role in big data processing

Module #2
Data Lake Architecture
Components of a data lake, including storage, processing, and security

Module #3
Scalable Data Processing Fundamentals
Key concepts and principles of scalable data processing, including distributed computing and parallel processing

Module #4
Hadoop and Spark Overview
Introduction to Hadoop and Spark, including their ecosystem and use cases

Module #5
Data Ingestion in Data Lakes
Methods and tools for ingesting data into a data lake, including NiFi, Kinesis, and Flume

Module #6
Data Storage in Data Lakes
Storage options for data lakes, including HDFS, S3, and object stores

Module #7
Data Processing with Apache Spark
Introduction to Apache Spark, including its architecture, RDDs, and DataFrames

Module #8
Spark SQL and DataFrames
Working with structured data in Spark using DataFrames and Spark SQL

Module #9
Spark Streaming and Real-Time Processing
Introduction to Spark Streaming, including its architecture and use cases

Module #10
Data Processing with Apache Flink
Introduction to Apache Flink, including its architecture and use cases

Module #11
Flink DataStream API
Working with unbounded data streams in Flink using the DataStream API

Module #12
Data Processing with Apache Beam
Introduction to Apache Beam, including its architecture and use cases

Module #13
Beam Pipeline Development
Building data pipelines with Apache Beam, including pipeline development and execution

Module #14
Data Lake Security and Governance
Best practices for securing and governing data lakes, including access control and auditing

Module #15
Data Quality and Data Cleansing
Techniques for ensuring data quality and performing data cleansing in data lakes

Module #16
Data Lake Analytics and Visualization
Tools and techniques for analyzing and visualizing data in data lakes, including Hive, Presto, and Tableau

Module #17
Machine Learning on Data Lakes
Introduction to machine learning on data lakes, including model training and deployment

Module #18
Orchestrating Data Processing with Apache Airflow
Using Apache Airflow to orchestrate data processing workflows in data lakes

Module #19
Cloud-Based Data Lakes
Deploying data lakes on cloud platforms, including AWS, GCP, and Azure

Module #20
Kubernetes and Containerization
Using Kubernetes and containerization to deploy and manage data lake components

Module #21
Monitoring and Troubleshooting Data Lakes
Best practices for monitoring and troubleshooting data lakes, including logging and metrics

Module #22
Data Lake Migration and Integration
Strategies for migrating and integrating data lakes with existing data systems

Module #23
Data Lake Cost Optimization
Techniques for optimizing costs in data lakes, including storage and compute optimization

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Scalable Data Processing in Data Lakes career

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Scalable Data Processing in Data Lakes ( 24 Modules )

Scalable Data Processing in Data Lakes
( 24 Modules )