Data Engineering in the Cloud: Big Data Solutions #901924

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Data Engineering in the Cloud: Big Data Solutions is a comprehensive 5-day course designed to equip participants with the skills and knowledge to design, implement, and manage big data solutions in the cloud. This course will cover the fundamentals of big data, cloud technologies, data engineering tools, and best practices for building scalable and efficient data pipelines.

Upon completion of this course, participants will be able to:
• Understand Big Data Concepts: Grasp the core concepts of big data, including volume, velocity, and variety.
• Choose the Right Cloud Platform: Evaluate the offerings of major cloud providers (AWS, Azure, GCP) for big data workloads.
• Design Data Pipelines: Design and implement efficient data pipelines using tools like Apache Airflow and Luigi.
• Process and Transform Data: Utilize data processing frameworks like Apache Spark and Hadoop to clean, transform, and enrich data.
• Store and Manage Big Data: Implement data storage solutions using cloud storage services (S3, Azure Blob Storage, Google Cloud Storage).
• Secure Big Data Environments: Implement security best practices to protect sensitive data.
• Monitor and Optimize Big Data Pipelines: Monitor performance, identify bottlenecks, and optimize resource utilization.

This course is suitable for:
• Data engineers
• Data analysts
• Data scientists
• Software engineers
• Cloud architects
• Anyone interested in big data and cloud technologies

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

• Big Data Fundamentals:
o What is big data?
o The 5 Vs of big data (Volume, Velocity, Variety, Veracity, Value)
o Big data challenges and opportunities
• Cloud Computing Basics:
o IaaS, PaaS, and SaaS
o Major cloud providers (AWS, Azure, GCP)
o Cloud storage and compute services

• Data Ingestion:
o Data sources (batch and streaming)
o Data ingestion tools (Apache Kafka, Apache Flume)
• Data Processing:
o Apache Spark and its core components (Spark SQL, Spark Streaming, MLlib)
o Data processing pipelines and workflows
• Data Storage:
o Cloud storage services (S3, Azure Blob Storage, Google Cloud Storage)
o Data lakes and data warehouses

• Data Pipelines:
o Designing and implementing data pipelines
o Scheduling and automation
o Error handling and monitoring
• ETL Processes:
o Extract, Transform, Load (ETL) operations
o Data cleaning and transformation
o Data quality assurance

• Data Warehousing:
o Data warehouse architecture and design
o Data modeling and ETL processes
o Data warehousing tools (SQL Server, Oracle, Snowflake)
• Data Lakes:
o Data lake architecture and benefits
o Data lake implementation using cloud storage
o Data access and querying

• Real-Time Data Processing:
o Stream processing with Apache Flink and Kafka Streams
o Real-time analytics and machine learning
• Data Security and Privacy:
o Data encryption and access control
o Data privacy regulations (GDPR, CCPA)
• Cloud Cost Optimization:
o Cost-effective data storage and processing
o Rightsizing resources
• Best Practices for Big Data Projects:
o Agile methodologies for data engineering
o Data governance and quality assurance

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