Cloud Data Engineering : AWS, Azure, and GCP for Data Excellence
Join us at LevelUp for an immersive journey into the world of Cloud Data Engineering! Our comprehensive program is designed to equip you with the skills and knowledge needed to harness the full potential of three of the industry's leading cloud platforms: AWS, Azure, and GCP.
Sateesh Pabbathi
Author & Cloud Expert
A Sneek-peek into what's inside!
- Understand core cloud computing concepts.
- Master AWS for data operations.
- Excel in Microsoft Azure for data solutions.
- Leverage Google Cloud Platform (GCP) for data engineering.
- Develop ETL (Extract, Transform, Load) skills.
- Explore cloud-based data warehousing.
- Build scalable data pipelines.
- Implement real-time data processing.
- Optimize data storage in the cloud.
- Apply best practices for data security.
- Use serverless and containerized solutions.
- Harness big data technologies.
- Perform data analytics on cloud platforms.
- Work on hands-on, real-world projects.
- Prepare for industry certification exam.
Essential Information Before You Begin!
What is Cloud Data Engineering Course?
Cloud data engineering refers to the process of designing, building, and managing data pipelines and infrastructure in a cloud computing environment. It involves the use of cloud services and platforms to collect, store, process, and analyze data efficiently and at scale.
What Are The Benefits Of this Course?
Joining the "Cloud Data Engineering at LevelUp" course offers several compelling benefits. Firstly, you'll gain expertise in leading cloud platforms like AWS, Azure, and GCP, opening doors to lucrative career opportunities. Secondly, mastering data engineering techniques enables you to efficiently process and analyze data, driving data-informed decision-making within organizations. Thirdly, hands-on experience through real-world projects ensures practical skill development. Moreover, our program equips you with in-demand skills, making you a valuable asset in the data-driven job market. Lastly, you'll join a community of like-minded learners, fostering networking and collaboration opportunities that extend beyond the course.
Cloud Data Engineering Certification Course
Excel in Cloud Data Engineering Certification for Lucrative Opportunities by Mastering These Vital 12 Core Skills
Cloud Platform Proficiency
Data Integration and
ETL
Cloud Data
Warehousing
Data Pipeline Development
Real-time Data
Processing
Data Security in
the cloud
Serverless
Computing
Big Data
Technologies
Data Analytics in the Cloud
Cloud Data Storage Optimization
Containerization
Skills
Cloud Certification Preparation
Course Cirriculum
- 1.Understanding Data Engineering vs. Data Science
2.Exploring the Cloud Data Stack: Storage, Processing, and Analytics
3.Key Concepts: Data Lakes, Warehouses, and Data Pipelines
4.Cloud Providers Comparison: AWS, Azure, Google Cloud
5.Role of Data Engineers in Modern Organizations
6.Case Studies: Successful Cloud Data Engineering Implementations
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1. Mastering SQL Queries for Data Retrieval and Transformation
2.SELECT Statements and Data Retrieval
3.Filtering and Sorting Data
4.Aggregation and Grouping
5.Joins and Subqueries
6.Window Functions for Advanced Analysis
7.Creating and Modifying Tables
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1. Python Essentials for Data Engineers
2.Data Types, Variables, and Operators
3.Control Structures (Loops, Conditional Statements)
4.Functions and Modules
5.File Handling in Python
6.Exception Handling
7.Data Structures (Lists, Dictionaries, etc.)
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1.Comparing Cloud Storage Options: S3, Azure Blob, Google Cloud Storage
2.Features and Capabilities of Each Storage Solution
3.Pricing Models and Cost Considerations
4.Access Control and Security Measures
5.Data Lifecycle Management in Cloud Storage
6.Data Replication and Availability
7.Integrations with Other Cloud Services
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1. Orchestrating Data Pipelines in the Cloud
2.Introduction to Orchestration Tools (Apache Airflow, Step Functions, etc.)
3.Building Workflow DAGs for Data Pipelines
4.Error Handling and Retry Strategies
5.Schedule and Trigger Data Pipelines
6.Monitoring and Alerting for Orchestration
7.Managing Dependencies between Tasks
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1.Introduction to Apache Spark for Cloud Data Processing
2.Setting Up Apache Spark on Cloud Platforms
3.Data Ingestion and ETL with Apache Spark on Cloud
4.Optimizing Data Pipelines with Apache Spark
5.Spark SQL: Querying and Analyzing Data on the Cloud
6.Real-time Stream Processing with Apache Spark
7.Machine Learning with Apache Spark on Cloud
8.Advanced Techniques for Scalable Data Engineering with Spark
9.Monitoring and Debugging Apache Spark Applications on the Cloud
10.Best Practices for Performance and Cost Optimization in Cloud-based Spark Deployments
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1.Getting Started with Databricks for Cloud Data Engineering
2.Setting Up a Databricks Workspace
3.Collaborative Data Processing in Databricks
4.Version Control and Collaboration Features
5.Databricks Notebooks and Jobs
6.Clusters and Scalability in Databricks
7.Integrations with Cloud Data Storage Solutions
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1.Introduction to Data Quality and Governance in Cloud Environments
2.Data Profiling and Validation Techniques in the Cloud
3.Implementing Data Quality Checks in Cloud-based Pipelines
4.Metadata Management and Data Catalogs in the Cloud
5.Data Lineage and Traceability in Cloud Environments
6.Monitoring and Alerting for Data Quality in the Cloud
7.Data Governance Policies and Compliance in Cloud Data Engineering
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1.Real-time Data Processing with Apache Kafka
2.Introduction to Stream Processing Concepts
3.Event Time vs Processing Time
4.Windowing and Time-based Operations
5.Exactly-once Processing Guarantees
6.Stateful Stream Processing
7.Building Fault-tolerant Stream Processing Pipelines
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1.Security Best Practices for Cloud Data Engineering
2.Encryption and Key Management
3.Access Control and Role-based Permissions
4.Data Masking and Anonymization
5.Regulatory Compliance (GDPR, HIPAA, etc.)
6.Security Auditing and Monitoring
7.Incident Response and Data Breach Handling
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1.Implementing Effective Data Monitoring Strategies
2.Setting up Monitoring Dashboards and Alerts
3.Log Collection and Aggregation
4.Anomaly Detection and Alerting
5.Performance Metrics and KPIs
6.Error and Exception Handling in Logs
7.Integrating with Monitoring Tools
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1.Version Control Strategies for Data Engineering
2.Git and Git-based Versioning Workflows
3.Branching Strategies for Data Pipelines
4.Continuous Integration and Continuous Deployment (CI/CD) Pipelines
5.Automated Testing for Data Pipelines
6.Deployment Strategies and Rollbacks
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1.Implementing Complex Data Engineering Patterns
2.Lambda Architecture for Real-Time and Batch Processing
3.Kappa Architecture for Stream Processing at Scale
4.Materialized Views and Caching Strategies
5.Data Mesh Architecture and Decentralized Data Ownership
6.Polyglot Persistence and Multi-Model Databases
7.Data Mesh and Data Fabric Patterns
Capstone Projects: From Concept to Completion, Dive into Real-World Projects!
Real-Time Analytics Platform
Data Ingestion: Set up data ingestion pipelines to collect streaming data from different sources.Stream Processing: Process incoming data streams in real-time using frameworks like Apache Kafka and Spark Streaming.Data Storage: Choose an appropriate data storage solution (e.g., NoSQL, Data Warehouse) for storing processed streaming data.Dashboard and Visualization: Create a user-friendly dashboard for monitoring and visualizing real-time analytics.
Cloud-Native Data Warehouse
Data Ingestion: Set up data pipelines for ingesting data from various sources into the data warehouse.ETL Processes: Implement ETL processes for cleaning, transforming, and loading data into the warehouse.Data Modeling: Design a data model optimized for analytics and reporting.Querying and Analytics: Write SQL queries and perform analytics on the data in the data warehouse.
Data Lake and Advanced Analytics Platform
Data Ingestion: Set up data pipelines for ingesting different types of data into the data lake (structured, semi-structured, unstructured).Data Lake Architecture: Design a scalable and cost-effective data lake architecture using cloud storage solutions.Data Catalog and Metadata Management: Implement a cataloging system to track and manage metadata for the stored data.
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Frequently asked questions
This course is designed for aspiring and current data engineers, data analysts, and professionals working in data-related roles who want to expand their expertise in cloud-based data engineering. It is also suitable for individuals interested in building scalable and efficient data pipelines in cloud environments.
In this course, you can expect to gain comprehensive knowledge and hands-on experience in designing, building, and managing data pipelines in cloud platforms. You'll learn about various cloud data storage solutions, data orchestration and ETL techniques, as well as best practices for ensuring data quality, security, and compliance in the cloud.
To enroll in this course, a basic understanding of data concepts and familiarity with programming (Python, SQL) is recommended. Additionally, having some exposure to cloud platforms like AWS, Azure, or GCP will be beneficial, though not mandatory.
Yes, upon successful completion of the course, you will receive a certificate of completion. This certificate attests to your proficiency in Cloud Data Engineering and can be a valuable addition to your professional portfolio.
The course is structured into modules that progressively cover different aspects of Cloud Data Engineering. Each module includes a combination of video lectures, hands-on exercises, and quizzes to reinforce learning. You'll work on practical projects, allowing you to apply the concepts learned in real-world scenarios. Additionally, there will be supplementary resources and forums for discussion and additional learning support.