About the Program
Are you passionate about building solutions to solve problems using data? Leadtecno’s Data Engineering Training will provide you with the skills to design data products that make a positive impact and drive change. This training is a technica stream that contains six courses designed for data professionals that want to further their career in a fascinating discipline. As a Data engineer, transform the way organizations think, view, and create business value from data by :
- Applying the fundamentals of data architecture
- Transferring enterprise data to cloud hosted environments
- Mastering computer programming skills that are essential to create data engineering products
- Refining data that can be used to provide decision makers with actionable information
- Preparing data for presentation of business and analytical insights The Data Engineering Training concludes with a project where you will build a minimum viable data engineering product (MVP) that you can use in your resume or on your portfolio.
Azure Cloud Data Engineering
Learn how to design, build, and manage data pipelines on Azure. This course covers big data tools, data workflow automation, and analytics, helping you transform large datasets into actionable insights.
Certified Cloud Practitioner
Gain foundational knowledge of cloud computing, covering core cloud concepts, cost structures, security, and compliance. This course is perfect for beginners looking to start their journey in cloud technology.
Azure Solution Architect
Master the skills to design, implement, and manage secure, scalable Azure solutions. You’ll learn to plan infrastructure, optimize resources, and implement security measures, aligning technical solutions with business needs.
Azure DevOps
This course focuses on continuous integration and delivery within the Azure platform. You’ll learn to automate deployment workflows, manage source control, and create CI/CD pipelines to streamline software development and delivery.
Potential Job Titles
- Data Engineer
- Cloud Data Engineer
- Data Warehouse Developer
- Data Warehouse Engineer
- Analytics Engineer
- Big Data Developer
- ETL Specialist
- ETL Developer
- Software Engineer (Data)
- Data Architect
- Data Integration Developer
- Data Solution Developer
Cloud Data warehouse Fundamentals
- Big data overview and objectives
- Hadoop architecture – History and evolutions
- Data lake – How it works
- Introduction of cloud data warehouse
Azure Data bricks, Spark, Pyspark
- Introduction of apache spark and data bricks cloud
- Azure Data bricks Workspace creation, delta tables, spark SQL, cluster management.
- Spark data frames and tables
- Data frame transformations and actions
- Managed vs. External Tables
- Spark Web UI
- Data frame transformations
- Spark data types
- Spark aggregations
- Spark Joins
- Spark data frame internals
- Spark joins and optimizations
- Advanced spark
- Spark streaming
Snowflake
- Snowflake UI overview
- Snowflake Internal stage
- Snowflake External stages
- Transaction, Commit and Rollback in Snowflake
- Snowflake CDC
- Snowflake ZERO copy cloning
- Snowflake Time travel
- Fail safe property in snowflake
- Different types of tables in snowflake
- Caching in Snowflake Data Warehouse
- Snow pipe
- Snowflake tasks
Azure Data factory
- Data pipelines and Dataflow using ADF
- Incremental and Batch Pipelines
- Connectors: Azure services, databases, Nosql, files, generic protocols, services & apps, custom
- Activities: data movement, data transformation, control flow
- Parameterization
- Pipeline Monitoring Debugging and Performance Optimization
- Integration Runtimes
Data Modelling & Azure Synapse
- Azure data Factory
- Azure Data bricks
- Azure Synapse
- Azure Functions, Logic Apps, Azure Storage, Key Vault
- Snowflake
Cloud Devops and CICD
- Introduction to git
- Introduction to CICD with azure data ops
- Git integration in data factory
- CiCD data best practices
