AWS Data Engineering Course Online

Get hands-on experience in AWS platform and Data Engineering concepts with this AWS Data Engineering Course online. As this course curriculum is designed by industry experts, you will acquire job-ready skills through which you can develop your career. As part of this AWS Data Engineering course, you will be dedicated to learning Data Engineering Services such as RD, Lambda, Athena, SQS, etc.

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AWS Data Engineering Online Course – Overview

If you want to make your career in AWS Data Engineering Domain, get through this online course where you will learn to implement the concepts of data engineering on the AWS platform. Adding to this, you will learn to build Data Engineering pipelines with the help of Lambda, Athena, Glue, EMR, etc.

AWS Data Engineering Online Course – Key Features

  • Trusted content.
  • Re-learn for free anytime in a year.
  • Rigorous assignments and assessments.
  • Learn at your own pace.
  • Mandatory feedback sessions.
  • Mock-interviews.
  • Hands-on real-time experience.
  • Free mentorship.
  • Live chat for instant solutions.
  • Job ready employees post-training.
  • End-to-end training.
  • Download the certificate after the course.

AWS Data Engineering Online Course – Benefits

The big data and data engineering global market growth is estimated to reach USD 77.37 billion with a CAGR of 17.6% by 2023.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
AWS Data Engineer

UK
Hiring Companies

AWS Data Engineering Online Program – Training Options

Self-Paced Learning

£ 1200.00

  • 1-year access to the Blockchain course content
  • 1 capstone project
  • Multiple assessments
  • Continuous feedback sessions
  • Access to the class recordings
  • Assistance and support
  • Download certification
  • Free mentorship

Online Boot Camp

£ 1000.00

  • Everything in Self-paced learning +
  • On-spot doubt clarification
  • Interactive training sessions
  • Sessions on the capstone project
  • Live, online classroom training
  • Mock-interviews

Corporate Training

Customized to your team's needs

  • 1-year access to the Blockchain course content
  • 1 capstone project
  • Multiple assessments
  • Continuous feedback sessions
  • Class recordings
  • Assistance and support
  • Certification after the course

AWS Data Engineering Training Program Online – Curriculum

Eligibility

Individuals or freshers who have done AWS foundational course and want to expand their skills in AWS Data Engineering can join this course. Furthermore, this AWS Data Engineering online training course is well-suited for data scientists, data analysts, python developers, and software developers.

Pre-requisites

In order to experience a smooth learning journey in this AWS Data Engineering training course online, you need to have prior programming experience using python, an understanding of Spark concepts, writing and interpreting SQL queries, etc.

Course Content

  • design an Azure Data Lake solution
  • recommend file types for storage
  • recommend file types for analytical queries
  • design for efficient querying
  • design for data pruning
  • design a folder structure that represents the levels of data transformation ∙ design a distribution strategy
  • design a data archiving solution
  • design a partitioning strategy for analytical workloads
  • design a partitioning strategy for efficiency/performance
  • design a partitioning strategy for Azure Synapse Analytics
  • identify when partitioning is needed in Azure Data Lake Storage
  • design a dimensional hierarchy
  • design a solution for temporal data
  • design for incremental loading
  • design analytical stores
  • design meta stores in Azure Synapse Analytics and Azure Databricks
  • design meta stores in Azure Synapse Analytics and Azure Databricks
  • implement compression
  • implement partitioning
  • implement sharding
  • implement different table geometries with Azure Synapse Analytics pools ∙ implement data redundancy
  • implement distributions
  • implement data archiving
  • build a temporal data solution
  • build a slowly changing dimension
  • build a logical folder structure
  • build external tables
  • implement file and folder structures for efficient querying and data
  • deliver data in a relational star schema
  • deliver data in Parquet files
  • maintain metadata
  • implement a dimensional hierarchy
  • transform data by using Apache Spark
  • transform data by using Transact-SQL
  • transform data by using Data Factory
  • transform data by using Azure Synapse Pipelines
  • transform data by using Stream Analytics
  • cleanse data
  • split data
  • shred JSON
  • encode and decode data
  • configure error handling for the transformation
  • normalize and denormalize values
  • transform data by using Scala
  • perform data exploratory analysis
  • develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks
  • create data pipelines
  • design and implement incremental data loads
  • design and develop slowly changing dimensions
  • handle security and compliance requirements
  • scale resources
  • configure the batch size
  • design and create tests for data pipelines
  • integrate Jupyter/IPython notebooks into a data pipeline
  • handle duplicate data
  • handle missing data
  • handle late-arriving data
  • upsert data
  • regress to a previous state
  • design and configure exception handling
  • configure batch retention
  • design a batch processing solution
  • debug Spark jobs by using the Spark UI
  • create data pipelines
  • design and implement incremental data loads
  • design and develop slowly changing dimensions
  • handle security and compliance requirements
  • scale resources
  • configure the batch size
  • design and create tests for data pipelines
  • integrate Jupyter/IPython notebooks into a data pipeline
  • handle duplicate data
  • handle missing data
  • handle late-arriving data
  • upsert data
  • regress to a previous state
  • design and configure exception handling
  • configure batch retention
  • design a batch processing solution
  • debug Spark jobs by using the Spark UI
  • develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs
  • process data by using Spark structured streaming
  • monitor for performance and functional regressions
  • design and create windowed aggregates
  • handle schema drift
  • process time-series data
  • process across partitions
  • the process within one partition
  • configure checkpoints/watermarking during processing
  • scale resources
  • design and create tests for data pipelines
  • optimize pipelines for analytical or transactional purposes
  • handle interruptions
  • design and configure exception handling
  • upsert data
  • replay archived stream data
  • design a stream processing solution
  • trigger batches
  • handle failed batch loads
  • validate batch loads
  • manage data pipelines in Data Factory/Synapse Pipelines
  • schedule data pipelines in Data Factory/Synapse Pipelines
  • schedule data pipelines in Data Factory/Synapse Pipelines
  • implement version control for pipeline artifacts
  • Excerise: Design security for data policies and standards
    • design data encryption for data at rest and in transit
    • design a data auditing strategy
    • design a data masking strategy
    • design for data privacy
    • design a data retention policy
    • design to purge data based on business requirements
    • design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2
    • design row-level and column-level security
  • implement data masking
  • encrypt data at rest and in motion
  • implement row-level and column-level security
  • implement Azure RBAC
  • implement POSIX-like ACLs for Data Lake Storage Gen2
  • implement a data retention policy
  • implement a data auditing strategy
  • manage identities, keys, and secrets across different data platform technologies ∙ implement secure endpoints (private and public)
  • implement resource tokens in Azure Databricks
  • load a DataFrame with sensitive information
  • write encrypted data to tables or Parquet files
  • manage sensitive information
  • implement logging used by Azure Monitor
  • configure monitoring services
  • measure performance of data movement
  • monitor and update statistics about data across a system ∙ monitor data pipeline performance
  • measure query performance
  • monitor cluster performance
  • understand custom logging options
  • schedule and monitor pipeline tests
  • interpret Azure Monitor metrics and logs
  • interpret a Spark directed acyclic graph (DAG)
  • compact small files
  • rewrite user-defined functions (UDFs)
  • handle skew in data
  • handle data spill
  • tune shuffle partitions
  • find shuffling in a pipeline
  • optimize resource management
  • tune queries by using indexers
  • tune queries by using cache
  • optimize pipelines for analytical or transactional purposes ∙ optimize pipeline for descriptive versus analytical workloads ∙ troubleshoot a failed spark job
  • troubleshoot a failed pipeline run
  • Daily Assignments
  • Weekly Online Tests
  • Three Grand Tests
  • Mock Interviews

AWS Data Engineering Online Training Course – FAQs

When you have done AWS foundational course online and gained basic-level AWS certification, then you can take up this AWS Data Engineer Course online with basic knowledge or prior experience in the field of data analytics. Therefore, when you have attained AWS and Data Engineering skills as part of this online course, you can avail of certification by clearing the examination and thus, become AWS Data Engineer through huge job opportunities.

AWS Data Engineer implements the concepts of data engineering in the AWS platform in order to derive better solutions. Furthermore, an AWS Data Engineer manages data transfer, data pipelines, and data storage using various tools and techniques.

Yes, data engineers are high in demand due to the fastest-growing job opportunities in data engineering technology. As per the latest reports, there will be huge growth in the number of open positions and thus, the candidates who are skilled with data engineering courses can experience career growth with wider job opportunities.

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