AWS Certified Data Engineer

|| AWS Certified Data Engineer - Associate Course in Dahod

The AWS Certified Data Engineer – Associate Training Course at BIT Dahod is a specialized program crafted for aspiring data professionals who aim to master the skills required to design, build, and manage data pipelines and analytics solutions on the AWS platform. This course offers a comprehensive understanding of the core AWS services and tools that are essential for ingesting, storing, transforming, and analyzing large-scale data efficiently and securely.

Designed with a hands-on, industry-relevant approach, the course covers vital topics such as data lake architecture, ETL (Extract, Transform, Load) processes, data modeling, and real-time and batch data processing using AWS services like AWS Glue, Amazon Redshift, Amazon Kinesis, Amazon S3, and AWS Lambda. Students will learn to implement data pipelines that support scalable, secure, and cost-effective data workflows across various business use cases.

A strong emphasis is placed on building end-to-end data solutions that are aligned with AWS best practices for performance optimization, security, reliability, and cost-efficiency. The course also includes training on monitoring and troubleshooting data workflows, ensuring data quality and governance, and implementing effective data lifecycle strategies.

Through real-world projects, practical labs, and mock exam sessions, learners will develop the expertise needed to confidently appear for the AWS Certified Data Engineer – Associate exam. By the end of the program, students will be equipped not only with certification readiness but also with practical experience to thrive in roles such as Data Engineer, Big Data Developer, or Analytics Specialist in the fast-growing cloud data landscape.

BIT Dahod ensures that participants gain a future-ready skill set, bridging the gap between theoretical concepts and real-world application in cloud data engineering.

|| Choose AWS Certified Data Engineer Course From BIT

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|| What will I learn?

Implement ETL processes for data transformation and integration using AWS Glue, AWS Data Pipeline, or custom scripts.
Analyze and visualize data using AWS analytics services such as Amazon Redshift, Amazon Athena, and Amazon QuickSight.
Implement big data processing and analytics solutions using AWS services like Amazon EMR and AWS Lambda.

Understand data engineering principles, concepts, and methodologies.
Design and implement data processing architectures using AWS services.
Build and manage data lakes on AWS using services like Amazon S3 and AWS Glue.

|| What will I learn?

Implement ETL processes for data transformation and integration using AWS Glue, AWS Data Pipeline, or custom scripts.
Analyze and visualize data using AWS analytics services such as Amazon Redshift, Amazon Athena, and Amazon QuickSight.
Implement big data processing and analytics solutions using AWS services like Amazon EMR and AWS Lambda.
Understand data engineering principles, concepts, and methodologies.
Design and implement data processing architectures using AWS services.
Build and manage data lakes on AWS using services like Amazon S3 and AWS Glue.

|| Requirements

Basic understanding of data engineering concepts and principles.
Proficiency in at least one programming language (Python, Java, Scala, etc.).

Experience with data analytics and visualization tools (optional but beneficial).
Familiarity with AWS services and cloud computing concepts.

|| Requirements

Basic understanding of data engineering concepts and principles.
Proficiency in at least one programming language (Python, Java, Scala, etc.).
Experience with data analytics and visualization tools (optional but beneficial).
Familiarity with AWS services and cloud computing concepts.

The AWS Certified Data Engineer - Associate course is designed to equip individuals with the skills and knowledge needed to design, build, secure, and maintain analytics solutions on AWS that are efficient, cost-effective, and secure. The course covers a broad range of topics, starting with an introduction to AWS services relevant to data engineering, such as Amazon S3, Amazon Redshift, AWS Glue, and Amazon EMR. Students learn how to design data storage solutions, including data lakes and data warehouses, ensuring optimal performance and cost management. The curriculum delves into data ingestion and transformation techniques using AWS Glue, AWS Lambda, and Amazon Kinesis. It also includes data analysis and visualization with Amazon QuickSight and AWS analytics services. Security is a crucial component, with modules on implementing data encryption, access control, and compliance measures. The course emphasizes practical, hands-on experience, with labs and projects that simulate real-world data engineering scenarios, preparing students for the AWS Certified Data Analytics – Specialty exam. This comprehensive training ensures that participants can effectively manage and analyze large-scale data sets, supporting decision-making and business intelligence initiatives.

Introduction to AWS Big Data
Overview of Big Data concepts and AWS Big Data services.
Understanding the AWS Shared Responsibility Model for data management and security.
Overview of AWS Free Tier and AWS Billing for Big Data services.

Data Collection and Ingestion
Introduction to Amazon S3 (Simple Storage Service) for data storage and ingestion.
Implementing data ingestion pipelines using AWS Glue, AWS Data Pipeline, or AWS Batch.
Real-time data ingestion using Amazon Kinesis streams and firehose.

Data Storage and Processing
Configuring and managing Amazon S3 buckets for data storage and archiving.
Implementing data lake architectures using Amazon S3 and AWS Glue.
Processing and analyzing data using AWS Big Data services such as Amazon EMR (Elastic MapReduce) and Amazon Redshift.

Data Transformation and ETL
Building and orchestrating ETL (Extract, Transform, Load) workflows using AWS Glue.
Writing and executing Spark and PySpark scripts for data transformation.
Leveraging AWS Glue Data Catalog for metadata management and schema inference.

Data Analytics and Visualization
Implementing data analytics solutions using Amazon Athena and Amazon QuickSight.
Creating and optimizing SQL queries for querying data in Amazon S3.
Designing dashboards and visualizations to gain insights from data using Amazon QuickSight.

Real-time Data Processing
Setting up and configuring Amazon Kinesis Data Streams for real-time data processing.
Implementing stream processing applications using Amazon Kinesis Data Analytics and AWS Lambda.
Integrating real-time analytics with other AWS services for event-driven architectures.

Data Security and Compliance
Implementing encryption at rest and in transit for data security using AWS KMS and AWS Certificate Manager.
Configuring access control and permissions for AWS Big Data services using IAM policies and resource policies.
Implementing data governance and compliance controls using AWS services like AWS Config and AWS CloudTrail.

Machine Learning and AI Integration
Integrating machine learning models with AWS Big Data solutions using Amazon SageMaker.
Implementing predictive analytics and anomaly detection using Amazon SageMaker and AWS Glue.
Building and deploying machine learning pipelines for data processing and analysis.

Data Monitoring and Management
Monitoring and logging data ingestion and processing workflows using AWS CloudWatch and AWS CloudTrail.
Setting up alarms and notifications for data quality and performance metrics.
Managing and optimizing AWS Big Data infrastructure for cost efficiency and performance.

Data Governance and Compliance
Implementing data governance policies and best practices for data management and compliance.
Ensuring data privacy and compliance with regulatory requirements such as GDPR and HIPAA.
Auditing and reporting on data access and usage using AWS services like AWS Config and AWS CloudTrail.

Case Studies and Best Practices
Analyzing real-world use cases and architectural patterns for AWS Big Data solutions.
Best practices for designing scalable, reliable, and cost-effective Big Data architectures on AWS.
Review of sample questions and exam preparation tips.

Setting Up AWS Data Lake
Create an Amazon S3 bucket to serve as the data lake storage.
Configure AWS Glue crawlers to catalog data stored in the S3 bucket.
Use AWS Glue to create a data catalog and define schema for different data sources.

Data Ingestion and Processing with AWS Glue
Implement a data ingestion pipeline using AWS Glue ETL jobs to extract, transform, and load data into the data lake.
Schedule AWS Glue crawlers and jobs to run at specific intervals for incremental data updates.
Monitor and troubleshoot AWS Glue jobs using AWS Management Console and CloudWatch logs.

Real-time Data Streaming with Amazon Kinesis
Set up an Amazon Kinesis Data Stream to ingest real-time data from simulated sources (e.g., IoT devices).
Implement Kinesis Data Analytics applications to process and analyze streaming data in real-time.
Integrate AWS Lambda functions with Kinesis Data Streams for real-time data processing and transformation.

Data Analysis and Visualization with Amazon Athena and QuickSight
Create a database and tables in Amazon Athena to query data stored in the S3 data lake.
Write SQL queries to analyze and aggregate data using Athena.
Design and publish dashboards using Amazon QuickSight to visualize insights derived from data queries.

Big Data Processing with Amazon EMR
Launch an Amazon EMR cluster with Hadoop, Spark, or Presto for distributed data processing.
Submit Spark or Hive jobs to the EMR cluster to process large datasets stored in the S3 data lake.
Monitor EMR cluster performance and resource utilization using AWS Management Console and CloudWatch metrics.

Data Orchestration and Workflow Automation
Create and configure AWS Data Pipeline to orchestrate data processing workflows across different AWS services.
Define pipeline activities, dependencies, and schedules using AWS Data Pipeline.
Monitor pipeline execution and troubleshoot errors using AWS Management Console and CloudWatch logs.

Data Security and Compliance
Implement encryption at rest and in transit for data stored in the S3 data lake using AWS KMS and SSL/TLS.
Configure access control and permissions for AWS Glue, Kinesis, Athena, and other Big Data services using IAM policies.
Implement data governance and compliance controls to ensure data privacy and regulatory compliance.

Optimizing Big Data Workloads
Implement cost optimization strategies for AWS Big Data services such as EMR, Glue, and Athena.
Analyze data usage patterns and performance metrics to right-size resources and optimize costs.
Implement data lifecycle management policies to manage data retention and archival in the S3 data lake.

Machine Learning Integration
Integrate Amazon SageMaker with AWS Glue to build and deploy machine learning models for data processing and analysis.
Use SageMaker notebooks for exploratory data analysis and model training on datasets stored in the S3 data lake.
Evaluate model performance and accuracy using SageMaker built-in algorithms and metrics.

Data Monitoring and Management
Set up CloudWatch alarms and metrics for monitoring AWS Big Data services.
Configure automated alerts and notifications for critical data processing events and errors.
Implement log aggregation and analysis using CloudWatch Logs and CloudWatch Insights for data troubleshooting and debugging.

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|| Future Scope and High Demand for AWS Certified Data Engineer

Professionals with certifications relevant to data engineering on AWS, such as the AWS Certified Data Analytics - Specialty, can anticipate a promising future. With the growing reliance on data-driven insights across industries, the demand for skilled data engineers on AWS is set to surge. AWS's comprehensive suite of data services and its emphasis on security and compliance position it as a preferred platform for organizations. Certified professionals can expect abundant career opportunities, competitive salaries, and avenues for professional growth as businesses increasingly leverage data to drive innovation and success. According to a report by Research and Markets, the AWS DevOps Engineer market in India is expected to grow at a CAGR of 25.2% from 2023 to 2028, implying that the number of jobs for AWS DevOps Engineers in India is projected to surpass 100,000 by 2028.

|| Competitive Salaries for AWS Certified Data Engineer In India

The average salary of an AWS Certified Data Engineer - Associate in India varies based on experience levels. Here's a breakdown:

Entry-Level (0-2 years of experience): ₹500,000 - ₹800,000 per year
Mid-Level (2-5 years of experience): ₹800,000 - ₹1,500,000 per year
Senior-Level (5-10 years of experience): ₹1,500,000 - ₹2,500,000 per year

These salary ranges can vary depending on factors such as location (metros like Bangalore, Mumbai, Delhi usually offer higher salaries), specific industry (IT services, finance, healthcare), company size, and additional skills or certifications. Holding an AWS Certified Data Engineer - Associate certification typically commands higher salaries due to the specialized knowledge and skills in managing data engineering tasks on the AWS platform, making it a lucrative career option in India's IT job market.

|| AWS Certified Data Engineer course career options and job opportunities in India

Completing the AWS Certified Data Engineer - Associate course opens up numerous career options and job opportunities in India, as businesses increasingly rely on cloud-based data solutions for their operations. Here are some potential career paths and job opportunities:

Data Engineer: Design, build, and maintain scalable data pipelines and ETL processes using AWS services. In demand across industries like IT, finance, healthcare, and e-commerce.
Big Data Engineer: Specialize in managing and analyzing large datasets using AWS technologies such as Amazon EMR, Redshift, and S3, supporting big data initiatives.
Cloud Data Architect: Design comprehensive cloud-based data solutions, ensuring they meet organizational requirements for performance, scalability, and security.
Data Analyst: Utilize AWS analytics services to extract insights from data, helping organizations make informed business decisions.
Business Intelligence Engineer: Create data models and dashboards using AWS tools like Amazon QuickSight, enabling effective data visualization and reporting.
Data Scientist: Use AWS machine learning and analytics services to build and deploy data-driven models and algorithms.
DevOps Engineer: Integrate and automate data processes within the broader IT infrastructure, ensuring seamless deployment and operation of data applications.
Consultant: Provide expertise to businesses transitioning to or optimizing their use of AWS for data management and analytics.
IT Manager: Oversee the implementation and management of AWS data solutions, ensuring alignment with business goals and strategies.

|| Leading Companies Hiring AWS Certified Data Engineer

Leading companies across various industries actively seek AWS Certified Data Engineers or professionals with expertise in data engineering on AWS. These companies include tech giants like Amazon Web Services (AWS), Google, and Microsoft, all of which rely on data engineering to manage and analyze vast amounts of data. Additionally, companies like Facebook, Netflix, and LinkedIn leverage data engineering to personalize content recommendations, improve user experience, and analyze user behavior on their platforms. Transportation and logistics companies such as Uber utilize data engineering for route optimization and demand forecasting. Similarly, e-commerce platforms like Airbnb and Walmart Labs harness data engineering to optimize pricing, enhance supply chain operations, and improve customer experience. Even companies outside the tech sector, such as Salesforce and Apple, rely on data engineering to analyze customer data and drive business growth. These companies actively recruit professionals with expertise in data engineering on AWS to manage and optimize their data infrastructure, analyze data, and derive actionable insights to inform strategic decision-making and business operations. As the importance of data-driven insights continues to grow across industries, the demand for skilled data engineers on AWS is expected to remain high, making these leading companies attractive employers for professionals in the field.

|| Top Hiring Companies

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|| Job Roles and Salary

Data Engineer

Big Data Engineer

Cloud Data Architect

Data Analyst

Business Intelligence Engineer

Machine Learning Engineer

Data Scientist

DevOps Engineer

0-2 years

2-5 years

5+ years

|| Get AWS Certified Data Engineer Certification Training

Three easy steps will unlock your AWS Certified Data Engineer - Associate Certification:

Finish the online / offline course of AWS Certified Data Engineer - Associate Course and the Assignment.
Take on and successfully complete a number of industry-based Projects
Pass the AWS Certified Data Engineer - Associate certification exam

The certificate for this AWS Certified Data Engineer - Associate Course will be sent to you through our learning management system, where you can also download it. Add a link to your certificate to your CV or LinkedIn profile