AWS Data Engineer Job Oriented Program
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Cloud Engineer at Walmart Labs
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Prepzee's Cloud Masters program changed my career from SysAdmin to Cloud Expert in just 6 months. Thanks to dedicated mentors, I now excel in AWS, Terraform, Ansible, and Python.
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Product Manger at Icertis Hear My StoryGreat learning experience through the platform. The curriculum is updated and covers all the topics. The trainers are experts in their respective fields and follow more of a practical approach.
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Senior Associate Product at Amazon Hear My StoryNice experience, I will recommend it to all the learners who are willing to join and learn IT skills. I was able to switch my domain from non-IT to IT in a reputed MNC
You’re an IT Professional who is looking for a career in Data Engineering especially dealing with Cloud-based solutions.
You’re looking to switch domains into the Future Proof Data Industry without going into Statistics and coding may start in Data Engineering.
You’re a DBA, with experience in database management and SQL, and can transition into data engineering roles with ease.
You’re a Data Analyst/ Scientist who wants to work with data at a larger scale and manage data pipelines that may transition into data engineering.
Including Top 2 Data Engineering Tools according to Linkedin Jobs
Learn by doing multiple labs in your learning journey.
Get a feel of AWS Data Engineering professionals by doing real-time projects.
Call us, E-Mail us whenever you stuck.
Instructors are Microsoft Certified Trainers.
Attend multiple batches until you achieve your Dream Goal.
Python Fundamentals
Setting up Python Virtual Environment
Implementing Conditional Statements
Working with Loops
Exploring Numeric Data Types(Numbers)
Understanding Tuples and Their Operations
Understanding Functions in Python
Working with OOP Concepts
Standard Libraries in Python
Exception Handling in Python
Understanding Structured, Unstructured, and Semi-Structured
Properties of Data: Volume, Velocity, and Variety
Comparing Data Warehouses and Data Lakes
Managing and Orchestrating ETL Pipelines for Data Processing
Data Modeling, Data Lineage, and Schema Evolution
Optimizing Database Performance
Introduction to PySpark Shell
Submitting PySpark Jobs for Execution
Navigating the Spark Web User Interface
Exploring Dataframes and Spark SQL
Comparing Dataframes with RDDs
Understanding the Lazy Evolution of Dataframes
Analyzing Directed Acyclic Graph (DAG) Stages and Spark Jobs
Optimizing Dataframes for Performance
Interoperating with RDDs: Bridging the Gap
Handling JSON and Parquet File Formats
Spark Streaming: Concept and Applications
Spark Streaming for Real-Time Data Processing
Understanding Compute Services in AWS
Understanding AWS Storage Services
Database Services: DynamoDB, RDS, Redshift
Amazon Database Migration Service
AWS DataSync
AWS Snow Family
Amazon Management and Governance
AWS CloudFormation
AWS CloudWatch
Traditional ETL vs AWS Glue
AWS Glue DataBrew
Implementing a Glue ETL Job
Understanding Amazon Kinesis Data Streams
Kinesis Data Firehose
Querying Data with Amazon Athena SQL
Troubleshooting Amazon Athena Queries
Overview of Hadoop and Serverless EMR
Comparing EMR with AWS Glue for ETL
Introduction to Amazon OpenSearch
Understanding QuickSight Pricing and Creating Dashboards
Exploring AWS Step Functions
Overview of Amazon SageMaker
Utilizing SageMaker Feature Store
Understanding Identity and Access Management (IAM)
Understanding Managed Streaming for Apache Kafka (MSK)
Provisioning Amazon MSK Cluster
Exploring MSK – Connect and Serverless Features
Processing Streaming Data with Amazon MSK
Introduction to Managed Workflows for Apache Airflow (MWAA)
DAGs and Components for MWAA
Setting Up Managed Airflow Environment
Building Workflows with Managed Apache Airflow
Understanding of ECS: Elastic Container Service
Understanding ECR: Elastic Container Registry
Overview of EKS: Elastic Kubernetes Service
AWS CodeCommit
AWS CodeBuild
AWS CodeDeploy
AWS CodePipeline
Introduction of SnowFlake Data Warehousing Service
SnowFlake Architecture
Complete Setup of SnowFlake
Create Data Warehouse on SnowFlake
Analytical Queries on SnowFlake Data Warehouse
Understand the entire Snowflake workflow from end-to end
Undestanding SnowPark (Execute PySpark Application on SnowFlake)
Solve AWS Data Engineer DEA-C01 Sample Exam Papers
Multiple Quizzes
Mock Certification Exam
14+ Years of experience
15+ Years of experience
12+ Years of experience
17+ Years of experience
Responsible for designing, implementing, and maintaining data pipelines and infrastructure on AWS, ensuring efficient data processing and analysis.
A Cloud Data Engineer specializes in managing data on cloud platforms, designing scalable solutions using cloud-native tools and services.
Integrates data from multiple sources into a unified ecosystem, designing and implementing data integration workflows.
Designs data architectures on AWS, defining data models and storage structures to meet business requirements
The AWS Platform Data Engineer creates and manages data solutions on AWS, ensuring optimal performance and security. They develop scalable pipelines.
They designs and implements tailored data solutions using advanced tools, focusing on data modeling, pipeline development, and governance for optimal performance and reliability.
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Embark on your journey towards a thriving career in AWS data engineering with best Data Engineering courses. This comprehensive program is meticulously crafted to empower you with the skills and expertise needed to excel in the dynamic world of data engineering.Learn Data Engineering with Prepzee, throughout the program, you’ll explore a wide array of essential tools and technologies, including industry favorites like PySpark, Kafka and Airflow.Dive into industry projects, elevate your CV and LinkedIn presence, and attain mastery in Data Engineer technologies under the mentorship of seasoned experts.
1.1: Exploring Python Fundamentals
1.2: Applications and Use Cases of Python
1.3: Setting up Python Virtual Environment
1.4: Utilizing Pip Installer
1.5: Understanding Visual Studio Code/PyCharm
1.6: Understanding Values, Types, and Variables
1.7: Exploring Operands and Expressions
1.8: Implementing Conditional Statements
1.9: Working with Loops
1.10: Parsing Command Line Arguments
1.11: Outputting Data to the Screen
1.12: Managing File Input and Output in Python
1.13: Exploring Numeric Data Types(Numbers)
1.14: Manipulating Strings and Related Operations
1.15: Understanding Tuples and Their Operations
1.16: Exploring Lists and Their Operations
1.17: Working with Dictionaries and Their Operations
1.18: Utilizing Sets and Their Operations
Hands On:
2.1: Understanding Functions in Python
2.2: Exploring Different Types of Function Parameters
2.3: Managing Global Variables
2.4: Understanding Variable Scope and Returning Values
2.5: Exploring Lambda Functions and their Applications
2.6: Introduction to Object-Oriented Programming (OOP) Concepts
2.7: Utilizing Standard Libraries in Python
2.8: Exploring Various Modules Used in Python
2.9: Understanding Import Statements in Python
2.10: Navigating the Module Search Path
2.11: Different Ways of Package Installation
Hands On:
3.1: Analyzing Spark Components and its Architectural Framework
3.2: Introduction to PySpark Shell
3.3: Submitting PySpark Jobs for Execution
3.4: Navigating the Spark Web User Interface (UI)
3.5: Developing Your Initial PySpark Job Utilizing Visual Studio Code
Hands On:
4.1: Identifying Challenges in Traditional Computing Methods
4.2: Exploring Potential Solutions and How RDD Addresses the Challenges
4.3: Understanding RDD: Definition, Operations, Transformations, and Actions
5.1: Introduction to Dataframes: Concept and Utility
5.2: Comparing Dataframes with RDDs
5.3: Understanding Dataframe Structure and Data Copying Operations
5.4: Dataframe Transformations and Actions: Manipulating Data Effectively
5.5: Differentiating between Wide and Narrow Transformations
5.6: Understanding the Lazy Evolution of Dataframes
5.7: Analyzing Directed Acyclic Graph (DAG) Stages and Spark Jobs
5.8: Optimizing Dataframes for Performance
5.9: Utilizing Spark SQL Functions and Aggregations
5.10: Working with Hive Tables and SQL Queries
5.11: Exploring the Need for Spark SQL
5.12: Understanding Spark SQL
5.13: Implementing User-Defined Functions (UDFs)
5.14: Interoperating with RDDs: Bridging the Gap
5.15: Handling JSON and Parquet File Formats
5.16: Loading Data from Various Sources
Hands On:
6.1: Identifying Limitations in Traditional Computing Approaches
6.2: Understanding the Importance and Need for Streaming Data Processing
6.3: Introduction to Spark Streaming: Concept and Applications
6.4: Key Features of Spark Streaming for Real-Time Data Processing
6.5: Navigating the Spark Streaming Workflow
6.6: Exploring Streaming Context and Discretized Streams (DStreams)
6.7: Applying Transformations on DStreams for Data Manipulation
6.8: Understanding Windowed Operators and their Significance
6.9: Essential Windowed Operators: Slice, Window, and ReduceByWindow
6.10: Utilizing Stateful Operators for Managing State Across Batches
Hands On:
7.1: Understanding Different Types of Data: Structured, Unstructured, and Semi-Structured
7.2: Exploring the Properties of Data: Volume, Velocity, and Variety
7.3: Comparing Data Warehouses and Data Lakes (Including Lakehouses)
7.4: Introduction to “Data Mesh” Concept and Its Implications
7.5: Managing and Orchestrating ETL Pipelines for Data Processing
7.6: Common Data Sources and Formats in Data Engineering
7.7: Brief Overview of Data Modeling, Data Lineage, and Schema Evolution
7.8: Optimizing Database Performance
7.10: Exploring Data Sampling Techniques
7.11: Understanding Data Skew Mechanisms
7.12: Implementing Data Validation and Profiling
8.1: Understanding Compute Services in AWS
8.2: Introduction to Amazon EC2: Elastic Compute Cloud
8.3: Exploring AWS Batch for Batch Computing Jobs
8.4: Introduction to ECS: Elastic Container Service
8.5: Understanding ECR: Elastic Container Registry
8.6: Overview of EKS: Elastic Kubernetes Service
8.7: Introduction to AWS Lambda and Serverless Computing
8.8: Invoking and Monitoring Lambda Functions
8.9: Understanding AWS SAM: Serverless Application Model
Hands On:
9.1: Understanding AWS Storage Services
9.2: Overview of Amazon S3: Simple Storage Service
9.3: Exploring Storage Classes
9.4: Introduction to Versioning
9.5: Utilizing Server-Access Logging for Enhanced Security
9.6: Implementing Object-Level Logging for Detailed Analysis
9.7: Understanding Object Lock for Data Governance
9.8: Utilizing Policies to Control Access in Amazon S3
9.9: Implementing Amazon S3 Replication for Disaster Recovery
9.10: Securing Data with Bucket Key Encryption
9.11: Utilizing Lifecycle Configurations for Efficient Data Management
9.12: Components and Creation of Lifecycle Configurations
9.13: Exploring Other Considerations and Limitations in Amazon S3
9.14: Introduction to Amazon Elastic Block Store (EBS)
9.15: Understanding Amazon Data Lifecycle Manager
9.16: Exploring Amazon Elastic File System (EFS)
9.17: Storage Classes and Performance Options in EFS
9.18: Creating and Managing EFS File Systems
9.19: Ensuring EFS Security and Importing Data
9.20: Introduction to AWS Backup for Data Protection
Hands On:
Hands On:
10.1: Introduction to Database Services in AWS(DEA-C01)
10.2: Amazon Relational Database Service: Overview
10.3: Walkthrough: Setting Up an Amazon RDS Database
10.4: Purchasing Options for RDS Instances
10.5: Pricing for Database Storage and I/O
10.6: Cost Breakdown for Backup Storage
10.7: Understanding Backtrack Storage Pricing
10.8: Snapshot Export Costs
10.9: Data Transfer Pricing in AWS
10.10: Introduction to Amazon DynamoDB
10.11: Core Features of DynamoDB
10.12: DynamoDB Terminology: Understanding the Basics
10.13: Comparative Analysis of DynamoDB with Other Database Solutions
10.14: Interacting with DynamoDB: Console Operations
10.15: Data Manipulation in DynamoDB via Code
10.16: Querying and Scanning DynamoDB Programmatically
10.17: Performance Optimization Strategies for DynamoDB
10.18: Provisioning Table Access in DynamoDB
10.19: Best Practices for DynamoDB Table Modeling
10.20: Creating and Deleting DynamoDB Tables Using the AWS Console
10.21: Introduction to Partitioning in DynamoDB
10.22: Achieving Balance in DynamoDB Partitioning
10.23: DynamoDB Accelerator (DAX): An Overview
10.24: Introduction to Amazon Neptune: A Graph Database Service
Demo:Deploying an Amazon Neptune Database
10.25: Introduction to Amazon Redshift: Features and Benefits
10.26: Overview of Redshift’s COPY Command
10.27: Upserting Data in Amazon Redshift
10.28: Resizing Operations in Amazon Redshift
10.29: Concurrency Scaling in Amazon Redshift
10.30: Understanding Data Distribution in Redshift
10.31: Different Types of Distribution Styles in Redshift
10.32: Distribution Keys vs. Sort Keys: A Comparative Overview
10.33: Example Illustration of Distribution Styles
10.34: Managing Cold Data in Amazon Redshift
10.35: Introduction to Amazon Redshift Spectrum
10.36: Step-by-step Guide to Executing a Spectrum Query
10.37: Insights into Spectrum Internals
10.38: Considerations for Amazon Redshift Spectrum
10.39: Cost Implications of Amazon Redshift Spectrum
10.40: Exploring Materialized Views in Amazon Redshift
10.41: Utilizing the VACUUM Command in Amazon Redshift
10.42: Data Sharing in Amazon Redshift
10.43: Leveraging the Data API for Amazon Redshift
10.44: Introduction to Amazon Redshift Serverless
10.45: Amazon DocumentDB: MongoDB Compatibility in AWS
Demo: Setting Up an Amazon DocumentDB Cluster
10.46: Amazon Keyspaces: Apache Cassandra in AWS
Demo: Creating a Keyspace and Table in Amazon Keyspaces
10.47: Amazon MemoryDB for Redis: An Overview
Hands On: DynamoDB Essentials
Hands On : Introduction to Amazon Redshift
Hands On : Configuring Distribution Styles and Access Control in Amazon Redshift
11.1: Introduction to Networking and Content Delivery (DEA-C01)
11.2: Understanding Virtual Private Cloud (VPC)
11.3: Exploring Subnets in VPC
11.4: Network Access Control Lists (NACLs) Overview
11.5: Security Groups: Enhancing Network Security
11.6: NAT Gateway: Facilitating Outbound Internet Traffic
11.7: VPN & Direct Connect: Secure Network Connectivity Options
11.8: Analyzing VPC Flow Logs for Network Monitoring
11.9: VPC Peering: Connecting VPCs Securely
11.10: Exploring VPC Endpoints for Service Access
11.11: Introduction to AWS PrivateLink
11.12: Working with Amazon CloudFront for Content Delivery
11.13: Common Patterns for Implementing Amazon CloudFront
11.14: Introduction to Amazon Route S3 DNS Service
11.15: Managing DNS Records with Amazon Route S3
11.16: Performing Health Checks with Amazon Route S3
11.17: Understanding Routing Policies in Amazon Route S3
11.18: Configuring Traffic Flow with Amazon Route S3
11.19: Utilizing Amazon Route S3 Resolver for Hybrid Clouds
11.20: Overview of Application Recovery Controller
11.21: Summary of Using Amazon Route S3
12.1: Introduction to Migration and Transfer
12.2: Exploring Application Discovery Service & Application Migration Service
12.3: Understanding AWS Database Migration Service (AWS DMS)
Hands On:
13.1: Introduction to Management and Governance (DEA-C01)
13.2: Understanding Amazon CloudWatch
13.3: Exploring CloudWatch Dashboards
13.4: CloudWatch Subscriptions
13.5: AWS CloudTrail: Tracking API Activity in AWS
13.6: Configuring AWS Config for Governance and Compliance
13.7: Introduction to AWS Systems Manager for Operational Management
13.8: Evaluating the Role of Systems Manager in the AWS Tool Set
13.9: Managing Resource Groups in AWS
13.10: Requirements and Building Blocks of AWS Systems Manager
13.11: Utilizing AWS Systems Manager Parameter Store
13.12: Introduction to AWS CloudFormation
13.13: Understanding the Structure of a CloudFormation Template
13.14: Building a CloudFormation Template: Hands-On Demonstration
13.15: Deploying a CloudFormation Template: Practical Exercise
13.16: Overview of Amazon Managed Grafana
13.17: Introduction to AWS Budgets for Cost Management
13.18: Analyzing Costs with Cost Explorer in AWS
14.1: Introduction to Security, Identity, and Compliance (DEA-C01)
14.2:Understanding Identity and Access Management (IAM)
14.3: Features of IAM and User Dashboard Overview
14.4: Creating and Managing IAM Users
14.5: User Group Management with IAM
14.6: IAM Roles and Their Users
14.7: Leveraging AWS Service Roles for Resource Access
14.8: Granting Temporary Access with IAM User Roles
14.9: Federated Access with IAM Roles
14.10: AWS Policy Types and Structure
14.11: Crafting AWS IAM Policies
14.12: Understanding Encryption and AWS Key Management Service (KMS)
14.13: Exploring Amazon Macie
14.14:Parameters and Secrets Management
14.15: Introduction to DDoS and AWS Shield
14.16: Overview of AWS WAF and Rule Groups
14.17: Creating and Configuring Web ACL
14.18: Introduction to CloudHSM
Hands On:
15.1: Introduction to Analytics
15.2: Understanding AWS Glue and Its Components
15.3: Authoring Solutions in AWS Glue Studio Console
15.4: Data Quality Assessment in AWS Glue
15.5: Modifying Glue Data Catalog from ETL Scripts
15.6: Developing ETL Jobs with Glue ETL
15.7: Cost Considerations and Best Practices for AWS Glue
15.8: Exploring AWS Glue Studio and Data Quality Features
15.9: Introduction to AWS Glue DataBrew
15.10: Demonstrating AWS Glue DataBrew
15.11: Managing PII Data with DataBrew Transformations
15.12: Introduction to AWS Glue Workflows
15.13: Comparing Amazon EMR and AWS Glue for ETL
Hands On: ETL Workloads with AWS Glue
16.1: Understanding Amazon Kinesis Data Streams
16.2: Key Components and Scaling of Kinesis Data Streams
16.3: Handling Duplicates and Security in Kinesis Data Streams
16.4: Introduction to Kinesis Data Firehose
16.5: Troubleshooting and Performance Tuning for Kinesis Data Streams
16.6: Overview of Kinesis Analytics and Amazon Managed Service for Apache Flink (MSAF)
Hands On: Processing Streaming Metadata with Amazon Kinesis Data Streams
Hands On : Sessionizing Clickstream Data with Amazon Kinesis and Managed Apache Flink
17.1: Querying Data with Amazon Athena SQL
17.2: Utilizing Athena for Apache Spark
17.3: Performance and Transactional Capabilities of Athena
17.4: Fine-Grained Access Control with AWS Glue Data Catalog
17.5: CTAS Functionality and Spark Integration
Hands On: Analyzing Log Data with Kinesis Agent and Amazon Athena
Hands On: Troubleshooting Amazon Athena Queries
18.1: Understanding EMR Characteristics and Architecture
18.2: Integration with AWS Services and Storage
18.3: Overview of Hadoop and Serverless EMR
18.4: Comparing EMR with AWS Glue for ETL
Hands On: Getting Started with Amazon EMR
19.1: Understanding Amazon MSK and Kafka Components
19.2: Provisioning Amazon MSK Cluster
19.3: Exploring MSK – Connect and Serverless Features
Hands On: Processing Streaming Data with Amazon MSK
20.1: Introduction to Managed Workflows for Apache Airflow (MWAA)
20.2: DAGs and Components for MWAA
20.3: Setting Up Managed Airflow Environment
Hands On: Building Workflows with Managed Apache Airflow
21.1: Introduction to Amazon OpenSearch Service
21.2: Managing OpenSearch Indexes and Designing for Stability
21.3: Optimizing Amazon OpenSearch Service Performance
21.4: Exploring Serverless Options with Amazon OpenSearch
21.5: Overview of Amazon QuickSight
21.6: Understanding QuickSight Pricing and Creating Dashboards
21.7: Leveraging ML Insights with QuickSight
Hands On: Visualizing Data in Amazon QuickSight
22.1: Introduction to Application Integration (DEA-C01)
22.2: Understanding Decoupled and Event-Driven Architecture
22.3: Exploring EventBridge
22.4: Deep Dive into Decoupling Applications with Queuing Services
22.5: Leveraging Simple Notification Service (SNS) for Notifications
22.6: Introduction to AWS Step Functions
Hands On: Introduction to AWS Step Functions
23.1: Introduction to Developer Tools (DEA-C01)
23.2: Overview of AWS Command Line Interface (CLI)
23.3: Installing and Configuring AWS CLI
23.4: Managing Credentials and Profiles
23.5: Understanding Command Structure and Output Control
23.6: Exploring Input Features for Ease of Use
23.7: Introduction to AWS Cloud9
Hands On: Creating a Cloud9 Environment
Hands On: Connecting to an AWS CodeCommit Repository
24.1: Introduction to Machine Learning Concepts
24.1: Overview of Amazon SageMaker
24.1: Utilizing SageMaker Feature Store
24.2: Tracking ML Lineage with SageMaker
24.3: Data Preparation with SageMaker Data Wrangler
25.1 Snowflake Overview and Architecture
25.2 Connecting to Snowflake
25.3 Data Protection Features
25.4 SQL Support in Snowflake
25.5 Caching in Snowflake
Query Performance
25.6 Data Loading and Unloading
25.7 Functions and Procedures
Using Tasks
25.8 Managing Security
Access Control and User Management
25.9 Semi-Structured Data
25.10 Introduction to Data Sharing
25.11 Virtual Warehouse Scaling
25.12 Account and Resource Management
26.1: CV Preperation
26.2: Interview Preperation
26.3: LinkedIn Profile Update
26.4: Expert Tips & Tricks
Our tutors are real business practitioners who hand-picked and created assignments and projects for you that you will encounter in real work.
Stream IoT data via Kinesis, analyze real-time patterns with Kinesis Analytics, and store results in S3 or Redshift.
Migrate on-premise data to Redshift, transform it using AWS Glue, and optimize the schema for faster querying and scalability.
Use AWS Glue to extract product, sales, and customer data from S3, transform it to aggregate sales by region and product category, and load it into an AWS Redshift data warehouse for reporting and analysis.
Leverage PySpark for big data processing to segment customers based on purchasing behavior, and provide insights for personalized marketing strategies.
Use Apache Airflow to automate the ingestion of real-time stock market data from APIs, process the data to calculate performance metrics, and store it in a database for further analysis and forecasting.
Use Kafka to capture clickstream data, process it for user behavior insights, and store results in a data warehouse.
Enrolling in the AWS Data Engineer Job Oriented Program by Prepzee for the AWS Data Engineer certification (DEA C01) was transformative. The curriculum covered critical tools like PySpark, Python, Airflow, Kafka, and Snowflake, offering a complete understanding of cloud data engineering. The hands-on labs solidified my skills, making complex concepts easy to grasp. With a perfect balance between theory and practice, I now feel confident in applying these technologies in real-world projects. Prepzee's focus on industry-relevant education was invaluable, and I’m grateful for the expertise gained from industry professionals.
I enrolled in the DevOps Program at Prepzee with a focus on tools like Kubernetes, Terraform, Git, and Jenkins. This comprehensive course provided valuable resources and hands-on labs, enabling me to efficiently manage my DevOps projects. The insights gained were instrumental in leading my team and streamlining workflows. The program's balance between theory and practice enhanced my understanding of these critical tools. Additionally, the support team’s responsiveness made the learning experience smooth and enjoyable. I highly recommend the DevOps Program for anyone aiming to master these essential technologies.
Enrolling in the Data Engineer Job Oriented Program at Prepzee,, exceeded my expectations. The course materials were insightful and provided a clear roadmap for mastering these tools. The instructors' expertise and interactive learning elements made complex concepts easy to grasp. This program has been invaluable for my professional growth, giving me the confidence to apply these technologies effectively in real-world projects.
Enrolling in the Data Analyst Job Oriented Program at Prepzee, covering Python, SQL, Advanced Excel, and Power BI, was exactly what I needed for my career. The course content was well-structured and comprehensive, catering to both beginners and experienced learners. The hands-on labs helped reinforce key concepts, while the Prepzee team’s support was outstanding, always responsive and ready to help resolve any issues.
Prepzee has been a great partner for us and is committed towards upskilling our employee.Their catalog of training content covers a wide range of digital domains and functions, which we appreciate.The best part was there LMS on which videos were posted online for you to review if you missed anything during the class.I would recommend Prepzee to all to boost his/her learning.The trainer was also very knowledgeable and ready to answer individual questions.
01/06/2025 - 05/10/2025
7:00 pm TO 10:00 pm IST (GMT +5:30)
Online(Sat-Sun)
12 Hours left at this price!
Get Certified after completing AWS Data Engineer full course with Prepzee
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Data Transformation
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