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Data Engineering Bootcamp

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Microsoft Certified: Fabric Data Engineer Associate

Microsoft Certified: Fabric Data Engineer Associate

Microsoft Certified: Fabric Data Engineer Associate

 

 

As a Fabric Data Engineer, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes.

  

Databricks Certified Data Engineer Associate

Microsoft Certified: Fabric Data Engineer Associate

Microsoft Certified: Fabric Data Engineer Associate

 The Databricks Certified Data Engineer Associate certification exam assesses an individual’s ability to use the Databricks Lakehouse Platform to complete introductory data engineering tasks. This includes an understanding of the Lakehouse Platform and its workspace, its architecture, and its capabilities.  

AWS Certified Data Engineer - Associate

Microsoft Certified: Fabric Data Engineer Associate

AWS Certified Data Engineer - Associate

validates skills and knowledge in core data-related AWS services, ability to ingest and transform data, orchestrate data pipelines while applying programming concepts, design data models, manage data life cycles, and ensure data quality. 

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120 % Average Salary Hike

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Project Based Learning Model

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Industry Oriented Curriculum

COURSE CURRICULUM

  •  Introduction to Apache Airflow 
  •  Core Concepts of Airflow 
  •  Operators and Task Execution 
  •  Airflow Variables and Connections 
  •  Task Instance and XComs Data
  •  Error Handling and Retry Mechanisms 
  •   Airflow SubDAGs 
  •  Dynamic DAG Generation 
  •  Airflow Plugins 
  •  Airflow for Data Pipelines 


  •  Python basics and Programming Concepts 
  •  File Handling and Data I/O
  •  Data Collection and Cleaning
  •  Working with Files and Data  
  •  ETL (Extract, Transform, Load) with Python 
  •  Working with Relational Databases 
  •  Advanced Data Transformations 
  •  Data Visualization Libraries and  Advanced Visualization Techniques
  • Cloud Data Engineering with Python
  • Data Quality, Testing and Debugging.
  • Interview Preparation (50 most frequently asked Questions)




  •   Introduction to Apache Spark  
  •   Understanding RDD (Resilient Distributed Datasets) 
  •   DataFrames and Spark SQL 
  •  Reading Data into Spark DataFrames 
  •  Basic DataFrame Operations 
  •  Using SQL Queries in Spark
  •  Advanced DataFrame Operations 
  •  Spark Performance Tuning
  •  Batch Data Processing with Spark 
  •  Building ETL Pipelines with Spark 
  • Interview Preparation (50 most frequently asked Questions)




  •  Getting Started with Polars 
  •  Working with Columns and Rows 
  •  Data Aggregation and Transformation 
  •  Reading and Writing Data 
  •  Handling Missing Data 
  •   Parallel and Lazy Execution 
  •  Working with Time Series Data 
  •  Custom Functions and Expressions  



  •   Set Up Databricks Workspace  
  •   Creating and Managing Clusters in Databricks  
  •   Introduction to Databricks Notebooks  
  •   Loading Data in Databricks 
  •   Performing Transformations in Databricks 
  •   Working with Delta Lake (ACID Transactions)  
  •   Databricks Jobs for Automation
  •   Sharing Notebooks and Visualizations  
  •   Performance Tuning and Cost Optimization 



  •  Kafka Basics 
  •  Kafka Components   
  •  Setting Up Kafka 
  •   Kafka Producers in Python  
  •    Kafka Consumers in Python  
  •    Kafka Streams   
  •    Building a Simple Kafka-based Application 
  •    Kafka Use Cases 
  •  Kafka Best Practices 


  •    Introduction to AWS Glue 
  •    Setting Up AWS Glue 
  •    Creating a Glue Job 
  •    Glue DynamicFrames  
  •    Reading and Writing Data   
  •    Monitoring Glue Jobs   
  •    Scheduling Glue Jobs
  •    AWS Glue for Data Lakes 
  •    Best Practices for AWS Glue Jobs    


  •   Introduction to Snowflake  
  •  Setting Up Snowflake  
  •  Snowflake Database Objects 
  •  Basic Snowflake SQL Operations 
  •  Handling Semi-Structured Data (JSON, Avro, Parquet) 
  •  Window Functions and Analytical Queries 
  •  Time Travel & Cloning 
  •  Scaling Compute and Storage Independently 
  •  Data Clustering
  •  User Roles and Permissions
  •  Streams and Tasks (Change Data Capture) 
  •  Snowflake External Functions 
  •  Integrating Snowflake with BI Tools (Power BI, Tableau, etc.) 
  •  Best Practices for Snowflake 

About Kushalify

Our Faculty

Project Based Learning

Our Faculty

We have seasoned IT professionals having significant work experience in top MNCs both product based and consultancy companies.

Our Moto

Project Based Learning

Our Faculty

We want to support people who want to kickstart their journey with IT domain irrespective of the fact they have any prior experience. 

Project Based Learning

Project Based Learning

Project Based Learning

We are dedicated to provide project based learning experience which ensures that our students don't face any challenge when they are exposed to real world projects.

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