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Data Engineering with Azure Course

Master the fundamentals of Data Engineering and get real-time experience working with multiple Databases, Python, and SQL.
  • Accredited by NASSCOM, Approved by Government of India
  • 80 Hours Assignments & Real-Time Projects
  • Complementary Hadoop and Spark
  • Complementary ML on Cloud
  • Complementary Python Programming
  • Enroll and avail Government of India (GOI) Incentives after successfully clearing the mandatory Future Skills Prime Assessment
Data Engineering certification course reviews - 360digitmg

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Data Engineering certification course reviews - 360digitmg

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data engineering certification course training
data engineering certification course training
Academic Partners & International Accreditations
  • Data Engineering Course with Microsoft
  • Data Engineering certification with nasscomm
  • Data Engineering certification innodatatics
  • Data Engineering certification with SUNY
  • Data Engineering certification with NEF
With 360DigiTMG's Certified Course on Data Engineering with Azure, you are introduced to fundamentals as well as advanced concepts of Data Engineering with a special focus on Azure. Students get a chance to extract raw data from various data sources in multiple formats and transform them into actionable insights, and deploy them into a single, easy-to-query database. They learn to handle huge data sets and build data pipelines to optimize processes for big data. Students get a chance to dive deeper into advanced data engineering projects that will help in gaining practical experience. This course is at par with Future Skills Prime, accredited by NASSCOM, and approved by the Government of India.

 

Data Engineering

Program Cost

INR 42,140 25,000/-

(Excluding Taxes)

Data Engineering Course Overview

The certification course in Data Engineering with Azure lets you explore Azure in depth, where you can learn how to create a new Azure account, and various sources offered by it. Master in creating containers and uploading files to Azure Blob Storage. The course will introduce you to Azure Data Factory, How to build Data Ingestion Pipelines Using Azure Data Factory, and Azure Data Factory Integration Runtime. Azure Synapse Analytics is given special focus in this course. Learn to handle huge data sets and build data pipelines to optimize processes for Big Data. Dive deeper into advanced data engineering projects which will help you gain practical experience and skills.

What is Data Engineering?

A Data Engineer collects and transforms data to empower businesses to make data-driven decisions. He has to pay attention to security and compliance; reliability and fidelity; scalability and efficiency; and flexibility and portability while designing, operationalizing, and monitoring data processing systems.

Data Engineering Training Learning Outcomes

Data Engineering with Azure modules will lay out the foundation for Data Engineering with in-depth knowledge of Azure. The course involves an understanding of creating a new Azure account and exploring various services offered by Azure with a special module dedicated to Azure Synapse Analytics. Students will get a keen understanding of how to handle data, as the course progress, they get to learn how to design as well as build data pipelines and work with big data of diverse complexity and production databases. You will also learn to extract and gather data from multiple sources, build data processing systems, optimize processes for big data, build data pipelines, and much more. With this course develop skills to use multiple data sources in a scalable way and also master the skills involved in descriptive and inferential statistics, interactive data analysis, regression analysis, forecasting, and hypothesis testing. Also, learn to

Comprehend the meaning of Data Engineering
Understand the Data Engineering Ecosystem and Lifecycle
Learn to draw data from various files and databases
Acquire skills and techniques to clean, transform, and enrich your data
Learn to handle different file formats in both NoSQL and Relational databases
Learn to deploy a data pipeline and prepare dashboards to view results
Learn to scale data pipelines in the production environment

Block Your Time

data engineering course

40 hours

Classroom Sessions

data engineering course

80 hours

Assignments

Who Should Sign Up?

  • Science, Maths, and Computer Graduates
  • IT professionals who want to Specialize in Digital Tech
  • SQL and related developers or software developers
  • Students/IT professionals have an interest in Data and Databases
  • Professionals working in the space of Data Analytics
  • Academicians and Researchers working with data
  • Cloud and BigData enthusiasts

Data Engineering Course Modules

These modules on Data Engineering are designed to ensure that they are at par with the current industry requirements for Data Engineers. All the modules will wrap up with hands-on practice using real tools and real-time multiple databases. With these modules you will learn to manage, load, extract, and transform data to facilitate delivering of results that your organization can leverage. You will also learn to master the core skills of cleansing, and migrating data.

  • Intro to Data Engineering
  • Data Science vs Data Engineering
  • Building Data Engineering Infrastructure
  • Working with Databases and various File formats (Data Lakes)
    • SQL
      • MySQL
      • PostgreSQL
    • NoSQL
      • MongoDB
      • HBase
      • Apache Cassandra
    • Cloud Sources
      • Microsoft Azure SQL Database
      • Amazon Relational Database Service
      • Google Cloud SQL
      • IBM Db2 on Cloud
  • Extra-Load, Extract-Load-Transform, or Extract-Transform-Load paradigms
  • Preprocessing, Cleaning, and Transforming Data
  • Cloud Data Warehouse Service
    • AWS: Amazon Redshift
    • GCP: Google Big Query
    • IBM: Db2 Warehouse
    • Microsoft: Azure SQL Data Warehouse
  • Distributed vs. Single Machine Environments
  • Distributed Framework - Hadoop
    • Various Tools in Distributed Framework to handle BigData
      • HBase
      • Kafka
      • Spark
      • Apache NiFi
    • Distributed Computing on Cloud
      • ML and AI platforms on Cloud
  • Databases and Pipelines
    • Data Pipeline
      • Features of Pipelines
      • Building a pipeline using NiFi
  • Installing and Configuring the NiFi Registry
    • Using the Registry in NiFi
    • Versioning pipelines
    • Monitoring pipelines
    • Monitoring NiFi using GUI
    • Using Pything with the NiFi REST API
  • Building pipelines in Apache Airflow
    • Airflow boilerplate
    • Run the DAG
    • Run the data pipelines
  • Deploy and Monitor Data Pipelines
    • Production Data Pipeline
    • Creating Databases
  • Data Lakes
    • Populating a data lake
    • Reading and Scanning the data lake
    • Insert and Query a staging database
  • Building a Kafka Cluster
  • Setup Zookeeper and Kafka Cluster
  • Configuring and Testing Kafka Cluster
  • Streaming Data with Apache Kafka
  • Data Processing with Apache Spark
  • Real-Time Edge Data with MiNiFi, Kafka, and Spark
  • Data Science vs Data Engineering
  • Data Engineering Infrastructure and Data Pipelines
  • Working with Databases and various File formats (Data Lakes)
    • SQL
      • MySQL
      • PostgreSQL
    • NoSQL
      • MongoDB
      • HBase
    • Cloud Sources
      • Microsoft Azure SQL Database
      • Amazon Relational Database Service
      • Google Cloud SQL
  • Python Programming
    • Getting started with Python programming for Data Processing
    • Data Types
    • Python Packages
    • Loops and Conditional Statements
    • Functions
    • Collections
    • String Handling
    • File handling
    • Exceptional Handling
    • MySQL Integration
    • INSERT, READ, DELETE, UPDATE, COMMIT, ROLLBACK operations
    • MongoDB Integration
  • Pre-processing, Cleaning, and Transforming Data
  • Data Lake Cloud offerings
  • Cloud Data Warehouse Service
  • Apache Hadoop
    • Pseudo Cluster Installation
    • HDFS
    • Hive
    • HBase
    • Sqoop
  • Big Data and Apache Kafka
  • Producers and Consumers
  • Clusters Architectures
  • Kafka Streams
  • Kafka pipeline transformations
  • Spark Components
  • Spark Executions – Session
  • RDD
  • Spark DataFrames
  • Spark Datasets
  • Spark SQL
  • Spark Streaming
  • Lambda
  • Kappa
  • Streaming Big Data Architectures Monitoring pipelines
  • Building pipelines in Apache Airflow
  • Deploy and Monitor Data Pipelines
  • Production Data Pipeline
  • Building a Kafka Cluster
  • Setup Zookeeper and Kafka Cluster
  • Configuring and Testing Kafka Cluster
  • Streaming Data with Apache Kafka
  • Data Processing with Apache Spark
  • AWS Sagemaker for end-to-end ML workflow
  • Azure Data factory for ETL

How we prepare you

  • data engineering course with placements
    Additional assignments of over 80+ hours
  • data engineering course with placements training
    Live Free Webinars
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    Resume and LinkedIn Review Sessions
  • data engineering course with certification
    Lifetime LMS Access
  • data engineering course  with USP
    24/7 support
  • data engineering certification with USP
    Job placements in Data Engineering fields
  • best data engineering course with USP
    Complimentary Courses
  • best data engineering course with USP
    Unlimited Mock Interview and Quiz Session
  • best data engineering training with placements
    Hands-on experience in a live project
  • data engineering course with USP
    Offline Hiring Events

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