GoLogica Azure Data Engineering Training in Bangalore includes hands-on training with expert-led workshops, real-time projects, and guidance for certification. Learn in-depth about the integration of data, its transformation and pipeline development on Azure. Improve your career by learning industry-specific skills and being prepared to be a top cloud role in data engineering.
Online self-learning courses offer autonomy, allowing individuals to learn at their own pace. They provide structured training materials with review exercises to enhance understanding. Utilizing multimedia resources like videos and presentations, learners actively engage with the content. while flexibility enables customization of study schedules. This fosters an environment conducive to effective learning and skill development, accommodating personal commitments.
GoLogica Azure Data Engineering Training in Bangalore is a specific training program designed to prepare professionals for a career in data engineering by using Microsoft Azure. This extensive training program is focused on the development of core competencies needed to develop the data management system and monitoring, security and privacy with the complete array of Azure data services.
The course covers the fundamental concepts and tools, including Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Data Lake Storage, and Azure Stream Analytics. With a focus on practical experience, students will be working on real-time tasks and case studies to learn how to create robust data pipelines that can scale and change raw data into invaluable insights and then implement end-to-end data solutions within the Azure environment.
Created by experts from the industry, the course's curriculum is in line with those who have worked on the DP-203 accreditation (Data Engineering using Microsoft Azure), helping learners not only Learn the basics while also preparing for the official test. Suppose you are considering a transition into the field of data engineering or want to increase your current skills. In that case, this course will ensure that you acquire the technical knowledge that modern companies require.
GoLogica-trained and experienced trainers provide in-depth understanding, individualized mentorship, and interactive training sessions that blend theory and practical application. The program also provides assignments, mock interviews and guidance on certification to ensure complete learning and job-ready.
Perfect for professionals in data as well as programmers, administrators of databases, and IT professionals seeking to focus on cloud-based solutions for data. This Azure Data Engineering Training in Bangalore provides you with the necessary skills to use large-scale data systems and improve data storage and data workflows in a cloud-based environment.
When you finish the course, you will be able to develop secure and reliable data platforms using Azure utilize automation tools effectively, and provide insights to aid in business decision-making. Sign up today with GoLogica and take the first step towards becoming an Azure Data Engineer, ready to face the challenges of a data-driven world.
Salary Trends:
According to ZipRecruiter, The average salary of an Azure Data Engineer professional typically ranges from $119k to $124k PA. It depends on factors such as experience, location, and specific job responsibilities.
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Azure Data Engineer Curriculum
Azure Databricks Introduction
Databricks Architecture
Databricks Components overview
Benefits for data engineers and data scientists
Azure Databricks concepts
Workspace – Creation and managing workspace.
Notebook – creating notebooks, calling and managing different notebooks.
Pool - creating pools and using pools for Auto scaling.
Databricks Runtime - understanding and using Databricks runtimes based on requirement.
Jobs - creating jobs from notebooks and assigning types of clusters for jobs.
Workload - monitoring jobs and managing loads.
Execution Context – understanding context.
Databricks Advanced topics.
Databricks Workflows
Calling one notebook into another notebook.
Creating global variables (widgets) and using into Azure ADF pipeline.
How to implement parallelism in notebooks execution.
Mounting azure blob storage and data lake storage accounts.
Integrating source code (notebooks) with GitHub
Calling DataBricks notebooks into Azure Data factory.
Databricks Clusters logs monitoring flow.
Introduction to Spark
What is Spark and what is its purpose?
Components of the Spark unified stack
Resilient Distributed Dataset (RDD)
Downloading and installing Spark standalone
Scala and Python overview
Launching and using Spark’s Scala and Python shell
Resilient Distributed Dataset and Data Frames
Understand how to create parallelized collections and external datasets
Work with Resilient Distributed Dataset (RDD) operations
Utilize shared variables and key-value pairs
Spark application programming
Understand the purpose and usage of the Spark Context
Initialize Spark with the various programming languages
Describe and run some Spark examples
Pass functions to Spark
Create and run a Spark standalone application
Submit applications to the cluster
Introduction to Spark libraries
Understand and use the various Spark libraries
Spark configuration, monitoring and tuning
Understand components of the Spark cluster
Configure Spark to modify the Spark properties, environmental variables, or logging properties
Understand components of the Spark cluster Monitor Spark using the web UIs, metrics, and external instrumentation ,Understand performance tuning considerations
Introduction To Pyspark
What is Spark Session?
How to create spark session
What is Spark Context?
How to create Spark Context
What is SQL CONTEXT?
How to Use Jupiter Notebooks & Databricks notebooks for Python Development. Install and configure PySpark in Local System for development. Introduction to Big Data and Apache Spark Apache Spark Framework & Execution Process. Introduction To RDDs
Different Ways to Create RDD’s in Pyspark.
RDD Transformations
RDD Actions
RDD Cache & Persist
Introduction to Data Frame.
Different Ways to Create Data Frame’sin Pyspark.
Dataframe Transformations
Dataframe Actions
Dataframe Cache & Persist
Different types of Big Data File systems.
Difference between Row store format and column store format.
Avro File
Parquet file
ORC File
Reading and Writing Different Types of Files using Dataframe.
CSV files
Json files
Xml files
Excel files
Complex Json files
Avro files
Parquet files
Orc files
Need for Spark SQL
What is Spark SQL
SQL Table Creation
SQL Join Types
SQL Nested Queries
SQL DML Operations
SQL Merge Scripts
SQL SCD Type 2 implementation
User-Defined Functions Create Private Link to different services Limitation What is Azure Data Factory How to create Data Factory + All Components Creating Pipeline + Copy Activity General Activities Iteration and Conditions Data Flow Resource + Data Flow Activity Synapse Activities Azure Monitor and Manage
Delta Lake usage in Databricks.
Delta Lake Architecture
Delta Lake Storage Understanding
Delta lake table creation and API options
Delta Lake DML Operations usage.
Delta Lake partitions
Delta Lake Schema Enforcement
Delta Lake Schema Evolution
Delta Lake Versions
Delta Lake Time Travel
Delta Lake Vaccum
Delta Lake Merge (SCD Type 1 and SCD Type2)
Overview of the Microsoft Azure Platform
Introduction to Azure
Basics of Cloud computing
Azure Infrastructure
Walkthrough of Azure Portal
Overview of Azure Services
Azure Data Architecture
Traditional RDBMS workloads.
Data Warehousing Approach
Big data architectures.
Transferring data to and from Azure
Azure Storage options
Blob Storage
ADLS Gen1 & Gen2
RDBMS
Hadoop
NoSQL
Disk
Blob Storage
Azure Blob Resources
Types of Blobs in Azure
Azure storage account data objects
Azure storage account types and Options
Replications in distribution
Secure access to an application's data
Azure Import/Export service
Storage Explorer
Practical section on Blob Storage
Azure Data Factory
Azure Data Factory Architecture
Creating ADF Resource and Use in azure cloud
Pipeline Creation and Usage Options
Copy Data Tool in ADF Portal, Use
Linked Service Creation in ADF
Dataset Creation, Connection Reuse
Staging Dataset with Azure Storage
ADF Pipeline Deployments
Pipeline Orchestration using Triggers
ADF Transformations and other tools integration.
Processing different type’s files using ADF.
Integration Runtime
Monitoring ADF Jobs
Manage IR’s and Linked Services.
Azure SQL Database Service
Introduction to Azure SQL Database
Relational Data Services in the Cloud
Azure SQL Database Service Tiers
Database Throughput Units (DTU)
Scalable performance and pools
Creating and Managing SQL Databases
Azure SQL Database Tools
Migrating data to Azure SQL Database
Azure Data Lake Gen1 & Gen2
Explore the Azure Data Lake enterprise-class security features.
Understand storage account keys.
Understand shared access signatures.
Understand transport-level encryption with HTTPS.
Understand Advanced Threat Protection.
Control network access.
Differences between Gen1 & Gen2
Azure Synapse SQL DW (Dedicated SQLPOOL)
Azure Synapse DW (Dedicated SQL POOL)?
Synapse DW Architecture.
Creating Internal table with default distribution
Creating external table in synapse dw
Loading data from databricks to azure synapse dw
Loading data from adls gen2 to azure synapse dw
What is dedicated sql pool
data warehouse unit overview
Distributed table with example
Hash distribution with example
Round robin distribution with example
Replicate distribution with example
What are the types of indexes with examples
Clustered Index with example
Non-Clustered index with example
Clustered Column Store Index with example
Heap Index with example
Introduction to Spark SQL.
Spark SQL Create database
Drop databases
Create internal table
Create external table
Create partitioned table
Create partitioned with bucketing table
SPARK DML insert, update, delete and merge operations
SPARK SQL DRL Select queries with different clauses
Spark SQL MERGE With SCD Type 1 and SCD Type 2
Spark SQL WHERE Clause, Group By Clause and Having Clauses
Spark SQL Order by, Sort By clauses
Spark SQL join types, Window , Pivot , Limit and Like
The GoLogica certification is widely acknowledged, enhancing the credibility of your resume and opening doors to high-level positions in leading multinational corporations globally.
At the end of this course, you will receive a course completion certificate which certifies that you have successfully completed GoLogica training in Azure Data Engineer technology.
You will get certified in Azure Data Engineer by clearing the online examination with a minimum score of 70%.
Get Certification
Azure Data Engineer Objectives
Azure Data Factory is a cloud-based total information integration carrier that enables the creation, scheduling, and orchestration of records pipelines to transport and rework records throughout diverse records shops. It supports ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes for large-scale records workflows in each cloud and on-premises environments. Azure Data Factory is widely used for automating information movement and getting ready information for analytics.
To optimize information pipelines, you could use strategies along with partitioning data, optimizing information glide sports, leveraging PolyBase for big data hundreds, and using parallel reproduction to enhance overall performance.
Azure Data Lake Storage Gen2 is an upgraded version of Gen1, presenting hierarchical namespace aid, better security features, and integration with Azure Blob Garage, making it greater scalable and efficient for large data analytics.
Data may be secured using function-based get entry to manage (RBAC), POSIX-compliant gets entry to permissions, statistics encryption at relaxation and in transit, and setting up virtual network provider endpoints.
Azure Data Factory supports a whole lot of transformation sports, together with information glide alterations, mapping facts flows, SQL-based variations, and external activities like Azure Databricks.s
Real-time statistics processing can be carried out with the usage of Azure Stream Analytics or Azure Databricks alongside Event Hubs or IoT Hub for streaming information ingestion and processing.
Best practices include structuring records in folders for logical corporations, the use of partitioning strategies, handling admission to manage with Azure AD, and optimizing garage charges with the aid of deciding on suitable file formats.
Data partitioning in Azure Synapse Analytics entails dividing large datasets into smaller, greater attainable components, which improves question overall performance and enables parallel processing.
Monitoring can be completed with the use of Azure Monitor, the ADF tracking dashboard, hobby runs, pipeline runs, and integration with Log Analytics for designated logging and diagnostics.
Azure Databricks is a fast, clean, and collaborative Apache Spark-based analytics carrier. It is used for huge statistics analytics, information engineering, gadget gaining knowledge of, and strolling information pipelines.
Azure Data Lake integrates with offerings like Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Power BI, and others, providing a continuing ecosystem for data garage, processing, and analytics.
Delta Lake is an open-supply garage layer that brings ACID transactions to huge information workloads. It enhances information reliability and helps schema enforcement and time tour skills.
Azure Data Catalog is a completely controlled cloud service that lets fact specialists discover, classify, and understand their information sources, making it less difficult to discover and use records for analytics and reporting.
Common ETL patterns encompass batch processing, real-time processing, incremental facts loading, and information differences in the usage of offerings like Azure Data Factory, Azure Synapse, and Azure Databricks.
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Azure Data Engineer rated
(5.0 / 5) based on 1 reviews.
Ganesh
I completed my Azure Data Engineer training with GoLogica. This course provides professionals with crucial experience in data storage, conversion, and protection on Microsoft Azure, as well as practical training for application development.
