Course Overview

About Azure Databricks Online Training in Mumbai

In today's data-driven world, organizations are increasingly looking for efficient ways to process and analyze vast amounts of data. Azure Databricks, a powerful analytics platform, offers a seamless integration of big data processing with the capabilities of Apache Spark. This platform empowers data engineers and data scientists to collaborate in real-time and derive actionable insights from their data. With the rising demand for Azure Databricks expertise, online training in Mumbai is becoming a sought-after option for professionals looking to enhance their skills.

What is Azure Databricks?

Azure Databricks is an Apache Spark-based analytics platform designed to simplify the process of building big data and AI solutions. It offers a collaborative workspace for data engineers and data scientists, enabling them to work together seamlessly on data processing, machine learning, and analytics projects. Azure Databricks combines the scalability of cloud infrastructure with the powerful capabilities of Spark, making it an ideal choice for businesses of all sizes.

Key Features of Azure Databricks

1. Collaborative Workspace: Azure Databricks provides an interactive workspace where teams can collaborate on notebooks, sharing insights and code in real time. This fosters teamwork and accelerates project delivery.

2. Auto-scaling Clusters: The platform automatically manages clusters based on workload demands, ensuring optimal performance while minimizing costs. This allows users to focus on their analysis rather than infrastructure management.

3. Integrated Data Processing: With Azure Databricks, users can easily connect to various data sources, including Azure Blob Storage, Azure Data Lake, and SQL databases. This integration facilitates seamless data ingestion and processing.

4. Machine Learning Support: Azure Databricks offers built-in support for machine learning, allowing data scientists to develop and deploy models efficiently. The platform includes libraries like MLlib for scalable machine learning.

5. Advanced Analytics: Users can leverage powerful analytics capabilities, including streaming analytics and batch processing, to derive insights from both real-time and historical data.

Why Choose Online Training for Azure Databricks in Mumbai?

Online training provides several advantages for professionals looking to enhance their Azure Databricks skills:

1. Flexible Learning: Online training allows participants to learn at their own pace and schedule, making it easier to balance work and study commitments.

2. Access to Expert Trainers: Online courses often feature experienced instructors who provide valuable insights and real-world examples, enhancing the learning experience.

3. Hands-on Practice: Many online training programs include hands-on labs and projects, allowing participants to apply their knowledge in practical scenarios.

4. Comprehensive Curriculum: A well-structured online training program covers various aspects of Azure Databricks, from basic concepts to advanced analytics techniques.

5. Networking Opportunities: Online training platforms often include forums and discussion groups where participants can connect with peers, share knowledge, and collaborate on projects.

Course Content for Azure Databricks Online Training

An effective Azure Databricks online training program typically covers the following topics:

1. Introduction to Azure Databricks: Understanding the fundamentals of Azure Databricks and its role in big data analytics.

2. Setting Up the Environment: Learning how to create and configure Azure Databricks workspaces and clusters.

3. Working with Notebooks: Exploring the interactive notebook interface, including creating, editing, and sharing notebooks.

4. Data Ingestion and Preparation: Techniques for importing and preparing data from various sources, including data cleaning and transformation.

5. Using Apache Spark: Gaining proficiency in using Spark SQL, DataFrames, and RDDs for data processing and analysis.

6. Machine Learning with Azure Databricks: Implementing machine learning models using built-in libraries and tools.

7. Data Visualization: Creating visualizations and dashboards to present insights derived from data analysis.

8. Performance Optimization: Strategies for optimizing the performance of Spark jobs and data pipelines.

9. Integrating with Other Azure Services: Learning how to connect Azure Databricks with other Azure services for enhanced data workflows.

Career Opportunities with Azure Databricks Skills

With the growing adoption of Azure Databricks in various industries, skilled professionals are in high demand. Completing an Azure Databricks online training program can open up a variety of career opportunities, including:

• Data Engineer: Responsible for designing and implementing data pipelines and workflows.
• Data Scientist: Focused on developing predictive models and conducting advanced analytics.
• Business Intelligence Analyst: Analyzing data to provide actionable insights for business decision-making.
• Machine Learning Engineer: Specializing in building and deploying machine learning models at scale.

Why BESTWAY Technologies for Azure Databricks Training?

BESTWAY Technologies stands out as a leading provider of Azure Databricks online training in Mumbai. Here are some reasons to choose us:

• Experienced Trainers: Our instructors have extensive experience in big data and analytics, ensuring high-quality training.
• Hands-on Learning: We emphasize practical, hands-on training to equip participants with real-world skills.
• Comprehensive Support: Our team provides continuous support, from initial inquiries to post-training job placement assistance.

Course Curriculum

Azure Databricks Course Syllabus :

Class 1:
 ADB_Class 1_Apache Spark Introduction

Class 2:
 ADB_Class 2_Apache Spark Architecture

Class 3:
 ADB_Class 3_Apache Spark_DriverNode Internals

Class 4:
 ADB_Class 4_Apache Spark_Worker Node Internals

Class 5:
 ADB_Class 5_Azure Databricks Introduction

Class 6:
 ADB_Class 6_Create Azure Databricks Workspace

Class 7:
 ADB_Class 7_Create Azure Databricks All-Purpose Cluster

Class 8:
 ADB_Class 8_Databricks All-Purpose Cluster_Databricls Pools_Spot Instances

Class 9:
 ADB_Class 9_RDD(Reslient Distributed Dataset)_Introduction

Class 10:
 ADB_Class 10_RDD_Types of Operations_Types of Transformations

Class 11:
 ADB_Class 11_Introduction To Databricks Utilities(dbutils Module)

Class 12:
 ADB_Class 12_Databricks File System Utility_Commands

Class 13:
 ADB_Class 13_Creation of Structured API_DataFrame

Class 14:
 ADB_Class 14_Creation of DataFrame_Schemas

Class 15:
 ADB_Class 15_Data Ingestion_Azure BlobStorage

Class 16:
 ADB_Class 16_Data Ingestion_Mount Azure BlobStorage To DBFS

Class 17:
 ADB_Class 17_Data Ingestion_Direct Access to Azure BlobStorage Using Account Key

Class 18:
 ADB_Class 18_Azure Blob Storage_ABFS_Account Access Keys_Direct Access

Class 19:
 ADB_Class 19_ADLS Gen2_ABFS_OAuth2.0 with Azure Service Principal

Class 20:
 ADB_Class 20_ADLS Gen2_ABFS_Access Key_SAS Token

Class 21:
 ADB_Class 21_Azure SQL Database_Data Ingestion

Class 22:
 ADB_Class 22_Reading CSV Files_User Defined Schema

Class 23:
 ADB_Class 23_Reading_SingleLine_MultiLine_Simple JSON Files

Class 24:
 ADB_Class 24_Reading_MultiLine_Complex JSON Files

Class 25:
 ADB_Class 25_PySpark_Explode Function_Array_Map

Class 26:
 ADB_Class 26_Reading_Writing_XML File formats

Class 27:
 ADB_Class 27_Reading_Writing_Excel File formats

Class 28:
 ADB_Class 28_Reading_Writing Data _Snowflake

Class 29:
 ADB_Class 29_Reading_Writing Data _Azure Synapse Dedicated SQL Pool

Class 30:
 ADB_Class 30_Batch ETL Processing

Class 31:
 ADB_Class 31_Batch ETL Processing

Class 32:
 ADB_Class 32_Batch ETL Processing_Replace Nulls with Literals

Class 33:
 ADB_Class 33_Batch ETL Processing_GroupBy_Aggregation Processing

Class 34:
 ADB_Class 34_Batch ETL Processing_GroupBy_Aggregation Processing

Class 35:
 ADB_Class 35_Batch ETL Processing_PySpark_Join Types

Class 36:
 ADB_Class 36_Batch ETL Processing_PySpark_Union_UnionAll

Class 37:
 ADB_Class 37_Batch ETL Processing_PySpark_Distinct_DropDuplicates Methods

Class 38:
 ADB_Class 38_Spark Structured Streaming API

Class 39:
 ADB_Class 39_Data Lake Introduction

Class 40:
 ADB_Class 40_Delta Lake Introduction

Class 41:
 ADB_Class 41_Delta Lake Features_BigData File Formats

Class 42:
 ADB_Class 42_Delta Lake Creation

Class 43:
 ADB_Class 43_Create Delta Table

Class 44:
 ADB_Class 44_Create Delta Table__Spark SQL

Class 45:
 ADB_Class 45_Delta Lake Table_PySpark_DataFrame

Class 46:
 ADB_Class 46_Delta Lake_OPTIMIZE and ZORDER_Performance Optimization

Class 47:
 ADB_Class 47_Delta Lake_Delta Cache

Class 48:
 ADB_Class 48_Delta Table Instance

Class 49:
 ADB_Class 49_Create Database_SparkSQL

Faq’s

Reviews

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Locations

• Mumbai