Azure Databricks 

 
  • Total hours and mode: 48 hours & VILT
  • Hands-on available? – Yes
  • Technologies taught: NA
  •  Assessments or certification available? – Assessment available
  Get Enterprise Training on Azure Databricks for Your Teams 
Rated x/y by our corporate
learners 

12 hours

Hours of Live Training

12 hours

Hours of Mentoring

36 hours

Hours of Practice

23 hours

Hours or Number of Assessments

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Organizational Benefits of Training on Azure Databricks

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Enhances efficiency in data processing and analysis workflows.

Optimize costs by learning resource utilization strategies.

Integrated Ecosystem

Foster team collaboration for streamlined processes and effective tool utilization

Benefit 2

Benefit 4

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Course Contents:

Module 1: Explore compute and storage options for data engineering workloads

  1. Introduction to Azure Synapse Analytics
  2. Describe Azure Databricks
  3. Introduction to Azure Data Lake storage
  4. Describe Delta Lake architecture
  5. Work with data streams by using Azure Stream Analytics
  1. Setup Azure subscription and create LABVM
  2. Combine streaming and batch processing with a single pipeline
  3. Organize the data lake into levels of file transformation
  4. Index data lake storage for query and workload acceleration
  1. Explore Azure Synapse serverless SQL pools capabilities
  2. Query data in the lake using Azure Synapse serverless SQL pools
  3. Create metadata objects in Azure Synapse serverless SQL pools
  4. Secure data and manage users in Azure Synapse serverless SQL pools
  1. Query Parquet data with serverless SQL pools
  2. Create external tables for Parquet and CSV files
  3. Create views with serverless SQL pools
  4. Secure access to data in a data lake when using serverless SQL pools
  5. Configure data lake security using Role-Based Access Control (RBAC) and Access Control List
  1. Describe Azure Databricks
  2. Read and write data in Azure Databricks
  3. Work with DataFrames in Azure Databricks
  4. Work with DataFrames advanced methods in Azure Databricks
  1. Use DataFrames in Azure Databricks to explore and filter data
  2. Cache a DataFrame for faster subsequent queries
  3. Remove duplicate data
  4. Manipulate date/time values
  5. Remove and rename DataFrame columns
  6. Aggregate data stored in a DataFrame
  7. After completing this module, students will be able to:
    • I. Describe Azure Databricks
      II. Read and write data in Azure Databricks
      III. Work with DataFrames in Azure Databricks
      IV. Work with DataFrames advanced methods in Azure Databricks
  1. Understand big data engineering with Apache Spark in Azure Synapse Analytics
  2. Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  3. Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  4. Integrate SQL and Apache Spark pools in Azure Synapse Analytics
  1. Perform Data Exploration in Synapse Studio
  2. Ingest data with Spark notebooks in Azure Synapse Analytics
  3. Transform data with DataFrames in Spark pools in Azure Synapse Analytics
  4. Integrate SQL and Spark pools in Azure Synapse Analytics
  5. After completing this module, students will be able to:
    • I. Describe big data engineering with Apache Spark in Azure Synapse Analytics
      II. Ingest data with Apache Spark notebooks in Azure Synapse Analytics
      III. Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
      IV. Integrate SQL and Apache Spark pools in Azure Synapse Analytics
  1. Use data loading best practices in Azure Synapse Analytics
  2. Petabyte-scale ingestion with Azure Data Factory
  1. Perform petabyte-scale ingestion with Azure Synapse Pipelines
  2. Import data with PolyBase and COPY using T-SQL
  3. Use data loading best practices in Azure Synapse Analytics
  4. After completing this module, students will be able to:
    • I. Use data loading best practices in Azure Synapse Analytics
      II. Petabyte-scale ingestion with Azure Data Factory
  1. Data integration with Azure Data Factory or Azure Synapse Pipelines
  2. Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
  1. Execute code-free transformations at scale with Azure Synapse Pipelines
  2. Create data pipeline to import poorly formatted CSV files
  3. Create Mapping Data Flows
  4. After completing this module, students will be able to:
    • I. Perform data integration with Azure Data Factory
      II. Perform code-free transformation at scale with Azure Data Factory
  1. Orchestrate data movement and transformation in Azure Data Factory
  1. Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
  2. After completing this module, students will be able to:
    • I. Orchestrate data movement and transformation in Azure Synapse Pipelines
  1. Secure a data warehouse in Azure Synapse Analytics
  2. Configure and manage secrets in Azure Key Vault
  3. Implement compliance controls for sensitive data
  1. Secure Azure Synapse Analytics supporting infrastructure
  2. Secure the Azure Synapse Analytics workspace and managed services
  3. Secure Azure Synapse Analytics workspace data
  4. After completing this module, students will be able to:
      I. Secure a data warehouse in Azure Synapse Analytics
    • II. Configure and manage secrets in Azure Key Vault
      III. Implement compliance controls for sensitive data
  1. Design hybrid transactional and analytical processing using Azure Synapse Analytics
  2. Configure Azure Synapse Link with Azure Cosmos DB
  3. Query Azure Cosmos DB with Apache Spark pools
  4. Query Azure Cosmos DB with serverless SQL pools
  1. Configure Azure Synapse Link with Azure Cosmos DB
  2. Query Azure Cosmos DB with Apache Spark for Synapse Analytics
  3. Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics
  4. After completing this module, students will be able to:
      I. Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics
      II. Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics
    • III. Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
    • IV. Integrate SQL and Apache Spark pools in Azure Synapse Analytics
  1. Enable reliable messaging for Big Data applications using Azure Event Hubs
  2. Work with data streams by using Azure Stream Analytics
  3. Ingest data streams with Azure Stream Analytics
  1. Enable reliable messaging for Big Data applications using Azure Event Hubs
  2. Work with data streams by using Azure Stream Analytics
  3. Ingest data streams with Azure Stream Analytics
  1. Use Stream Analytics to process real-time data from Event Hubs
  2. Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics
  3. Scale the Azure Stream Analytics job to increase throughput through partitioning
  4. Repartition the stream input to optimize parallelization
  5. After completing this module, students will be able to:
    • I. Enable reliable messaging for Big Data applications using Azure Event Hubs
      II. Work with data streams by using Azure Stream Analytics
      III. Ingest data streams with Azure Stream Analytics
  1. Process streaming data with Azure Databricks structured streaming
  1. Explore key features and uses of Structured Streaming
  2. Stream data from a file and write it out to a distributed file system
  3. Use sliding windows to aggregate over chunks of data rather than all data
  4. Apply watermarking to remove stale data
  5. Connect to Event Hubs read and write streams
  6. After completing this module, students will be able to:
    • I. Process streaming data with Azure Databricks structured streaming
  1. The case studies will be shared with the participants for practising the real-time implementation of Azure Data Platform services.
  2. Case Study-1 Azure Synapse Analytics
  3. Case Study-2 Innovate and modernize apps with Data and AI

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Trainer Details 

X years experience
X batches trained
Conducted trainings at A, B, C

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What our learners have to say about this training 

Arun KK is a seasoned professional with over two decades of experience in the IT industry. Throughout his illustrious career, Arun has adeptly navigated diverse roles, showcasing his versatility and expertise. With a rich background in customer-facing responsibilities, as well as in delivery and operations, Arun has consistently demonstrated

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