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PySpark with Azure DataBricks Training
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About Course
🌐 What Is PySpark with Azure Databricks?
PySpark with Azure Databricks is a powerful combination of Apache Spark’s big data processing capabilities and Microsoft Azure’s cloud-based analytics platform. It enables organizations to process, analyze, and visualize large-scale datasets efficiently using Python (PySpark) within the collaborative and scalable Azure Databricks environment.
This integrated solution allows data engineers, analysts, and scientists to build data pipelines, perform ETL operations, and develop machine learning models with ease. PySpark’s distributed computing framework, coupled with Databricks’ interactive workspace, provides unparalleled performance and flexibility for modern data engineering and analytics workloads.
Core Capabilities Include:
- Big Data Processing: Handle large datasets efficiently using Apache Spark.
- Data Integration: Connect seamlessly to Azure Data Lake, Blob Storage, SQL, and Power BI.
- Collaborative Workspace: Develop, test, and deploy code in Databricks notebooks.
- ETL & Data Transformation: Build automated data pipelines and clean data at scale.
- Machine Learning & AI: Implement ML models using PySpark MLlib within Databricks.
- Scalability & Cloud Integration: Leverage Azure’s managed clusters for optimized performance.
📊 Course Features Typically Included
Top online training platforms (like Viswa Online Trainings, Udemy, Coursera, and Microsoft Learn) offer the following features for PySpark with Azure Databricks Training:
- ✅ Live instructor-led sessions and recorded video tutorials
- ✅ Hands-on labs for Spark data processing and Azure Databricks workflows
- ✅ Real-world projects for ETL, analytics, and machine learning use cases
- ✅ Certification and interview preparation guidance
- ✅ Lifetime access to Databricks notebooks, materials, and recorded sessions
- ✅ Step-by-step guidance for integrating PySpark with Azure Data Lake, Power BI, and Synapse Analytics
🎓 Key Learning Outcomes
After completing the PySpark with Azure Databricks Training, learners will be able to:
- Understand PySpark architecture, RDDs, DataFrames, and Spark SQL
- Build and manage data pipelines and ETL workflows using Databricks
- Perform data cleaning, transformation, and aggregation in PySpark
- Integrate Databricks with Azure Data Lake Storage, Synapse, and Power BI
- Implement machine learning models using Spark MLlib
- Optimize cluster performance and job execution in Azure Databricks
- Apply DevOps practices for deploying Databricks notebooks and workflows
Roles Prepared For:
- Data Engineer
- Big Data Developer
- Azure Data Engineer
- Spark Developer
- Machine Learning Engineer (using PySpark)
📍 Bonus: Certification Tracks
Learners can pursue industry-recognized certifications such as:
- 🏅 Microsoft Certified: Azure Data Engineer Associate
- 🏅 Databricks Certified Data Engineer Professional
- 🏅 Apache Spark Developer (HDPCD – Spark)
- 🏅 Viswa Online Trainings – PySpark with Azure Databricks Professional CertificationProfessional Certification
PySpark with Azure DataBricks Training Course Syllabus
Spark and Azure DataBricks Architecture
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Mounting ADLS with Azure Data Bricks
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RDDs
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DataFrames
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Parallelize () and repartition
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Struct Type and Struct Field
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Select, With column and with column renamed
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Collect
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UDF
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Join()
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Dataframe Operations
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Reading different Files
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Pyspark SQL Functions
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Mounting to Databricks
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Cluster Types and setting up the clusters
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Pyspark Built-in Functions
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Cosmos DB connectivity with Azure Databricks
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PySpark with Azure DataBricks Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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- Learn as a full day schedule with discussions, exercises.
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PySpark with Azure DataBricks Online Training FAQ'S
What is PySpark in the context of Azure Databricks?
PySpark is the Python library for Apache Spark, and when used in Azure Databricks, it enables distributed big data processing and machine learning in the cloud.
Why is Azure Databricks a preferred platform for PySpark?
Azure Databricks provides a scalable, collaborative workspace with optimized clusters, making PySpark development and deployment efficient for real-time analytics.
What are PySpark DataFrames and their benefits?
DataFrames are high-level abstractions for structured data that support SQL-like operations, enabling easier data manipulation and better performance.
How does PySpark process real-time streaming data?
Using Structured Streaming, PySpark ingests and processes continuous data streams, useful for IoT, financial, and event-driven applications.
How does Azure Data Lake integrate with Azure Databricks and PySpark?
Azure Data Lake provides scalable storage, and Databricks with PySpark uses connectors or mounts to process both structured and unstructured datasets efficiently.
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