Implement a Data Analytics Solution with Azure Databricks
- Duration: 1 day
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The Implement a Data Analytics Solution with Azure Databricks Course delivers practical training for professionals responsible for building analytics pipelines and insights platforms on Azure. Participants gain a structured understanding of how Azure Databricks enables large-scale data processing, analytics, and collaboration using Apache Spark. The course emphasizes real-world implementation patterns, enabling learners to transform raw data into analytics-ready datasets that support reporting, dashboards, and advanced analytics use cases.
Course Details
- Build practical skills in implementing analytics solutions with Azure Databricks.
- Understand data ingestion, transformation, and analytics workflows.
- Learn to use Apache Spark and Databricks notebooks effectively.
- Apply Delta Lake concepts for reliable and scalable analytics.
- Improve performance through optimization and best practices.
- Establish a strong foundation for advanced analytics and data engineering roles.
- Ideal for data analysts, data engineers, and analytics professionals.
- Suitable for teams building cloud-based analytics platforms.
- Basic knowledge of data concepts and SQL or Python is beneficial.
- Prior exposure to cloud analytics platforms is helpful but not mandatory.
- Design data analytics solutions using Azure Databricks.
- Ingest and transform data using Spark-based processing.
- Build analytics-ready datasets using Delta Lake concepts.
- Execute analytical queries and support downstream reporting.
- Optimize performance and manage analytics workloads.
- Operate and maintain Databricks-based analytics environments.
- Azure Databricks Overview: Analytics architecture and use cases.
- Data Ingestion: Loading data from multiple sources.
- Data Transformation: Spark DataFrames and processing patterns.
- Delta Lake Concepts: Reliability, performance, and scalability.
- Analytics and Querying: Supporting insights and reporting workloads.
- Performance Optimization: Tuning and best practices.
- Operations and Monitoring: Managing and maintaining analytics solutions.
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