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1
Introduction
2
History of Databricks
3
Spark
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Machine Learning
5
Machine Learning Monitoring
6
Analytics Tools
7
Managed Service
8
Processing
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GitHub Integration
10
Managing Databricks
11
Pricing
12
Machine Learning Process
13
Databricks
14
Creating a Databricks resource
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Databricks workspace
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Creating a new cluster
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Analyzing data
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Using Databricks
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Databricks Job
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Databricks Notifications
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Structured Streaming
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Deep Learning
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Jobs
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Recap
25
Questions
Description:
Explore how to develop scalable data solutions using Azure Databricks in this comprehensive conference talk. Learn about connecting to various Azure data sources, processing large datasets with Apache Spark, and creating machine learning solutions. Discover the collaborative workspace features, integration with Power BI for visualization, and performance optimization techniques. Follow step-by-step demonstrations covering Databricks implementation, including resource creation, workspace navigation, cluster management, data analysis, job scheduling, and structured streaming. Gain insights into Databricks' history, pricing, and integration with tools like GitHub. Understand the benefits of using Databricks for processing data lakes, implementing machine learning workflows, and leveraging deep learning capabilities. By the end of this talk, acquire the knowledge needed to effectively utilize Azure Databricks for developing scalable and efficient data solutions.

Using Azure Databricks to Develop Scalable Data Solutions

PASS Data Community Summit
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