Description
Introduction:
This course is designed for data analysts looking to enhance their data querying, reporting, and visualization skills using Databricks. By leveraging SQL and Databricks’ powerful data processing capabilities, participants will learn how to work with large datasets, perform advanced data analysis, and create compelling data visualizations. The course will cover the use of SQL in Databricks for querying data, joining tables, performing aggregations, and creating dashboards to visualize data insights. This hands-on course also introduces integration with popular BI tools for seamless reporting and decision-making.
Prerequisites:
- Familiarity with SQL (Structured Query Language) basics.
- Basic understanding of data analysis concepts.
- No prior experience with Databricks or Apache Spark is required, but a general knowledge of cloud computing and data manipulation is helpful.
Table of Content:
- Introduction to Databricks for Data Analysts
1.1 Overview of Databricks as a platform for data analysis
1.2 Key features of Databricks for data analysts: SQL, notebooks, and visualization tools
1.3 How Databricks handles large-scale data analytics
1.4 Introduction to Apache Spark for data processing - Getting Started with Databricks Workspace
2.1 Creating and managing Databricks notebooks
2.2 Uploading and importing datasets
2.3 Exploring the Databricks workspace interface
2.4 Collaboration features for team-based analytics - Introduction to SQL in Databricks
3.1 Overview of SQL language support in Databricks
3.2 Running SQL queries in Databricks notebooks
3.3 Connecting to external data sources with SQL
3.4 Best practices for writing SQL queries in Databricks - Querying Large Datasets with Spark SQL
4.1 Introduction to Spark SQL and its benefits
4.2 Using Spark DataFrames for querying data
4.3 Writing and executing SQL queries on large-scale datasets
4.4 Optimizing SQL queries for performance and scalability - Advanced SQL Techniques for Data Analysts
5.1 Advanced filtering, sorting, and grouping operations
5.2 Working with complex joins, subqueries, and common table expressions (CTEs)
5.3 Aggregations and window functions for advanced analysis
5.4 Handling null values and missing data in SQL queries - Data Exploration and Transformation with SQL
6.1 Exploring and summarizing large datasets with SQL
6.2 Data transformation techniques: Pivot, unpivot, and data reshaping
6.3 Creating calculated columns and derived metrics
6.4 Case study: Data preparation for business reporting - Building Interactive Dashboards in Databricks
7.1 Introduction to Databricks’ visualization capabilities
7.2 Creating and configuring data visualizations within notebooks
7.3 Building interactive dashboards to present data insights
7.4 Sharing and exporting dashboards for business stakeholders - Data Visualization Best Practices
8.1 Choosing the right visualization for different types of data
8.2 Effective storytelling with data: Visualizing trends, outliers, and distributions
8.3 Customizing visualizations: Formatting, labels, and tooltips
8.4 Case study: Creating a comprehensive sales dashboard - Integration with Business Intelligence (BI) Tools
9.1 Connecting Databricks with BI tools like Power BI, Tableau, and Looker
9.2 Building seamless reporting pipelines with external BI platforms
9.3 Live querying Databricks data with BI tools
9.4 Automating report generation and sharing across teams - Working with Delta Lake for Data Analysts
10.1 Introduction to Delta Lake and its role in analytics
10.2 Performing SQL queries on Delta Lake tables
10.3 Data versioning and time travel for historical analysis
10.4 Ensuring data reliability with ACID transactions in Delta Lake - Optimizing Query Performance in Databricks
11.1 Techniques for improving SQL query performance
11.2 Understanding Spark’s Catalyst Optimizer for SQL optimization
11.3 Using indexes, partitions, and caching to speed up queries
11.4 Query profiling and troubleshooting performance bottlenecks - Real-Time Data Analytics with Structured Streaming
12.1 Introduction to real-time data processing with Structured Streaming
12.2 Writing SQL queries for real-time data analytics
12.3 Use cases for real-time reporting and dashboards
12.4 Integrating streaming data into business workflows - Time Series Analysis and Trend Detection
13.1 Working with time series data in Databricks
13.2 Using SQL to perform time-based calculations (rolling averages, year-over-year comparisons)
13.3 Building time series visualizations to identify trends
13.4 Case study: Time series analysis for business forecasting - Collaborative Analytics in Databricks
14.1 Sharing notebooks and dashboards with team members
14.2 Using Git for version control in Databricks
14.3 Collaborative data analysis workflows: Tips for effective teamwork
14.4 Managing permissions and data access control for team members - Final Project: Building a Data-Driven Decision-Making Dashboard
15.1 Designing and developing an interactive dashboard
15.2 Incorporating SQL queries and visualizations to present business insights
15.3 Applying advanced data analysis techniques to real-world datasets
15.4 Presenting and explaining data insights to stakeholders - Conclusion and Next Steps
16.1 Recap of course learnings
16.2 Additional resources for further learning in SQL, Databricks, and data visualization
16.3 Certification paths for data analysts
16.4 Future developments in Databricks for data analysis
In conclusion, this course equips data analysts with the essential skills to leverage Databricks for data analysis, visualization, and collaboration. By mastering these techniques, participants can drive data-driven decision-making and enhance their analytical capabilities.
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