deswandhi/demo_bq

Step 1: Introducing the Challenges

Script:

"Welcome to our demonstration on leveraging BigQuery and Looker Studio for advanced analytics and visualization. Meet our data analyst, Sarah. She's facing common challenges in the world of data analysis: massive datasets with complex structures, scalability concerns, and the demand for advanced analytics and visualization tools."

Technical Narrative:

• Challenges:
  • Data Complexity: Diverse data sources and structures.
  • Scalability: Difficulty in handling large volumes of data.
  • Advanced Analytics: Need for complex queries and visualization.

Step 2: Introducing BigQuery and Looker

Script:

"To overcome these hurdles, Sarah turns to BigQuery and Looker Studio. Let's dive into how these tools can revolutionize data analysis."

Technical Narrative:

• BigQuery Overview:
  • Highly scalable, serverless data warehouse.
  • Efficient handling of petabytes of data.
• Looker Overview:
  • Intuitive platform for creating interactive dashboards.
  • Drag-and-drop interface for easy visualization.

Step 3: Transition to BigQuery Demo

Script:

"Now, let's see how BigQuery tackles the challenges of large datasets and complex queries."

Technical Narrative:

1. Show Data Source:
   • Use the bigquery-public-data.samples.natality dataset.
   • Display its size, row count, and complexity.

   # SQL Script to Display Data Source Info
   SELECT
     *
   FROM
     `bigquery-public-data.samples.natality`
   LIMIT
     10

2. Complex Query Example:
   • Demonstrate a complex SQL query for data preparation and cleansing.

   # SQL Script for Complex Query
   SELECT
     source_year AS year,
     COUNT(is_male) AS total_births
   FROM
     `bigquery-public-data.samples.natality`
   WHERE
     source_year > 2000
   GROUP BY
     year
   ORDER BY
     year

3. Advanced Analytics:
   • Showcase BigQuery's ability to perform advanced analytics like aggregation and grouping.

   # SQL Script for Advanced Analytics
   SELECT
     gestation_weeks,
     AVG(weight_pounds) AS avg_weight
   FROM
     `bigquery-public-data.samples.natality`
   WHERE
     gestation_weeks IS NOT NULL
   GROUP BY
     gestation_weeks
   ORDER BY
     gestation_weeks

Step 4: Transition to Looker Studio Demo

Script:

"Now, let's explore Looker Studio and see how it complements BigQuery for creating interactive and insightful dashboards."

Technical Narrative:

1. Introduce Looker Studio:
   • Access Looker Studio's user-friendly interface.
   • Mention drag-and-drop functionality and dashboard creation.
2. Create Interactive Dashboard:
   • Demonstrate creating a sales trends dashboard using the bigquery-public-data.samples.natality dataset.
   • Drag and drop elements like charts and graphs.

   # SQL Script for Looker Dashboard
   SELECT
     source_year AS year,
     COUNT(is_male) AS total_births
   FROM
     `bigquery-public-data.samples.natality`
   WHERE
     source_year > 2000
   GROUP BY
     year
   ORDER BY
     year

3. Customization and Deployment:
   • Customize dashboard layout, colors, and widgets.
   • Deploy the dashboard for sharing with teams.

Step 5: Seamless Integration and Workflow Benefits

Script:

"BigQuery and Looker Studio seamlessly integrate, empowering data analysts like Sarah to streamline their workflows and derive actionable insights."

Technical Narrative:

1. Integration Overview:
   • Seamless connection between BigQuery datasets and Looker's dashboards.
   • Instant updates in Looker with changes in BigQuery data.
2. Workflow Benefits:
   • Easy transition between data exploration in BigQuery and visualization in Looker.
   • Time-saving in data preparation, analysis, and sharing insights.

SQL Queries:

Here are the modified SQL queries using the bigquery-public-data.samples.natality dataset:

1. Data Source Information:

   # SQL Script to Display Data Source Info
   SELECT
     *
   FROM
     `bigquery-public-data.samples.natality`
   LIMIT
     10

2. Complex Query Example (Sales Trends):

   # SQL Script for Complex Query
   SELECT
     source_year AS year,
     COUNT(is_male) AS total_births
   FROM
     `bigquery-public-data.samples.natality`
   WHERE
     source_year > 2000
   GROUP BY
     year
   ORDER BY
     year

3. Advanced Analytics (Average Birth Weight by Gestation Weeks):

   # SQL Script for Advanced Analytics
   SELECT
     gestation_weeks,
     AVG(weight_pounds) AS avg_weight
   FROM
     `bigquery-public-data.samples.natality`
   WHERE
     gestation_weeks IS NOT NULL
   GROUP BY
     gestation_weeks
   ORDER BY
     gestation_weeks

Summary:

In this part, we introduced the challenges faced by data analysts like Sarah, such as dealing with massive datasets, scalability issues, and the need for advanced analytics tools. We then introduced BigQuery and Looker as solutions to these challenges. The technical script includes SQL queries using the bigquery-public-data.samples.natality dataset to showcase BigQuery's capabilities in handling large datasets and advanced analytics. Next, we transition to Looker Studio to demonstrate its intuitive dashboard creation and visualization features. Finally, we highlight the seamless integration between BigQuery and Looker, emphasizing the workflow benefits for data analysts.