Visualization types in Elasticsearch - Mini Project: Build & Apply

Create Basic Visualizations in Elasticsearch

📖 Scenario: You are working as a data analyst for an online store. You want to create simple visualizations to understand sales data better using Elasticsearch queries.

🎯 Goal: Build Elasticsearch queries step-by-step to create data for three common visualization types: bar chart, pie chart, and line chart.

📋 What You'll Learn

Create an Elasticsearch index called sales_data with sample sales documents

Define a date range filter variable called date_range

Write an aggregation query for a bar chart showing total sales per product category

Write an aggregation query for a pie chart showing sales distribution by region

Write an aggregation query for a line chart showing daily sales over time

💡 Why This Matters

🌍 Real World

Creating visualizations from sales data helps businesses understand trends and make decisions.

💼 Career

Data analysts and developers use Elasticsearch aggregations to prepare data for dashboards and reports.

1

Create the sales_data index with sample documents

Create an Elasticsearch index called sales_data with these exact documents: one with product_category as "Books", region as "North", sales as 100, and date as "2024-06-01"; another with product_category as "Electronics", region as "South", sales as 200, and date as "2024-06-02".

Elasticsearch

# Create the sales_data index with the specified documents
# Your code here

Need a hint?

Use the PUT method to add documents to the sales_data index with the exact fields and values.

2

Define a date_range filter for June 2024

Create a variable called date_range that filters documents with date between "2024-06-01" and "2024-06-30".

Elasticsearch

PUT /sales_data/_doc/1
{
  "product_category": "Books",
  "region": "North",
  "sales": 100,
  "date": "2024-06-01"
}

PUT /sales_data/_doc/2
{
  "product_category": "Electronics",
  "region": "South",
  "sales": 200,
  "date": "2024-06-02"
}

# Define the date_range filter variable
# Your code here

Need a hint?

Use a range query on the date field with gte and lte for the start and end dates.

3

Write a bar chart aggregation query for total sales per product category

Write an Elasticsearch aggregation query called sales_per_category that sums sales grouped by product_category, applying the date_range filter.

Elasticsearch

PUT /sales_data/_doc/1
{
  "product_category": "Books",
  "region": "North",
  "sales": 100,
  "date": "2024-06-01"
}

PUT /sales_data/_doc/2
{
  "product_category": "Electronics",
  "region": "South",
  "sales": 200,
  "date": "2024-06-02"
}

"date_range": {
  "range": {
    "date": {
      "gte": "2024-06-01",
      "lte": "2024-06-30"
    }
  }
}

# Add the sales_per_category aggregation query
# Your code here

Need a hint?

Use a filter aggregation with the date_range, then a terms aggregation on product_category.keyword, and inside it a sum aggregation on sales.

4

Add pie chart and line chart aggregation queries

Add two more aggregation queries: sales_by_region for a pie chart that sums sales grouped by region, and daily_sales for a line chart that sums sales grouped by date, both applying the date_range filter.

Elasticsearch

PUT /sales_data/_doc/1
{
  "product_category": "Books",
  "region": "North",
  "sales": 100,
  "date": "2024-06-01"
}

PUT /sales_data/_doc/2
{
  "product_category": "Electronics",
  "region": "South",
  "sales": 200,
  "date": "2024-06-02"
}

"date_range": {
  "range": {
    "date": {
      "gte": "2024-06-01",
      "lte": "2024-06-30"
    }
  }
}

"sales_per_category": {
  "filter": {
    "range": {
      "date": {
        "gte": "2024-06-01",
        "lte": "2024-06-30"
      }
    }
  },
  "aggs": {
    "by_category": {
      "terms": {
        "field": "product_category.keyword"
      },
      "aggs": {
        "total_sales": {
          "sum": {
            "field": "sales"
          }
        }
      }
    }
  }
}

# Add sales_by_region and daily_sales aggregation queries
# Your code here

Need a hint?

Use the same filter with date_range for both queries. For sales_by_region, use a terms aggregation on region.keyword. For daily_sales, use a date_histogram aggregation on date with calendar_interval set to "day".