Why Kibana visualizes Elasticsearch data - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an index in Elasticsearch.

Elasticsearch

PUT /[1]

Drag options to blanks, or click blank then click option'

Adata-store

Bsearch-data

Clogs

Dmy-index

Attempts:

3 left

💡 Hint

Common Mistakes

Using uppercase letters in the index name.

Forgetting to specify the index name.

The PUT command creates an index named my-index in Elasticsearch.

2fill in blank

medium

Complete the code to add a document to the Elasticsearch index.

Elasticsearch

POST /my-index/_doc/[1] 
{
  "user": "alice",
  "message": "Hello Kibana!"
}

Drag options to blanks, or click blank then click option'

Adoc1

B1

Cuser1

Dmsg1

Attempts:

3 left

💡 Hint

Common Mistakes

Omitting the document ID.

Using spaces or special characters in the ID.

The document ID 1 uniquely identifies the document in the index.

3fill in blank

hard

Fix the error in the query to retrieve all documents from the index.

Elasticsearch

GET /my-index/_search
{
  "query": {
    [1]: { "match_all": {} }
  }
}

Drag options to blanks, or click blank then click option'

Amatch_all

Bmatch

Cquery

Dterm

Attempts:

3 left

💡 Hint

Common Mistakes

Using query, which is not a valid query type.

Using match which expects a field.

Inside the query object, match_all: {} retrieves all documents from the index.

4fill in blank

hard

Fill both blanks to create a Kibana visualization that shows the count of documents per user.

Elasticsearch

{
  "aggs": {
    "users": {
      [1]: {
        "field": "[2]"
      }
    }
  }
}

Drag options to blanks, or click blank then click option'

Aterms

Bcount

Cuser

Dmessage

Attempts:

3 left

💡 Hint

Common Mistakes

Using count as aggregation type which is invalid here.

Using message field instead of user.

The terms aggregation groups documents by the user field to count them.

5fill in blank

hard

Fill all three blanks to filter documents where the message contains 'Kibana' and visualize the count per user.

Elasticsearch

{
  "query": {
    "match": {
      "[1]": "Kibana"
    }
  },
  "aggs": {
    "users": {
      "[2]": {
        "field": "[3]"
      }
    }
  }
}

Drag options to blanks, or click blank then click option'

Amessage

Bterms

Cuser

Dmatch_all

Attempts:

3 left

💡 Hint

Common Mistakes

Using match_all in the query instead of match.

Mixing up the aggregation type or field names.

The query matches the message field for 'Kibana'. The aggregation uses terms on the user field to count documents per user.

Practice

1. Why does Kibana visualize data stored in Elasticsearch?

easy

A. To help users easily understand and analyze data through charts and dashboards

B. To store data more efficiently than Elasticsearch

C. To replace Elasticsearch as a database

D. To write complex code for data processing

Solution

1. Step 1: Understand Kibana's role

   Kibana is designed to create visual representations like charts and dashboards from Elasticsearch data.

2. Step 2: Identify the purpose of visualization

   Visualization helps users quickly find insights and monitor data without needing to write code.

3. Final Answer:

   To help users easily understand and analyze data through charts and dashboards -> Option A

4. Quick Check:

   Kibana visualizes data = Easy analysis [OK]

Hint: Kibana = Visualize Elasticsearch data for easy insights [OK]

Common Mistakes:

• Thinking Kibana stores data instead of visualizing it
• Confusing Kibana with a database
• Assuming Kibana requires coding for visuals

2. Which of the following is the correct way to create a visualization in Kibana?

easy

A. Use the Kibana interface to select data and choose visualization types without coding

B. Write SQL queries directly in Kibana to generate charts

C. Manually code HTML and CSS to display Elasticsearch data

D. Export data from Elasticsearch and use external software only

Solution

1. Step 1: Review Kibana's user interface

   Kibana provides a user-friendly interface to create visualizations by selecting data and chart types without coding.

2. Step 2: Eliminate incorrect options

   Options B and C require coding, which Kibana does not need for visualization. Export data from Elasticsearch and use external software only is external to Kibana.

3. Final Answer:

   Use the Kibana interface to select data and choose visualization types without coding -> Option A

4. Quick Check:

   Kibana interface = No code visuals [OK]

Hint: Kibana uses GUI, not code, for creating visuals [OK]

Common Mistakes:

• Assuming SQL queries are needed inside Kibana
• Thinking manual coding is required for visuals
• Believing data must be exported for visualization

3. Given Elasticsearch data indexed with sales records, what will Kibana show if you create a bar chart visualization grouping sales by product category?

medium

A. A list of raw sales records without any grouping

B. An error because Kibana cannot group data

C. A bar chart showing total sales amounts for each product category

D. A pie chart showing sales by date

Solution

1. Step 1: Understand grouping in Kibana visualizations

   Kibana can group Elasticsearch data by fields like product category to summarize data visually.

2. Step 2: Identify the correct visualization output

   A bar chart grouped by product category will show total sales per category, not raw records or other chart types.

3. Final Answer:

   A bar chart showing total sales amounts for each product category -> Option C

4. Quick Check:

   Grouping data = summarized bar chart [OK]

Hint: Grouping fields in Kibana creates summarized charts [OK]

Common Mistakes:

• Expecting raw data instead of grouped summary
• Confusing chart types (bar vs pie)
• Thinking Kibana cannot group data

4. You created a Kibana visualization but it shows no data. Which of these is the most likely cause?

medium

A. You must write code to display data in Kibana

B. The Elasticsearch index pattern is incorrect or missing

C. Kibana does not support visualizations for Elasticsearch data

D. Your browser does not support charts

Solution

1. Step 1: Check the index pattern setup

   Kibana needs a correct Elasticsearch index pattern to find and display data in visualizations.

2. Step 2: Rule out other causes

   Kibana supports visualizations without coding, and modern browsers support charts, so these are unlikely causes.

3. Final Answer:

   The Elasticsearch index pattern is incorrect or missing -> Option B

4. Quick Check:

   Missing index pattern = no data shown [OK]

Hint: Check index pattern if Kibana shows no data [OK]

Common Mistakes:

• Assuming Kibana can't visualize Elasticsearch data
• Thinking coding is required to show data
• Blaming browser for visualization issues

5. You want to monitor website traffic trends over time using Kibana. Which approach best uses Kibana's visualization features with Elasticsearch data?

hard

A. Use Kibana only to view raw log data without visualization

B. Export Elasticsearch logs to Excel and create charts there

C. Write custom scripts to generate charts outside Kibana

D. Create a time series line chart in Kibana using the timestamp field from Elasticsearch logs

Solution

1. Step 1: Identify the best visualization type for trends

   Time series line charts are ideal for showing trends over time using timestamped data.

2. Step 2: Use Kibana's built-in features

   Kibana can directly use Elasticsearch timestamp fields to create dynamic, interactive time series charts without exporting or coding.

3. Final Answer:

   Create a time series line chart in Kibana using the timestamp field from Elasticsearch logs -> Option D

4. Quick Check:

   Time series + Kibana = trend monitoring [OK]

Hint: Use Kibana time series charts for timestamped data trends [OK]

Common Mistakes:

• Exporting data unnecessarily instead of using Kibana
• Ignoring Kibana's visualization capabilities
• Using raw data views only without charts