Elasticsearch for ML: Data Visualization and Dashboarding

1. Introduction

Data visualization and dashboarding are essential skills for any data scientist or machine learning engineer. They allow you to explore, analyze, and communicate your data and results in a clear and engaging way.

But how do you create effective and interactive data visualizations and dashboards? What tools and frameworks can you use to make your data come alive?

In this blog, you will learn how to use Elasticsearch, Kibana, and Vega to visualize and dashboard your data and ML results in a simple and effective way. You will learn how to:

• Set up Elasticsearch and Kibana on your local machine or cloud service
• Load and index data into Elasticsearch using Python and the Elasticsearch API
• Explore data with Kibana Lens and Discover, two powerful tools for data exploration and analysis
• Create basic visualizations with Kibana Visualize, such as bar charts, pie charts, line charts, and more
• Build advanced visualizations with Vega and Vega-Lite, two declarative languages for creating custom and interactive visualizations
• Design and share dashboards with Kibana Dashboard, a tool for creating and managing dashboards that combine multiple visualizations

By the end of this blog, you will have a solid understanding of how to use Elasticsearch, Kibana, and Vega for data visualization and dashboarding. You will also have a portfolio of visualizations and dashboards that you can use to showcase your data and ML results.

Ready to get started? Let’s dive in!

2. Setting up Elasticsearch and Kibana

Before you can start visualizing and dashboarding your data and ML results, you need to set up Elasticsearch and Kibana on your machine or cloud service. Elasticsearch is a distributed, open-source search and analytics engine that can handle large amounts of structured and unstructured data. Kibana is a user interface for Elasticsearch that allows you to create and share data visualizations and dashboards.

There are different ways to install and run Elasticsearch and Kibana, depending on your operating system and preferences. You can download and install them manually, use Docker containers, or use cloud services such as Amazon Web Services (AWS) or Elastic Cloud. In this tutorial, we will use the manual installation method for Windows, but you can follow the official documentation for other options.

To install Elasticsearch and Kibana, you need to follow these steps:

1. Download the latest versions of Elasticsearch and Kibana from the Elastic website. Make sure you download the zip files for Windows.
2. Extract the zip files to your preferred location, such as C:\elasticsearch and C:\kibana.
3. Open a command prompt and navigate to the bin folder of Elasticsearch, such as C:\elasticsearch\bin. Run the command

   elasticsearch.bat

   to start Elasticsearch.

4. Open another command prompt and navigate to the bin folder of Kibana, such as C:\kibana\bin. Run the command

   kibana.bat

   to start Kibana.

5. Open your web browser and go to http://localhost:9200 to check if Elasticsearch is running. You should see a JSON response with information about the cluster name, version, and status.
6. Go to http://localhost:5601 to check if Kibana is running. You should see the Kibana home page with different options to explore and visualize your data.

Congratulations, you have successfully set up Elasticsearch and Kibana on your machine! You are now ready to load and index data into Elasticsearch and start creating data visualizations and dashboards with Kibana and Vega.

3. Loading and indexing data into Elasticsearch

Now that you have Elasticsearch and Kibana running on your machine, you need to load and index some data into Elasticsearch so that you can visualize and dashboard it later. In this tutorial, we will use a sample dataset of movie ratings from the MovieLens website. This dataset contains information about movies, ratings, tags, and users.

To load and index data into Elasticsearch, you need to follow these steps:

1. Download the ml-latest-small.zip file from the MovieLens website and extract it to your preferred location, such as C:\data.
2. Install Python and the elasticsearch library on your machine. You can use the Anaconda distribution of Python, which comes with many useful packages for data science and machine learning.
3. Create a Python script to load and index the data into Elasticsearch. You can use the following code as a template, but make sure to change the file paths and index names according to your preferences.

# Import the elasticsearch library
from elasticsearch import Elasticsearch

# Create an Elasticsearch client object
es = Elasticsearch()

# Define the index settings and mappings
index_settings = {
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 0
  },
  "mappings": {
    "properties": {
      "movieId": {"type": "integer"},
      "title": {"type": "text"},
      "genres": {"type": "keyword"},
      "userId": {"type": "integer"},
      "rating": {"type": "float"},
      "timestamp": {"type": "date"},
      "tag": {"type": "text"}
    }
  }
}

# Create the index in Elasticsearch
es.indices.create(index="movies", body=index_settings)

# Define a function to read a CSV file and return a list of dictionaries
def read_csv(file):
  data = []
  with open(file, "r", encoding="utf-8") as f:
    header = f.readline().strip().split(",")
    for line in f:
      fields = line.strip().split(",")
      doc = dict(zip(header, fields))
      data.append(doc)
  return data

# Read the movies, ratings, and tags files
movies = read_csv("C:/data/ml-latest-small/movies.csv")
ratings = read_csv("C:/data/ml-latest-small/ratings.csv")
tags = read_csv("C:/data/ml-latest-small/tags.csv")

# Define a function to merge the ratings and tags with the movies
def merge_data(movies, ratings, tags):
  data = []
  for movie in movies:
    movieId = movie["movieId"]
    movie_ratings = [rating for rating in ratings if rating["movieId"] == movieId]
    movie_tags = [tag for tag in tags if tag["movieId"] == movieId]
    for rating in movie_ratings:
      doc = movie.copy()
      doc.update(rating)
      doc["tags"] = movie_tags
      data.append(doc)
  return data

# Merge the data
data = merge_data(movies, ratings, tags)

# Index the data into Elasticsearch
for doc in data:
  es.index(index="movies", body=doc)

Run the Python script and wait for it to finish. You can check the progress by printing the number of documents indexed or by using the cat count API.

Congratulations, you have successfully loaded and indexed data into Elasticsearch! You are now ready to explore data with Kibana Lens and Discover, two powerful tools for data exploration and analysis.

4. Exploring data with Kibana Lens and Discover

Once you have loaded and indexed data into Elasticsearch, you can start exploring it with Kibana Lens and Discover. These are two powerful tools that allow you to quickly and easily explore and analyze your data and ML results. You can use them to find insights, patterns, and anomalies in your data, as well as to create and save queries for later use.

Kibana Lens is a drag-and-drop interface that lets you create data visualizations without writing any code. You can choose from different types of charts, such as bar, line, pie, area, and more, and customize them with various options, such as colors, labels, axes, and legends. You can also combine multiple charts into a single visualization to compare and contrast different aspects of your data.

Kibana Discover is a search interface that lets you query your data using the Elasticsearch Query DSL. You can use different types of queries, such as match, term, range, bool, and more, to filter and refine your data. You can also use aggregations, such as sum, average, count, and more, to group and summarize your data. You can also view your data in a table or a histogram, and export it to CSV or JSON formats.

To explore data with Kibana Lens and Discover, you need to follow these steps:

1. Go to the Kibana home page and click on the Analyze data option. You will see a list of different tools that you can use to explore and visualize your data.
2. Click on the Lens option to open the Lens interface. You will see a blank canvas where you can create your visualization, and a panel where you can select your index and fields.
3. Select the movies index that you created in the previous section. You will see a list of fields that are available in your index, such as movieId, title, genres, rating, tag, and more.
4. Drag and drop the fields that you want to visualize from the panel to the canvas. For example, you can drag and drop the rating field to the Vertical axis area, and the genres field to the Horizontal axis area. You will see a bar chart that shows the average rating for each genre.
5. Customize your visualization by changing the chart type, adding filters, adjusting the colors, and more. You can also add more layers to your visualization by clicking on the + icon at the top right corner of the canvas. For example, you can add a line chart that shows the number of ratings for each genre over time.
6. Save your visualization by clicking on the Save button at the top right corner of the screen. You can give your visualization a name and a description, and choose whether to add it to a dashboard or not.
7. Click on the Discover option to open the Discover interface. You will see a search bar where you can enter your query, and a table where you can view your data.
8. Enter your query using the Elasticsearch Query DSL syntax. For example, you can enter

   {"match": {"genres": "Comedy"}}

   to find all the movies that belong to the comedy genre.

9. View your data in the table below the search bar. You can add or remove columns, sort by different fields, and paginate through the results.
10. Use aggregations to group and summarize your data. You can click on the Add button next to the Buckets or Metric sections to add different types of aggregations. For example, you can add a Terms aggregation on the tag field to see the most popular tags for comedy movies, and a Average aggregation on the rating field to see the average rating for each tag.
11. View your data in a histogram above the table. You can change the interval and the time range of the histogram, and hover over the bars to see the details.
12. Save your query by clicking on the Save button at the top right corner of the screen. You can give your query a name and a description, and choose whether to add it to a dashboard or not.

Congratulations, you have successfully explored data with Kibana Lens and Discover! You are now ready to create basic visualizations with Kibana Visualize, a tool for creating and managing different types of visualizations.

5. Creating basic visualizations with Kibana Visualize

After exploring your data with Kibana Lens and Discover, you may want to create more complex and customized visualizations with Kibana Visualize. This is a tool that allows you to create and manage different types of visualizations, such as pie charts, line charts, heat maps, gauges, and more. You can also use Kibana Visualize to create visualizations based on saved queries from Discover, or to create new queries using the Lucene query syntax.

To create basic visualizations with Kibana Visualize, you need to follow these steps:

1. Go to the Kibana home page and click on the Visualize option. You will see a list of different types of visualizations that you can create.
2. Click on the + icon at the top right corner of the screen to create a new visualization. You will see a list of different visualization types that you can choose from, such as pie chart, line chart, heat map, gauge, and more.
3. Select the type of visualization that you want to create. For example, you can select the Pie chart option to create a pie chart.
4. Select the index that you want to use for your visualization. For example, you can select the movies index that you created in the previous section.
5. Configure your visualization by adding buckets and metrics. Buckets are the categories that you want to group your data by, such as terms, ranges, filters, and more. Metrics are the values that you want to measure for each bucket, such as count, sum, average, and more. For example, you can add a Terms bucket on the genres field to see the distribution of genres, and a Count metric to see the number of movies for each genre.
6. Customize your visualization by changing the options, such as labels, colors, legend, and more. You can also add filters, queries, or time ranges to refine your data.
7. Save your visualization by clicking on the Save button at the top right corner of the screen. You can give your visualization a name and a description, and choose whether to add it to a dashboard or not.

Congratulations, you have successfully created a basic visualization with Kibana Visualize! You are now ready to build advanced visualizations with Vega and Vega-Lite, two declarative languages for creating custom and interactive visualizations.

6. Building advanced visualizations with Vega and Vega-Lite

If you want to create more advanced and customized visualizations with Kibana, you can use Vega and Vega-Lite. These are two declarative languages that allow you to create custom and interactive visualizations using JSON syntax. You can use Vega and Vega-Lite to create visualizations that are not supported by the default Kibana visualization types, such as maps, networks, treemaps, and more. You can also use Vega and Vega-Lite to add interactivity to your visualizations, such as tooltips, zooming, filtering, and more.

To build advanced visualizations with Vega and Vega-Lite, you need to follow these steps:

1. Go to the Kibana home page and click on the Visualize option. You will see a list of different types of visualizations that you can create.
2. Click on the + icon at the top right corner of the screen to create a new visualization. You will see a list of different visualization types that you can choose from, such as pie chart, line chart, heat map, gauge, and more.
3. Select the Vega option to open the Vega editor. You will see a blank canvas where you can create your visualization, and a panel where you can write your Vega or Vega-Lite specification.
4. Write your Vega or Vega-Lite specification using the JSON syntax. You can use the Vega documentation or the Vega-Lite documentation to learn more about the syntax and the options. You can also use the Vega editor or the Vega-Lite examples to get some inspiration and guidance.
5. Preview your visualization by clicking on the Update button at the bottom of the panel. You will see your visualization rendered on the canvas.
6. Customize your visualization by changing the options, such as data sources, scales, marks, encodings, interactions, and more. You can also add filters, queries, or time ranges to refine your data.
7. Save your visualization by clicking on the Save button at the top right corner of the screen. You can give your visualization a name and a description, and choose whether to add it to a dashboard or not.

Congratulations, you have successfully built an advanced visualization with Vega and Vega-Lite! You are now ready to design and share dashboards with Kibana Dashboard, a tool for creating and managing dashboards that combine multiple visualizations.

7. Designing and sharing dashboards with Kibana Dashboard

After creating your visualizations with Kibana Lens, Visualize, and Vega, you may want to design and share dashboards with Kibana Dashboard. This is a tool that allows you to create and manage dashboards that combine multiple visualizations into a single view. You can use dashboards to present and communicate your data and ML results in a clear and interactive way.

To design and share dashboards with Kibana Dashboard, you need to follow these steps:

1. Go to the Kibana home page and click on the Dashboard option. You will see a list of existing dashboards that you can open or edit, or you can create a new dashboard by clicking on the Create dashboard button.
2. Add visualizations to your dashboard by clicking on the Add button at the top right corner of the screen. You will see a list of saved visualizations that you can choose from, or you can create a new visualization by clicking on the Create new button.
3. Arrange and resize your visualizations on the dashboard by dragging and dropping them, or by using the Resize button at the bottom right corner of each visualization. You can also edit or delete your visualizations by using the Edit or Delete buttons at the top right corner of each visualization.
4. Customize your dashboard by changing the options, such as title, time range, filters, queries, and more. You can also add interactivity to your dashboard by using the Controls visualization, which allows you to create input controls, such as dropdown menus, range sliders, and more, to filter your data.
5. Save your dashboard by clicking on the Save button at the top right corner of the screen. You can give your dashboard a name and a description, and choose whether to store the time range and filters with the dashboard or not.
6. Share your dashboard by clicking on the Share button at the top right corner of the screen. You can choose from different options, such as embedding the dashboard in an iframe, generating a permalink, or exporting the dashboard to PDF or PNG formats.

Congratulations, you have successfully designed and shared a dashboard with Kibana Dashboard! You have completed this tutorial on how to use Elasticsearch, Kibana, and Vega for data visualization and dashboarding. We hope you enjoyed it and learned something new!

8. Conclusion

In this blog, you have learned how to use Elasticsearch, Kibana, and Vega for data visualization and dashboarding. You have learned how to:

• Set up Elasticsearch and Kibana on your local machine or cloud service
• Load and index data into Elasticsearch using Python and the Elasticsearch API
• Explore data with Kibana Lens and Discover, two powerful tools for data exploration and analysis
• Create basic visualizations with Kibana Visualize, such as bar charts, pie charts, line charts, and more
• Build advanced visualizations with Vega and Vega-Lite, two declarative languages for creating custom and interactive visualizations
• Design and share dashboards with Kibana Dashboard, a tool for creating and managing dashboards that combine multiple visualizations

By following this blog, you have gained a solid understanding of how to use Elasticsearch, Kibana, and Vega for data visualization and dashboarding. You have also created a portfolio of visualizations and dashboards that you can use to showcase your data and ML results.

We hope you enjoyed this blog and learned something new. If you have any questions or feedback, please leave a comment below. Thank you for reading!