Connect Elasticsearch to BigQuery: 2 Easy Methods

Do you want to transfer your Elasticsearch data to Google BigQuery? Are you finding it challenging to connect Elasticsearch to BigQuery? If yes, then you’ve landed at the right place! This article will answer all your queries & relieve you of the stress of finding a truly efficient solution. Follow this step-by-step guide to master the skill of efficiently transferring your data to Google BigQuery from Elasticsearch.

It will help you take charge in a hassle-free way without compromising efficiency. This article aims at making the data export process as smooth as possible.

Upon a complete walkthrough of the content, you will be able to successfully set up a connection between Elasticsearch & Google BigQuery to seamlessly transfer data to Google BigQuery for a fruitful analysis in real-time. It will further help you build a customized ETL pipeline for your organization. Through this article, you will get a deep understanding of the tools and techniques & thus, it will help you hone your skills further.

Methods to Connect Elasticsearch to BigQuery

There are multiple ways in which you can transfer data from Elasticsearch to BigQuery:

Method 1: Using Hevo Data, a No-code Data Pipeline

Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Steps to use Hevo Data:

Hevo Data focuses on two simple steps to get you started:

Step 1: Configure Elasticsearch as your Source

• Click PIPELINES in the Navigation Bar.
• Click + CREATE in the Pipelines List View.
• In the Select Source Type page, select Elasticsearch.
• In the Configure your Elasticsearch Source page, specify the following:

Step 2: Set BigQuery as your Destination

• Click DESTINATIONS in the Navigation Bar.
• Click + CREATE in the Destinations List View.
• In Add Destination page select Google BigQuery as the Destination type.
• In the Configure your Google BigQuery Warehouse page, specify the following details:

To know more about BigQuery Elasticsearch integration, refer to Hevo documentation:

• Elasticsearch as a Source
• Google BigQuery as a Destination

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Method 2: Using Google Dataflow to Connect Elasticsearch to BigQuery

To illustrate how to integrate data from Elasticsearch to BigQuery, we are going to use a public dataset from Stack Overflow. With just a few steps, you can successfully ingest this data using Dataflow job.

Step 1: Log in to Google Cloud Platform

You can use your existing Google Account to log in; otherwise, you can create a new account.

• You can download the public dataset that I have used, or you can use your own dataset.
• Click on View Dataset.

• Once you click on View Dataset, you will be redirected to BigQuery Studio.

• You can search for the table stackoverflow_posts under the BigQuery dataset. It has columns like id, title, body, etc.

Step 2: Create a DataFlow Job

You can create a DataFlow Job to migrate data from BigQuery to ElasticSearch.

• Select BigQuery to ElasticSearch from the drop-down menu, which is one of Google’s provided templates.

• You can fill in the Job name and Regional endpoint as per your choice. I have given the Job name as stackoverflow_load, and the regional endpoint as us-central1(lowa).

• If you don’t have an existing API key, you can create an API key.
• The API Key would be a Base64-encoded key. 
• Cloud ID will be shown when you create a new API key. I have highlighted the Cloud ID of my API key for your reference.

{
  "id": "Qq3CypABgXuLDQJS7fph",
  "name": "demo",
  "expiration": 1726572690786,
  "api_key": "mTrSpsIYQSqtPmeXbOtXAg,"
  "encoded": "UXEzQ3lwQUJnWHVMRFFKUzdmcGg6bVRyU3BzSVlRU3F0UG1lWGJPdFhBZw==",
  "beats_logstash_format": "Qq3CypABgXuLDQJS7fph:mTrSpsIYQSqtPmeXbOtXAg"
}

The highlighted is what your API key would like.

Step 3: Run the Job

Once you have filled in the necessary fields, check them once, and you can run the created job.

Step 3: Using ElasticsearchIO to Fetch Data from Elasticsearch

• The details can vary depending on the creation of an API key. I have attached my details for your reference.
• Once checked, you can now Click on ‘Run Job’ to start the batch processing dataflow.

Within a few minutes, you can visualize your data flowing into your Elasticsearch index.

That’s all you need to do to successfully migrate your data from BigQuery to Elasticsearch.

Are you tired of following a tedious setup process? Click here to check out the method of using Hevo Data that will let you integrate Elasticsearch to BigQuery in an automated and hassle-free manner!

Limitations of Integrating Elasticsearch to Google BigQuery using Google Dataflows

• Integrating Elasticsearch with Google BigQuery using Apache Beam & Google Dataflow requires you to write custom Kotlin-based code to fetch, transform, and then load data. Hence, you must have strong technical knowledge. 
•  Setting up a dedicated VPC network, NAT gateways, etc., can be a challenging task, especially for beginners as it requires you to have a deep understanding of how IP addresses & subnetting work.
• You must ensure that you provide all correct parameters such as table name, schema, etc. as even a small error can result in the ETL process failing.
• It requires you to have a general idea of how different services such as Apache Airflow, Beam, Google Dataflow, etc. work, resulting in a bottleneck, as many might not be aware of their operations.

Use Cases of BigQuery Elasticsearch Integration

• Data Analytics: By using Elasticsearch connector for BigQuery, you can analyze your business data in real-time. BigQuery’s Machine Learning features enable you to generate insightful analytics about customers, campaigns, and marketing pipelines. 
• Data Storage: Building ETL pipelines for Elasticsearch BigQuery makes the data storage and transformation process easier. You can store large amounts of data, for example, marketing data from multiple sources, in a centralized cloud-based location such as BigQuery. You can access and query the data without expensive storage hardware.

Conclusion

This article teaches you how to connect Elasticsearch to BigQuery with ease. It provides in-depth knowledge about the concepts behind every step to help you understand and implement them efficiently. These methods, however, can be challenging, especially for a beginner & this is where Hevo saves the day. Hevo Data, a No-code Data Pipeline, helps you transfer data from a source of your choice in a fully-automated and secure manner without having to write the code repeatedly. This articles also sheds light on some BigQuery connector for Elasticsearch use cases. 

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Tell us about your experience of connecting Elasticsearch to BigQuery! Share your thoughts in the comments section below!

FAQs to load data from Elasticsearch to BigQuery