How to Load Data from Elasticsearch to Snowflake?

Unlock the full potential of your Elasticsearch data by integrating it seamlessly with Snowflake. With Hevo’s automated pipeline, get data flowing effortlessly—watch our 1-minute demo below to see it in action!

Are you trying to connect Elasticsearch to Snowflake? Have you looked all over the internet to find a solution for it? If yes, then you are in the right place. Snowflake is a fully managed Data Warehouse, whereas Elasticsearch is a modern search and analytics engine. Loading your data from Elasticsearch to Snowflake provides data-driven insights and solutions.

This article will give you a brief overview of Elasticsearch and Snowflake. You will also get to know how you can set up your Elasticsearch to Snowflake Integration using 2 methods. Moreover, the limitations in the case of the manual method will also be discussed in further sections. Read along to decide which method of connecting Elasticsearch to Snowflake is best for you.

Prerequisites

You will have a much easier time understanding the ways for setting up the Elasticsearch to Snowflake Integration if you have gone through the following aspects:

• An active Elasticsearch account.
• An active Snowflake account.
• Working knowledge of Databases and Data Warehouses.
• Clear idea regarding the type of data to be transferred.

What is Elasticsearch?

Elasticsearch is a distributed, open-source analytics and search engine for all types of data, such as numerical, structured, textual, etc. Elasticsearch was developed on Lucene in 2010. It is well known for its speed, scalability, REST APIs, etc. It is the core component of Elastic Stack, also known as ELK Stack (Elasticsearch, Logstash, Kibana). 

Elasticsearch is based on indexes and documents. Indexing is used for running complex queries. A collection of related documents is formed where each document correlates with a key-value pair.   

To know more about Elasticsearch, visit this link.

What is Snowflake?

Snowflake is a fully managed Data Warehouse on the cloud that is available as SaaS (Software-as-a-Service). It enables you to store, share and analyze data. With its multi-cluster shared architecture, you can have better performance, scalability, and flexibility. Snowflake is available on Azure, AWS, and GCP (Google Cloud Platform). Its in-built infrastructure removes the need to manage infrastructure. It has a columnar database engine that uses advanced optimization. 

To know more about Snowflake, visit this link.

Methods to Set up Elasticsearch to Snowflake Integration

There are many ways of loading data from Elasticsearch to Snowflake. In this blog, we are going to look into two popular ways. In the end, you will have a good understanding of each of these two methods. This will help you to make the right decision based on your use case:

• Method 1: Using Hevo Data to Set up Elasticsearch to Snowflake Integration
• Method 2: Manual ETL Process to Set up Elasticsearch to Snowflake Integration using Amazon S3

Method 1: Best Way to Set up Elasticsearch to Snowflake Integration: Using Hevo Data

Hevo Data, a No-code Data Pipeline, helps you replicate data from Elasticsearh to Snowflake in a completely hassle-free & automated manner. Hevo’s end-to-end Data Management connects you to Elasticsearch’s cluster using the Elasticsearch Transport Client and synchronizes your cluster data using indices. Hevo’s Pipeline allows you to leverage the services of both Generic Elasticsearch & AWS Elasticsearch.

The steps to load data from Elasticsearch to Snowflake using Hevo Data are as follow:

• Step 1) Authenticate Source: Connect your Elasticsearch account to Hevo’s platform. Hevo has an in-built Elasticsearch integration that connects to your account within minutes.

• Step 2) Configure Destination: Select the Snowflake as your destination and start moving your data.

Check out what makes Hevo amazing:

• Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
• Auto Schema Mapping: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data from Elasticsearch and replicates it to the destination schema.
• Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
• Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use for aggregation.
• Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
• Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
• Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

With continuous real-time data movement, Hevo allows you to transfer your Elasticsearch data and seamlessly load it to Snowflake or any other destination of your choice with a no-code, easy-to-setup interface. Try our 14-day full-feature access free trial!

Method 2: Manual ETL Process to Set up Elasticsearch to Snowflake Integration using Amazon S3

Elasticsearch and Snowflake are two different data storage solutions as both of them offer completely different structures. There is no direct method to load data from Elasticsearch to Snowflake, but the use of a middleman, that connects with both Elasticsearch and Snowflake, can ease this process. In this blog, you will use Amazon S3 as an intermediate step.

The following steps describe the flow to connect Elasticsearch to Snowflake:

• Step 1: Load Data from Elasticsearch to S3
• Step 2: Load Data from S3 to Snowflake

Let’s discuss these methods in detail.

Step 1: Load Data from Elasticsearch to Amazon S3 

In this section, you will use Logstash to load data from Elasticsearch to Amazon S3. Logstash is a core element of the ELK stack. Logstash is a server-side data processing data pipeline that ingests data from multiple data sources, processes them, and then delivers it to your desired destination. You can export your data from an elastic index to a JSON or CSV.   

You need to follow these steps to load data from Elasticsearch to S3:

1. To install the Logstash Elasticsearch plugin, you need to execute the following command:

logstash-plugin install logstash-input-elasticsearch

2. To install the Logstash plugin for S3, you can execute the following command:

logstash-plugin install logstash-output-s3

3. You need to use the following configuration for the Logstash execution and save it as ‘es_to_s3.conf’.

input {

 elasticsearch {

    hosts => "elastic_search_host"

    index => "source_index_name"

    query => '

    {

    "query": {

    "match_all": {}

    }

    } 

  '

  }

}


output {

   s3{

     access_key_id => "aws_access_key"

     secret_access_key => "aws_secret_key"

     bucket => "bucket_name"

   }

}

Here, you can replace the ‘elastic_search_host’ with the URL of your Elasticsearch instance, and ‘source_index_name’ with your index value.

4. The following command will execute your configuration file created in the last step. It will create a JSON output that will match the query provided in the S3 location. 

logstash -f es_to_s3.conf

You can also refer to the detailed guide here to understand the configuration parameters.

Step 2: Load Data from Amazon S3 to Snowflake

To authenticate S3 bucket access control during load or unload operation, AWS (Amazon Web Services) allows you to create IAM (Identity Access Management) users with required permissions. You can also create an IAM role and assign it to a set of users. 

It is highly recommended to compress your files stored in an S3 bucket to facilitate faster data transfer. You can use compression methods like gzip, bzip2, brotli, deflate, raw deflate, and zstandard. 

You can use the ‘COPY INTO’ command to copy data from S3 to Snowflake. It has a source, destination, and a set of parameters to specify your copy operation. To copy data using the pattern matching option is as follows:

copy into table_name   
from s3://snowflakebucket/data/abc_files 
credentials=(aws_key_id='$KEY_ID' aws_secret_key='$SECRET_KEY')   pattern='filename.JSON';

Limitations of Setting up ElasticSearch to Snowflake Integration using Amazon S3

Some of the limitations of the manual method are as follows:

1. The manual method doesn’t support real-time data replication. You need to periodically send data from Elasticsearch to Snowflake.
2. With the huge volume of data, you need a solution to track all the updates and stay on top. 

Use Cases to Connect Elasticsearch to Snowflake

1. Unified Analytics Across Structured and Unstructured Data: Elasticsearch is ideal for indexing and querying unstructured data like logs, text, and documents, while Snowflake is optimized for structured data and SQL-based analytics.
2. Real-Time Search and Historical Data Analysis: Elasticsearch is typically used for real-time search and analytics, but Snowflake excels at historical data analysis and long-term storage.
3. Advanced BI and Reporting: Elasticsearch provides fast, scalable search, but lacks advanced business intelligence (BI) tools. Snowflake integrates seamlessly with BI platforms like Tableau, Power BI, and Looker.
4. Machine Learning and Predictive Analytics: Elasticsearch is excellent for real-time data processing but lacks native machine learning (ML) and predictive analytics capabilities that Snowflake and platforms like Amazon SageMaker or DataRobot offer.

Conclusion

In this blog, you have learned about Elasticsearch, Snowflake, and two different approaches to load Elasticsearch data to the Snowflake data warehouse. The custom method involves the usage of an external tool, Amazon S3. This blog also discusses the limitations of the manual method in detail. Now, transferring data from Elasticsearch to Snowflake using the manual will consume a lot of time, money, and resources. Moreover, such a solution will require skilled engineers and regular maintenance.

Hevo offers a No-code Data Pipeline that can empower you to overcome the above limitations. Hevo caters to 100+ data sources (including 40+ free sources) like Elasticsearch and can seamlessly transfer your data to Snowflake in real-time, something that is not possible via the manual approach. Hevo’s fault-tolerant architecture ensures a consistent and secure transfer of your Elasticsearch data. It will make your life easier and make data replication hassle-free.

Learn more about Hevo

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