ElasticSearch is a distributed, open-source, real-time search and analysis system that enables you to go beyond simple full-text search and perform complex operations to access, collect, index, and filter data.

Using Elasticsearch as a data source, organizations can extract data for analysis on other BI platforms. When using Elasticsearch as back-end storage, organizations require a way to inject data from multiple sources into their Elasticsearch data storage.

In this article, we will list the 7 best Elasticsearch ETL Tools for analyzing your Elasticsearch data. Read along to learn more about them and decide which one best suits you.

What is Elasticsearch? 

6 Best Elasticsearch ETL Tools | Elasticsearch logo
Elasticsearch Logo

Elasticsearch is an open-source tool that acts as a distributed engine for search and analytics for all kinds of structured and unstructured data. It can index documents. Elasticsearch is fast, scalable, and can handle huge quantities of data. 

When you load a document to Elasticsearch, it creates a reverse index of all the fields in that document. This data is stored in JSON form and can be queried. 

Elasticsearch is frequently used as a part of the ELK (Elasticsearch, Logstash, and Kibana) stack. Logstash provides loading and transformation capabilities. Kibana is used to visualize Elasticsearch data. Elasticsearch has APIs to add documents to the index (Index API), retrieve documents (Get API), query over the index data (Search API), and add additional fields to an index (Get API). Elasticsearch works using a cluster of servers, which can be scaled by adding more servers. 

Some important use cases of Elasticsearch are as follows:

  • Application search
  • Website search
  • Enterprise search
  • Logging and log analytics
  • Infrastructure metrics and container monitoring
  • Application performance monitoring
  • Geospatial data analysis and visualization
  • Security analytics
  • Business analytics

To learn more about Elasticsearch, visit here.

What is ETL?

ETL — Extract, Transform, and Load — simply stands for the transfer and the transformation of data using data pipelines. In general, data is extracted from multiple sources, transformed, and loaded into a data lake or a data warehouse for analytics. 

If you are using Elasticsearch then you might need to perform ETL to move the data to a business analytics platform or a data warehouse. To achieve this you can select any one of the numerous Elasticsearch ETL Tools available in the market.

Limitations of Manual ETL

  • Manual ETL is labor-intensive and takes a very long time to get right.
  • Manual ETL also requires heavy technical expertise.
  • Data security can be an issue if not managed correctly.

But manual ETL for your Elasticsearch data can be completely avoided using some good Elasticsearch ETL Tools. 

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7 Best Elasticsearch ETL Tools

Here’s a list of some of the best Elasticsearch ETL Tools available in the market for you can choose from to simplify ETL processes. Selecting the right tool for your business needs has never been this easy:

  1. Elasticsearch ETL Tools: Hevo Data
  2. Elasticsearch ETL Tools: Logstash
  3. Elasticsearch ETL Tools: Apache NiFi
  4. Elasticsearch ETL Tools: Apache Spark
  5. Elasticsearch ETL Tools: treamSets
  6. Elasticsearch ETL Tools: CloverDX
  7. Elasticsearch ETL Tools: Transporter

7 Best Elasticsearch ETL Tools

Choosing the one ideal tool from the numerous Elasticsearch ETL Tools that perfectly meets your business requirements can be a challenging task, especially when there’s a large variety of ETL tools available in the market.

ETL Elasticsearch tools can manage data extraction, transformation, and loading into Elasticsearch. Elasticsearch query optimization is important for improving search performance and ensuring efficient resource use.

Elasticsearch monitoring and management solutions ensure optimal performance, identifying issues, and maintaining the health of your system.

You can use an Elasticsearch query tool, which facilitates interaction with Elasticsearch clusters by providing a user-friendly interface for executing queries and managing data.

To simplify your search, here is a comprehensive list of the 7 best Elasticsearch ETL Tools that you can choose from and start setting up ETL pipelines with ease:

1) Hevo Data

Elasticsearch ETL Tools- Hevo Data Logo
Hevo Data Logo

Hevo Data, a No-code Data Pipeline reliably replicates data from any data source with zero maintenance. You can get started with Hevo’s 14-day Free Trial and instantly move data from 150+ pre-built integrations comprising a wide range of SaaS apps and databases. What’s more – our 24X7 customer support will help you unblock any pipeline issues in real time.

Get started for Free with Hevo

With Hevo, fuel your analytics by not just loading data into Warehouse but also enriching it with in-built no-code transformations. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Check out what makes Hevo amazing:

  • Near Real-Time Replication: Get access to near real-time replication on All Plans. Near Real-time via pipeline prioritization for Database Sources. For SaaS Sources, near real-time replication depend on API call limits.
  • In-built Transformations: Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty Python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation. 
  • Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every stat of the pipeline and data flow. Bring real-time visibility into your ETL with Alerts and Activity Logs.
  • Reliability at Scale: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.

Hevo provides Transparent Pricing to bring complete visibility to your ETL spend.

After trying Hevo for about 2 weeks, we were highly impressed with some of the features like easy setup, intuitive UI, end-to-end data encryption, multiple pre-built integrations, and advanced transformation logic and we knew we had a winner.

– Samarth Kishnani, Product Manager, Groww

Pricing

Elasticsearch ETL Tools: Hevo Data Pricing
Pricing of Hevo Data
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2) Logstash

Elasticsearch ETL Tools- Logstash Logo
Logstash Logo

Another important choice among the Elasticsearch ETL Tools is Logstash. It is an Elastic company’s product built to collect, store, and manage data from logs. It is an open-source tool and it can collect data from multiple sources in real-time. But Logstash is not just about data collection from logs. It can transform data using its output plugins, filters, and native codecs. 

Logstash processing data pipeline has three stages. Input, filters, and outputs, which generate, modify, and ship them respectively. Logstash is extremely effective, however unlike other Elasticsearch ETL Tools, it requires a level of technical expertise to handle.

Unique features of Logstash

  • Logstash can dynamically bring together data from various sources, standardize it, and direct it to your preferred destinations. 
  • It enables the purification and democratization of your data, making it suitable for a wide range of advanced downstream analytics and visualization scenarios.
Download the Guide to Evaluate ETL Tools
Download the Guide to Evaluate ETL Tools
Download the Guide to Evaluate ETL Tools
Learn the 10 key parameters while selecting the right ETL tool for your use case.

3) Apache NiFi

Elasticsearch ETL Tools- Apache Nifi Logo
Apache NiFi Logo

Apache NiFi is an open-source tool used to automate data transfer between software. NiFi has a web-based UI and provides a configuration facility, great design, control, low latency, and dynamic prioritization.  NiFi can work on work on several nodes improving processing performance. You can write SQL queries locally in NiFi that process Elasticsearch data. Like Logstash, NiFi is also suitable for programmers with high levels of technical expertise. 

Unique Features of Apache Nifi

  • Highly configurable
  • Web-based UI
  • In-built monitoring system
  • Support for secure protocols
  • Good user management

4) Apache Spark

Elasticsearch ETL Tools- Apache Spark Logo
Apache Spark Logo

Apache Spark is an open-source, large-scale data processing engine. It is one of those Elasticsearch ETL Tools that provides high performance for batch and streaming data. Using Spark, you can transform data, and process it in real-time. Spark supports multiple programming languages and different types of structured or semi-structured data.

Unique Features of Apache Spark

  • Fault Tolerance
  • Dynamic Characteristics
  • Lazy Evaluation Approach
  • Real-Time Stream Processing Capability
  • High Speed
  • Reusability Feature
  • Advanced Analytics Support
  • In-Memory Computing Capability
  • Compatibility with Multiple Languages
  • Integration with Hadoop
  • Cost Efficiency

5) StreamSets

Elasticsearch ETL Tools- StreamSets Logo
StreamSets Logo

StreaSets Data Collector is an open-source software using which you can build enhanced data ingestion pipelines for Elasticsearch. These pipelines can adapt automatically to changes in schema, infrastructure, and semantics. It can clean streaming data and handle errors while data is in motion. There can be multiple unwanted changes in data streams called data drifts, which affect the quality of data and StreamSets lets you build reliable pipelines to combat that. 

Unique Features of StreamSets Logo

  • StreamSets Data Collector (SDC) supports up to 40 database sources
  • It provides support through the Zendesk ticketing system
  • It also comes with 50 pre-load transformation processors

6) CloverDX

Elasticsearch ETL Tools- CloverDX Logo
CloverDX Logo

You can work with Elasticsearch as a source or as a destination and transform the data using the CData JDBC Driver for Elasticsearch. The JDBC Driver lets you use CloverDX’s transformation components. CloverDX is a Java-based tool for automation of data integration, offering a highly scalable and available, distributed environment. 

Unique Features

  • Ease of use
  • Has the capability to pull data from multiple sources
  • High-speed data processing
  • Automates data jobs, boosts the productivity of teams
  • It provides an added layer of trust

7) Transporter

The Transporter from Compose serves as a data pipeline designed to transform and move data from various sources to data warehouses. It is an open-source Extract, Transform, Load (ETL) tool to support specific versions of Elasticsearch. Once data is extracted, the Transporter adaptor converts it into a message format, sending these messages to sinks where they undergo conversion into files that are subsequently written to Elasticsearch.

Unique features of Transporter

Leveraging Transporter’s built-in capabilities, such as goja, you can incorporate precise processes and transformations directly into in-stream data.

Transporter stands out as an advanced ETL tool designed for pro-level users, operating within a development environment and necessitating familiarity with Git commands. It proves to be an excellent choice for those who are at ease with a more technical and code-intensive approach.

Need for data integration from Elasticsearch to a data warehouse

Organizations might perform Elasticsearch ETL for various reasons:

  • Business Intelligence: Organizations often engage in Elasticsearch ETL to transfer data into a data warehouse, enabling analysis, reporting, and business intelligence endeavors.
  • Data Consolidation: The need to consolidate Elasticsearch data with other systems or applications arises to achieve a more comprehensive understanding of business operations.
  • Compliance: Specific industries may impose data retention or compliance regulations, leading to the extraction of data from Elasticsearch for archival purposes.

In essence, ETL from Elasticsearch empowers organizations to harness data for diverse business applications, encompassing integration, analytics, compliance, and performance optimization.

Criteria for Selecting the Right Elasticsearch ETL Solution

The major goal of any organization is to avoid using separate data integration tools for each data source, necessitating a clear integration strategy and well-defined evaluation criteria for choosing the appropriate Elasticsearch ETL solution. The following recommendations outline key criteria to consider:

  • Connector Coverage:
    • Evaluate whether the ETL tool can extract data from various systems, including cloud applications, REST APIs, relational databases, NoSQL databases, CSV files, etc.
    • Assess if it supports exporting data to the required destinations, such as data warehouses, databases, or data lakes.
  • Connector Extensibility:
    • Examine the ease with which connectors can be edited to add missing endpoints or address issues.
  • Ability to Build New Connectors:
    • Consider whether the solution allows the creation of new connectors, as some data integration tools have limitations on supported data sources.
  • Support of Change Data Capture:
    • Prioritize solutions that offer robust support for change data capture, especially concerning databases.
  • Data Integration Features and Automation:
    • Evaluate features such as schema change migration, historical data re-syncing, and scheduling capabilities.
  • Efficiency:
    • Assess the user interface’s ease of use, including graphical interfaces, APIs, and command-line interfaces if required.
  • Integration with the Stack:
    • Check how well the ETL solution integrates with other tools like dbt, Airflow, Dagster, Prefect, etc.
  • Data Transformation:
    • Ensure the tool facilitates easy data transformation, potentially supporting complex transformations, perhaps through integration with dbt.
  • Level of Support and High Availability:
    • Consider the helpfulness and responsiveness of the support team.
    • Examine the average percentage of successful syncs for the required connectors.
  • Data Reliability and Scalability:
    • Look for recognizable brands using the ETL tool as an indicator of its scalability and reliability for high-volume data replication.
  • Security and Trust:
    • Verify the level of certification (e.g., SOC2, ISO) to ensure compliance with security standards.
    • If expanding to Europe, prioritize GDPR compliance to maintain trust and avoid potential legal issues related to data leaks.

Conclusion

In this blog, you have looked at some of the best Elasticsearch ETL Tools. All of them are great, but some require you to do a lot of coding. And, if you are looking for a tool that can seamlessly handle your organization’s ETL needs for Elasticsearch; Hevo Data is the right choice for you!

By choosing Hevo Data to cater to your business needs, you can save a lot of engineering bandwidth and resources. Hevo will not only help set up a secured data pipeline but also simplify the Data Management, Data Analysis, and Data transformation processes.

Hevo comes with a complete feature suite including automatic schema management, real-time monitoring, and alerts that will make your Elasticsearch ETL journey easier and hassle-free.

Learn more about Hevo’s integration with Elasticsearch

Want to take Hevo for a spin? Sign up for a 14-day free trial and see the difference yourself!

And, don’t forget to share your list of the best Elasticsearch ETL Tools in the comments section below!

Nikhil Annadanam
Freelance Technical Content Writer, Hevo Data

Nikhil specializes in freelance writing within the data industry, delivering informative and engaging content related to data science by blending his problem solving ability.