7 Best Elasticsearch ETL Tools in 2025

Ever wondered how companies like GitHub index over 8 million code repositories, comprising more than 2 billion documents? How does The Guardian process 40 million documents daily and have over 360 million searchable documents? They use Elasticsearch, which is a mighty search engine and analytics tool that companies rely on as a data source. It enables them to go beyond simple full-text search and perform complex operations to access, collect, index, and filter vast troves of data with precision.

But even with Elasticsearch’s immense capabilities, data rarely lives in isolation. Organizations frequently need to extract insights from Elasticsearch for analysis in other business intelligence platforms. They also need to pull data from various sources into their Elasticsearch data warehouse. These critical data movements require reliable ETL (Extract, Transform, and Load) tools to ensure data flows efficiently in and out of storage.

To help you decide, we’ve compiled a list of industry-standard ETL tools that excel with Elasticsearch. We’ll dive into their key features, weigh their pros and cons, and help you choose the one that best aligns with your use case.

Quick Summary of Elasticsearch ETL Tools

Here is a quick comparison of top three tools:

7 Elasticsearch ETL Tools List for Better ETL Processing

1. Hevo Data

Hevo Data is one of the leading cloud-based ETL platforms that provides a no-code interface to its users to stream data from more than 150 data sources to target destinations. Hevo provides ETL for Elasticsearch as well. It is very easy to set up an ETL pipeline in Hevo with three easy steps: select the data source, provide valid credentials, and choose the destination.  It’s a fully automated platform, designed to minimize manual intervention so your team can focus on deriving insights, not managing data plumbing.

Technically, Hevo connects to your Elasticsearch cluster (supporting both Generic Elasticsearch and AWS Elasticsearch variants) using the Elasticsearch Transport Client. It then efficiently synchronizes the data available in your cluster to your preferred data warehouse, leveraging indices for optimal performance.

So, if you’re seeking a low-maintenance solution for your Elasticsearch ETL needs while ensuring top-notch data consistency and security, Hevo Data makes a strong case as your go-to tool.

Key Features

• Elastic Search Exceptions parsing: Hevo parses Elasticsearch exceptions that prevent memory issues and recommends corrective actions.
• Catches AWS Elasticsearch circuit breaker errors: Hevo catches AWS Elasticsearch circuit breaker errors, which stop operations exceeding JVM (Java Virtual Machine) memory limits, and recommends corrective actions.
• Alert and Monitoring: You can monitor your ETL pipeline health with intuitive dashboards showing every pipeline stat and data flow. You also get real-time visibility into your CDC pipeline with alerts and activity logs.
• Automated Schema Management: Whenever there’s a change in the schema of the source database, Hevo automatically picks it up and updates the schema in the destination to match.
• Security: Hevo complies with major security certifications such as HIPAA, GDPR, and SOC-2, ensuring data is securely encrypted end-to-end.

Pros

• 24×7 Customer Support – Live chat with around-the-clock assistance and thorough support documentation is available.
• No Technical Expertise Required
• Supports data transformations through a drag-and-drop interface and Python code-based transformations as well.

Cons

• Only Native Realm authentication is supported.
• Hevo currently does not support deletes. Therefore, any data deleted in the source may continue to exist in the destination.
• Hevo does not support the replication of hidden objects.

Customer Testimonial

Elastic stack gives us the ability to aggregate logs from all our systems and applications, analyze these logs, and create visualizations for application and infrastructure monitoring, faster troubleshooting, security analytics, and more. It is very easy to use and implement, and it easily gets integrated with various other tools in Cyberspace.

Verified User

Information Technology and Services

Pricing Model

Logstash is open-source and free to download and use without direct software license fees. Costs arise from the infrastructure (servers/VMs) needed to run it and personnel for configuration and management.

3. Fivetran

Fivetran is a cloud-based, automated data movement platform that also provides ETL for Elasticsearch. Fivetran supports more than 650 connectors. As a cloud-native tool, Fivetran extensively uses on-demand parallelization, which powers its performance.

Fivetran supports two types of Elasticsearch services: Elastic Cloud and Self-Hosted Elasticsearch. It is compatible with Elasticsearch versions ranging from 7.10.0 to 8.x.

There are no limits on the number of connections per database, allowing for flexible scaling. Fivetran supports Transport Layer Security (TLS) versions 1.1 through 1.3 for secure connections.

Once connected to your Elasticsearch instance, Fivetran fetches all historical data and keeps it up-to-date by syncing only the most recent inserts and updates. This is done at regular intervals using the sequence number and version fields on the documents. Additionally, deleted data is captured using Fivetran Teleport Sync.

If you are looking for a reliable system with strong data governance policies and anticipate heavy data volumes in your ETL pipeline, then Fivetran is your ideal tool.

Key Features

• CDC to achieve incremental updates: Fivetran uses change data capture (CDC) to achieve incremental updates, which ensures minimal disruption to the source system.
• Idempotence: Idempotence in Fivetran ensures that a data connector can recover from failed syncs by allowing the same data to be applied multiple times without causing duplicates. If a sync fails, the connector can replay the data; if a record already exists, it has no effect; otherwise, the record is added.
• Schema drift handling: Whenever there is a change in the source, Fivetran automatically detects and updates it in the destination database.
• Minimizing latency and performance bottlenecks: Fivetran accomplishes this through algorithmic optimization, parallelization, pipelining, and buffering.

Pros

• Minimal setup with automated pipeline management.
• Wide range of connectors for SaaS applications and databases.
• Scalable for high-volume data processing.

Cons

• Unmapped fields in an index are not supported for Elasticsearch.
• Indices with dynamic fields set to off may cause sync failures.
•  Elasticsearch field names are case-sensitive, but columns in Fivetran are case-insensitive.
• Pricing may become challenging as data usage scales. (Source)

Pricing Model

Fivetran offers four pricing plans: Free, Standard, Enterprise, and Business Critical. Pricing is based on Monthly Active Rows (MAR) and plan features. Fivetran has recently changed its pricing model. Check out the Fivetran Pricing Model Update for a detailed insight.

Customer Testimonial

Most of the older connectors are reliable -- consistent data, a consistent data delivery schedule, easy setup, implementation, and integration.

Eric A.

Chief Data Officer

4. Matillion

Matillion is a prominent cloud-native ETL/ELT platform designed to help organizations efficiently move and transform data. It’s particularly well-regarded for its integration with modern cloud data warehouses and its visual, low-code approach to building data pipelines. 

Matillion provides Dedicated Elasticsearch Components. It includes built-in components to extract data from and load data into Elasticsearch. The built-in components connect securely to Elasticsearch and offer users flexible options to specify indices, full custom queries, or even update document IDs when running updates/upserts.

If you’re already invested in or moving towards a cloud data warehouse ecosystem and your team prefers a visual, low-code development environment but needs the option for advanced customization (like Query DSL), then Matillion is your go-to Elasticsearch ETL solution.

Key Features

• Visual Pipeline Orchestration: Matillion provides a graphical interface to design, build, and manage ETL pipelines, reducing the need for extensive coding for many everyday tasks.
• Rich Transformation Capabilities: While data is in the Matillion environment (often staged in a cloud data warehouse), users can leverage Matillion’s extensive suite of transformation components (join, filter, aggregate, pivot, custom SQL, Python/Javascript scripting, etc.) to cleanse, reshape, and enrich data before loading it into Elasticsearch or after extracting it.
• Cloud-Native Architecture: Matillion is built for the cloud (often deployed via cloud marketplaces like AWS, Azure, GCP) and designed to scale with cloud infrastructure.
• Scheduling and Automation: Pipelines can be scheduled to run at regular intervals, automating the data flow to and from Elasticsearch.
• Parameterization and Variables: Allows dynamic pipeline execution, making workflows reusable and adaptable.

Pros 

• The visual interface and pre-built components can significantly speed up the development of Elasticsearch ETL pipelines, especially for users less familiar with coding.
• You can perform complex data manipulations before loading into Elasticsearch or after extracting from it.
• The “Advanced Mode in the Elasticsearch Query component gives power users the full capabilities of Elasticsearch’s query language for precise data extraction.
• Leverages the scalability of the underlying cloud platform.

Cons

• Matillion is great for batch-loading Elasticsearch, but not for ultra-low latency, where event-by-event streaming is directly into it.
• Mastering complex transformations, advanced Query DSL within Matillion, or intricate pipeline orchestration can require a lot of technical acumen.

Pricing Model

Matillion organizes its pricing around four tiers. The first tier is for individuals, and they also typically operate on a pay-as-you-go model. For businesses that need higher abilities, the pricing for the following tiers starts at roughly $1,000 a month. For large enterprises, Matillion offers customized solutions, so pricing can be negotiated with their team.

Customer Testimonial

What I like best about Matillion is its seamless integration with major cloud platforms like AWS, GCP and Azure. This is very user friendly platform for ETL. It\'s visual interface makes complex workflows look easier. It offers great scalability, making it suitable for big and small scale users. It helps to reduce the complexity of ETL Process with its no code working ability.

Nikhil L.

Data Engineer

5. SteamSets

StreamSets Data Collector is an open-source software that you can use to 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 the data is in motion. 

There can be an accumulation of numerous unanticipated changes that occur in data streams called data drifts. By being resistant to “data drift”, StreamSets minimizes ingest-related data loss and helps ensure optimized indexes so that Elasticsearch and Kibana users can perform real-time analysis with confidence.

Key Features

• In-Flight Data Preparation with Pre-built Functions: Streamsets provides a large library of processors that can apply various transformations, such as field parsing, type conversion, and sensitive data masking (PII). 
• Visual Pipeline Design & Connections: Streamsets provides a simple drag-and-drop interface to design data flows visually to easily connect different data sources and stream them into Elasticsearch without extensive coding.
• Conditional Data Routing & Advanced Error Handling: You can use Streamsets’ conditional logic to route records based on pre-defined conditions, including routing unexpected values or processing errors to an error queue or different stream for future action as part of data governance.
• Python SDK for Pipeline Automation & Management: Streamsets provides Python SDK to programmatically create, deploy, and manage a large volume of data pipelines, streamlining operations at enterprise scale.

Pros

• StreamSets provides a wide array of APIs for extensibility and customization.
• StreamSets Data Collector (SDC) supports up to 40 database sources.
• It also comes with over 50 pre-load transformation processors.

Cons

• Performance Issues with large data volumes.
• Documentation lacks clarity.

Pricing Model

StreamSets Data Collector (SDC) is free, as it’s open-source. For advanced enterprise capabilities, centralized control, and support, StreamSets offers a commercial platform with subscription-based pricing. Operational costs for infrastructure will apply in both scenarios.

Customer Testimonial

I like about streamsets is how it makes it easy in the use cases of AI, where you can do the continuous training process.

Vasstav K

AI Intern

6. Apache NiFi

Apache NiFi is a flexible and powerful open-source platform for automating system data flow. While it does not strictly fall into the category of a traditional ETL tool, its data route and transformation capabilities, and system mediation capabilities make it a very valuable tool to build complex data pipelines, such as for Elasticsearch. 

Additionally, it uses an ETL architecture based on the principle of Flow-Based Programming. Data flows through a series of “Processors”, each performing specific functions, such as pulling data, transforming it, routing it based on its contents, or sending it to a destination. NiFi includes dedicated processors for interacting with Elasticsearch, including sending data to (PutElasticsearch) Elasticsearch and pulling data from (e.g., ScrollElasticsearchHttp, QueryElasticsearchHttp) Elasticsearch clusters.

If your organization is seeking a configurable, scalable, and open-source solution to manage the complex flow of data into and out of Elasticsearch (particularly involving fine-grained control, data provenance, and diverse data sources), then we have strong reasons to consider Apache NiFi.

Key Features

• Dedicated Elasticsearch Processors:
  • PutElasticsearchHttp / PutElasticsearchRecord: This is used to index data into Elasticsearch, support bulk operations, dynamic index/type naming, and a variety of authentication mechanisms.
  • ScrollElasticsearchHttp / QueryElasticsearchHttp: This is for fetching data from Elasticsearch using scroll APIs or working with Query DSL.

• Data Provenance: Captures and tracks data provenance as it moves through the flow, and creates a detailed audit trail of how data was sourced, transformed, and delivered. This can be useful for debugging purposes and compliance.
• Guaranteed Delivery: Provides strategies and methods like write-ahead logs and persistent queues to ensure that your data is not lost in failures.
• Back Pressure and Pressure Release: NiFi supports dynamic flow rates that help upstream systems from overwhelming downstream systems (or vice versa)
• Security: Provides secure communication mechanisms (SSL/TLS), pluggable authentication and authorization mechanisms, and encryption of sensitive properties.
• Extensibility: Users can build custom processors using Java to enhance the overall capability of NiFi.

Pros

• Can handle almost any data routing/transformation with its extensive processor library.
• Free, open-source software with an engaged and supportive community
• Provides broad frameworks for ingesting and governing varied data sources and formats

Cons

• Requires significant, and tunable, computer resources (CPU, memory)
• Complex transformations could require many granular, chained processors.
• Primarily focused on data flow/orchestration, not deep or singular transformations.

Pricing Model

Apache NiFi operates under an open-source license from the Apache Software Foundation, making the core software completely free to download, deploy, and modify.

Customer Testimonial

The best thing about Nifi is that the tools bar is located at convenient place for the user to acces the tools. The drag and drop feature comes handy. The grid offers a perfect measure of components. DAG is represented properly by connecting arrows.

Shubham G.

Full Stack Engineer

7. Apache Spark

Apache Spark is a powerful, open-source, distributed processing system designed for big data workloads. It provides an interface for programming entire clusters with data parallelism and fault tolerance. Spark, with its Elastic Search-Hadoop (or Elastic Search-Spark) connector, can perform complex ETL operations to and from Elasticsearch, especially when dealing with large datasets.

Spark processes data in memory, which can lead to significantly faster execution. It supports various workloads, including batch processing, interactive queries (Spark SQL), real-time stream processing (Spark Streaming), machine learning (MLlib), and graph processing (GraphX). For Elasticsearch ETL, you’d primarily leverage Spark Core (RDDs), Spark SQL (DataFrames/Datasets), and Spark Streaming.

If your organization processes massive volumes of data, requires advanced analytical transformations before indexing into Elasticsearch, or already has an existing Spark ecosystem, using Apache Spark for Elasticsearch ETL can be highly effective.

Key Features

• Elasticsearch-Hadoop Connector (elasticsearch-spark): This official library enables seamless reading from and writing to Elasticsearch using Spark’s RDD, DataFrame, or Dataset APIs.
• Distributed Processing: Spark distributes data and computations across a cluster of machines, enabling massive scalability for ETL jobs.
• Rich Transformation APIs: Offers extensive libraries and APIs (Scala, Python, Java, R) for complex data transformations, aggregations, joins, and cleansing operations on DataFrames/Datasets.
• In-Memory Computation: Accelerates processing by keeping intermediate data in memory, reducing disk I/O bottlenecks.
• Spark SQL: Allows querying structured data using SQL or DataFrame API, making it easier to express complex transformations and integrate with various data sources.
• Query Pushdown: The connector can push down certain predicates and filters to Elasticsearch, reducing the amount of data transferred to Spark for processing when reading.
• Support for Batch and Micro-Batch Streaming: Spark can handle large batch ETL jobs and also near real-time data ingestion into Elasticsearch using Spark Streaming (micro-batching).

Pros

• Integrates well if you already have a Spark or Hadoop ecosystem.
• Supports multiple programming languages (Scala, Python, Java, R).
• Open-source with a large, active community and extensive documentation.

Cons

• Significant setup and operational complexity for Spark clusters.
• Can be resource-intensive, requiring substantial memory and CPU.
• Spark Streaming is micro-batch, not true event-at-a-time streaming.

Pricing Model

Apache Spark itself, as an open-source software project under the Apache Software Foundation, is free to download, use, and modify. You do not pay a license fee for the Apache Spark software itself.

However, running Apache Spark in a production environment incurs costs related to the infrastructure and resources it utilizes.

Customer Testimonial

I have used spark for data processing purpose the thing that I like the most is the speed , it process huge amount of data because of in memory computation which is very better a compare to Hadoop map reduce

Richa A

Senior Engineer

Key Factors in Choosing the Best Elasticsearch ETL Tool

1. Real-Time Capabilities

The Elasticsearch instance thrives on fresh data. Can your ETL tool deliver it in real-time, without any sync errors? Does it offer robust Change Data Capture (CDC) to snag incremental updates as they happen instantly? In today’s world, analytics and search demand now, not “later.” If your ETL tool makes Elasticsearch wait, it undermines its very purpose and leaves your insights stuck in the past.

2. Minimal Maintenance

You choose an ETL tool to save your team’s effort, not add another time-sink. How much ETL automation is needed for the tool’s configuration? Does it require minimal setup and ongoing maintenance? Since you are going for an external ETL tool for your Elasticserch data, you must also ensure you don’t put too many internal resources into its setup and maintenance. Prioritize solutions boasting intelligent automation, a setup that’s more plug-and-play than pain, and low ongoing maintenance.

3. Transformation Capabilities 

Elasticsearch handles complex data types, from nested JSON to geospatial information. But can your ETL tool expertly transform these before they hit your index? Seek out powerful in-flight transformation capabilities. If your data needs reshaping, cleansing, or enriching (especially those tricky geospatial coordinates or custom structures!), your ETL tool must be capable, not a bottleneck.

4. Technical Flexibility

How does your team like to roll? Do they prefer a slick no-code, drag-and-drop interface? Or the raw power and customizability of an open-source solution? Or a hybrid approach? Investigate how easily you can wield the tool: through an intuitive GUI, an API for seamless automation, or a command-line interface. 

5. Justified Pricing

Does the tool’s price tag genuinely reflect the value, features, and support it brings to your Elasticsearch operations? Choose a partner whose cost makes sense for the power it delivers.

What is Elasticsearch?

Elasticsearch is an open-source, distributed engine that doesn’t just store data; it ignites it. Elasticsearch powers search and analytics with incredible speed and scale, ready for modern AI.

This digital powerhouse ingests structured and unstructured data, sprawling text, and complex AI vectors in real time and stores them in JSON. When you add documents, it instantly builds a smart reverse index. Elasticsearch delivers blazing-fast hybrid and vector searches. It performs robust full-text queries in near real-time. It operates as a distributed cluster.

This design fuels:

• Smarter AI: It powers intelligent applications with precision.
• Clear Observability: It helps you understand complex systems instantly.

Elasticsearch turns raw data into sharp, actionable intelligence. Developers can control it using robust APIs and change structures during runtime. Elasticsearch isn’t mere storage; it’s your high-speed map to information.

Conclusion

In this blog, we have examined a range of tools for Elasticsearch ETL with different expertise, each with its unique value proposition. As you search for a tool, remember there is not one “best” answer; it is whichever tool that most closely meets your operational expectations, technical expectations, data workloads, and budgets.

As you sift through options, if you are looking for a powerful yet incredibly easy-to-use platform to help you move data into Elasticsearch in a secure, automated, and low-maintenance fashion, then Hevo Data is your best choice.

Hevo is built to give your engineering team more bandwidth and resources. Hevo will allow you to create encrypted data pipelines fast while simplifying overall data management, analysis, and transformation operations. 

Learn how to connect Elasticsearch to MySQL for effective data synchronization and management.

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

Learn more about Hevo’s integration with Elasticsearch

FAQs about Elasticsearch ETL Tools