Companies like GitHub and The Guardian manage staggering amounts of data daily, from millions of code repositories to tens of millions of documents. Elasticsearch powers their ability to search, index, and analyze this data quickly, going far beyond simple full-text search to enable complex queries, filtering, and real-time insights.
But data rarely stays in one place. Organizations often need to move data from multiple sources into Elasticsearch, or extract it for reporting and analytics in other platforms. Efficient and reliable ETL (Extract, Transform, Load) tools are essential to automate these workflows, handle schema changes, and maintain data quality at scale.
In this guide, we explore the best ETL tools for Elasticsearch in 2025, diving into their integration capabilities, key features, pros, cons, and pricing to help you choose the right solution for your data pipelines and analytics needs.
Key Takeaways: Top 5 Elasticsearch ETL Tools- Hevo Data – Stands out with fully automated pipelines and real-time monitoring, letting your team focus on insights instead of managing data flow.
- Pentaho (PDI) – Excels at bulk indexing and JDBC access, making it perfect for complex data transformations and reporting from Elasticsearch.
- Fivetran – Shines with smart incremental syncing and automatic schema drift handling, ideal for high-volume, dynamic datasets.
- Matillion – Its visual, low-code interface and cloud-native architecture speed up ETL development while supporting advanced transformations.
- StreamSets – Handles “data drift” seamlessly with adaptive pipelines and in-flight transformations, keeping Elasticsearch indexes accurate and reliable.
Table of Contents
10 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 over 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 Elasticsearch’s indexing system 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.
- Alerts 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 with live chat and comprehensive documentation.
- 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
Pricing Model
- Hevo offers a 14-day free trial for new users.
- Paid plans are subscription-based, starting at $239 per month for the Starter plan, scaling with the number of rows processed and additional features.
- Enterprise plans are custom-priced based on organization size, usage, and advanced requirements.
2. Pentaho
Pentaho Data Integration (PDI) stands out for its strong integration with Elasticsearch, making it ideal for ETL and analytics workflows. It allows organizations to extract data from multiple sources, transform it, and efficiently load it into Elasticsearch. PDI’s visual, drag-and-drop interface simplifies complex ETL pipelines while maintaining high performance, even with large datasets.
A key strength is the Elasticsearch REST Bulk Insert step. This feature uses Elasticsearch’s Bulk REST API to quickly index large volumes of data. Users can map Pentaho stream fields directly to Elasticsearch index fields, streamlining data ingestion. Additionally, Pentaho supports JDBC connections for retrieving Elasticsearch data into PDI transformations or Pentaho reports, enabling detailed analysis and reporting from your indexed data.
Pentaho also supports diverse use cases like ETL, reporting, and real-time data streaming. You can configure server details, authentication, and required plugins while ensuring version compatibility for seamless integration. Whether loading batch data or streaming near real-time data for analytics, Pentaho provides a flexible, high-performance solution for organizations leveraging Elasticsearch.
Key Features
- Broad Connectivity: Connect to relational databases, cloud platforms (AWS, Azure, GCP), big data systems, and enterprise apps.
- Flexible Engines: Switch between Pentaho’s native Kettle engine or Spark to handle varying data volumes and complexities.
- Operational Reporting: Generate scalable, pixel-perfect reports accessible across the organization.
- Ad-Hoc Analysis: Enable users to explore data beyond predefined metrics for deeper insights.
- Responsible AI: Manage data for AI-driven decision-making while maintaining ethical standards.
Pros
- Supports scalable and distributed processing for large datasets.
- Offers robust security, compliance, and governance features.
- Flexible execution engines.
Cons
- Enterprise features can get expensive for large deployments.
- Learning curve for complex transformations and big data pipelines.
- Some advanced analytics and AI capabilities require additional setup.
Customer Testimonial
Pricing Model
Pentaho has a free Community Edition for basic ETL and paid Enterprise Editions with advanced features and support. A 30-day enterprise trial is available, with on-premises, cloud, or hybrid deployment options.
Its Enterprise pricing isn’t public and requires direct contact. Additional costs may include implementation, support, training, and custom integrations.
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 captures only new and changed records using CDC, avoiding full reloads. 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.
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
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.r large enterprises, Matillion offers customized solutions, so pricing can be negotiated with their team.
Customer Testimonial
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
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
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
8. Keboola
Keboola is a cloud-based ETL and data operations platform that helps teams automate data workflows and integrate multiple sources efficiently. It integrates with Elasticsearch, a distributed, multitenant full-text search engine, primarily for data export, analysis, and operational insights. With Keboola, teams can export processed data into Elasticsearch and take advantage of its advanced search, analytics, and visualization capabilities through tools like Kibana.
Keboola’s integration with Elasticsearch is highly configurable. Users can define connection details such as host, port, credentials, and SSH tunnels for secure connections. Keboola provides an Elasticsearch writer component that handles data export, supports bulk uploads for efficiency, and allows column mapping from Keboola Storage to Elasticsearch fields. This ensures data is structured optimally for analysis, reporting, or log analytics.
Beyond technical setup, Keboola empowers teams to use Elasticsearch for actionable insights. Whether performing log analytics, full-text searches, or business intelligence queries, the platform enables reliable, automated, and secure data flows. Its combination of flexibility, automation, and integration strength makes Keboola a strong choice for organizations looking to leverage Elasticsearch in their ETL pipelines.
Key Features
- Low-Code & No-Code Options: Visual builder and coding support for SQL, Python, R, dbt, and more.
- Data Hub: Centralizes data from disparate sources for streamlined integration and management.
- Version Control & Sandboxes: Built-in features for branching, sandboxing, and tracking changes simplify pipeline management.
- AI-Ready Platform & Workspaces: Supports Python, R, Julia, and MLflow for machine learning deployment.
- Centralized Governance & IAM: Robust access controls, secure isolation, and identity management for compliance.
Pros
- Extensive pre-built connectors, including Elasticsearch, simplify integration.
- Supports both technical and non-technical users with low-code/no-code interfaces.
- Automated ETL/ELT pipelines with real-time CDC and self-healing infrastructure.
- AI-ready platform with support for ML workflows and data science workspaces.
Cons
- Can be complex to configure for very large, highly customized pipelines.
- Pricing can scale quickly for enterprise workloads.
Customer Testimonial
Pricing Model
Keboola’s Free Plan offers 120 minutes the first month, unlimited ETL/ELT workflows, SQL/Python transformations, 250 GB storage, and an Extra Small Snowflake backend. Extra minutes cost $0.14 each.
Paid Plans start with Data Craft (€49/month) and Data Teams (€79/month), adding advanced features like pipelines, data science workspaces, orchestration, and dedicated support. Its Enterprise plans are custom.
9. Talend
Talend provides robust ETL capabilities to integrate with Elasticsearch, enabling extraction, transformation, and loading of data while supporting logging and analytics. Its components, like tElasticSearchConfiguration, tElasticSearchInput, and tElasticSearchOutput, allow users to connect to clusters, read data, and write or update documents efficiently. Lookup and advanced query operations are also supported through tElasticSearchLookupInput.
The Talend Cloud Data Management Platform and Talend LogServer enhance integration by improving data quality, governance, and centralized logging. Talend can integrate Elasticsearch data into cloud data platforms or data warehouses for analytics, enabling unified reporting and insight generation.
When working with Elasticsearch, Talend users should consider version compatibility, performance optimization, and security. Proper resource allocation and monitoring with LogServer or the ELK stack ensure smooth operation, making Talend a reliable choice for scalable and governed Elasticsearch ETL workflows.
Key Features
- Data Integration: Connects to 900+ sources, transforms data, and maps it to a single source of truth.
- Master Data Management (MDM): Manage and master reference data across domains.
- Graphical Design Environment: Build ETL jobs visually in Talend Studio without heavy coding.
- Big Data & Virtualization: Integrates with Spark/Hadoop, and you can query data without moving it physically.
- Collaboration & Governance: Shared repositories, Git integration, metadata management, and continuous integration for auditing and testing.
Pros
- Complete end-to-end data lifecycle coverage.
- Visual interface reduces coding complexity.
- Strong team collaboration and governance tools.
Cons
- A wide feature set can be overwhelming for beginners.
- Enterprise licensing costs can be high.
Customer Testimonial:
Pricing Model
With Talend, you pay for the platform that helps you connect, manage, and move data from Elasticsearch. Pricing depends on your plan and team size.
Elasticsearch, now Elastic, is billed separately. You can choose Serverless, Cloud-hosted, or Self-Managed options. For exact costs, check the Elastic Cloud page or contact sales.
10. Stitch Data
Stitch stands out for its seamless integration with Elasticsearch, allowing businesses to easily move data from multiple sources directly into Elasticsearch clusters. This integration ensures that data from SaaS apps, databases, and other platforms is automatically structured and updated, ready for analytics, reporting, or search without extra manual work.
With Stitch, you can set up ETL pipelines that feed Elasticsearch in real time or via scheduled batch updates. It handles incremental updates, schema changes, and automated replication, making it easy to maintain accurate, consistent, and query-ready data. Teams can focus on analyzing data rather than managing the pipelines or dealing with infrastructure complexities.
The integration also mirrors Elasticsearch’s own data consolidation capabilities. By “stitching” data streams from various sources into unified indices, Stitch enables powerful search and analytics. Companies like Stitch Fix use these capabilities to optimize complex queries, demonstrating how Stitch’s integration with Elasticsearch can improve performance and simplify large-scale data workflows.
Key Features
- Certified connectors ensure reliable, scalable movement into Elasticsearch.
- Cloud-native platform designed for efficient cloud-to-cloud and hybrid integrations.
- Automated pipelines manage extraction, loading, and syncing to Elasticsearch.
- Encrypts data in transit (TLS) and at rest (AES) for full protection.
- Prepares data in ready-to-query schemas for Elasticsearch analytics.
Pros
- Quick, low-maintenance setup with automated ETL pipelines.
- Handles incremental updates and schema changes automatically.
- Strong security with encryption, access controls, and HIPAA support.
Cons
- Limited advanced transformation capabilities compared to some full-feature ETL platforms.
- Real-time streaming is supported, but extremely low-latency use cases may need additional tools.
- Pricing can become higher as data volume scales.
Pricing Model
Stitch Data offers a 14-day free trial to test its features. Paid plans start with Standard at $100/month, Advanced at $1,250/month, and Premium at $2,500/month, which includes enhanced support and compliance options like HIPAA.
Enterprise plans provide custom pricing for large organizations. Costs depend on data volume, features, connectors, and billing cycle, with annual plans often discounted.
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 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
1. Is Elasticsearch an ETL tool?
No, Elasticsearch isn’t an ETL tool. Think of it like a supercharged library for your data, where you can store, search, and analyze huge volumes of information instantly, but it doesn’t handle the extraction or transformation part.
2. Is Logstash an ETL tool?
Yes, Logstash is an ETL tool in the Elastic Stack. It extracts data from sources like logs or databases, transforms it with parsing or filtering, and loads it into Elasticsearch. For example, a website can use Logstash to clean user activity logs and send them to Elasticsearch for real-time dashboards.
3. What is the best tool for Elasticsearch?
It depends on your goal. Logstash handles ETL, Kibana visualizes data, and Beats ships lightweight data. Together, they form a powerful combo for managing, analyzing, and presenting Elasticsearch data efficiently.
4. How do you pull data from Elasticsearch?
You can pull data using Elasticsearch’s RESTful API. For instance, a simple query can retrieve all customer records stored in Elasticsearch, which you can then use in dashboards, reports, or downstream analytics.
