Best Estuary Alternatives to Consider in 2025

“Is Estuary still the best tool for my data workflows in 2025?”

If that’s your question, keep reading!

As data engineers, developing and maintaining data pipelines that are fast, reliable, and scalable is a hands-on process. You must be using Estuary to move, transform, and synchronize data until now. 

While Estuary is good at setting up connectors and replicating data in real-time, it does not fit the bill when it comes to a complex connector ecosystem. This is when choosing the right ETL tool becomes necessary.

In this article, we help you choose a scalable, flexible, and faster Estuary alternative to unleash your team’s optimal productivity. Compare the pros, cons, use cases, and price models of the latest tools to choose the best fit for your data workflows.

Let’s get started!

Estuary Overview

Estuary is a cloud-based platform specializing in real-time ETL and ELT data pipelines. Unlike traditional ETL tools, Estuary automates the data collection process and loads it into data warehouses for further analytics. This enables teams to access necessary data for executing operations.

Specifically, Estuary helps SMBs manage and consolidate data without requiring large IT teams. Teams leverage automation and real-time abilities to back up data and access actionable insights. It transports data with comparatively low latency while guaranteeing accuracy, thus striking a balance between speed and reliability.

Moreover, it supports 200+ data source connectors to ensure hassle-free integration with databases, cloud-based storage, and SaaS applications. The tool is well-suited for SMBs and data teams using SaaS applications.

Why Are People Moving Away from Estuary?

With growing data integration needs, users are reconsidering Estuary as their go-to platform and turning to Estuary alternatives. The key limitations include:

Learning curve

Users often experience a steep learning curve when transitioning from traditional ETL tools to new integration solutions. The tool’s advanced real-time streaming functionality and technological terminologies frequently confuse users.

One of the user’s reviews says: “In my experience with Estuary Flow, while the majority of the user interface is intuitive and user-friendly, I did find that certain aspects required a learning curve initially.”

Limited ecosystem

Estuary doesn’t use widely accepted streaming platforms like Kafka, it has built its streaming infrastructure to establish workflows. As teams are unfamiliar with new ecosystems, onboarding becomes slow and encourages users to access third-party resources.

A user on G2 says: “Sometimes the documentation can be a bit hard to follow for more complex connectors, but can typically figure it out within a few minutes.”

Connector limitations

Although Estuary features over 200 pre-built ETL connectors, it falls short when dealing with a broader array of connectors. The limitations of the connector library and insufficient focus on batch processing and legacy system integrations create resistance and give an operational advantage to Estuary competitors.

Cost

Although Estuary’s per-GB pricing is transparent and based on data moved, users with unpredictable streaming data volumes have expressed concerns about computing activities and unexpected costs.

A G2 review stated: “Our usage of instant data updates has resulted in some not-insignificant Snowflake virtual warehouse costs.”

Top 10 Estuary Alternatives to Consider

Here’s a quick summary of the top alternatives to Estuary:

Tool	Best For	Key Advantage	Starting Price
Hevo Data	Small and medium-sized enterprises (SMEs)	No-code UI and real-time streaming support with 150+ pre-built connectors	$239/month
Fivetran	Enterprises wanting to consolidate and automate ETL workflows	Minimal setup, fully automated with built-in security	$500 for the first million MAR
Stitch	Teams with small to medium-sized data requirements	Developer-friendly, simple UI, establish pipelines with minimal configurations	$100/month
Integrate.io	Companies that have complex ETL pipelines and transformation needs	Build low-code pipelines, easy Salesforce sync to transform and load data	$1,999/month (Fixed-fee model)
Airbyte	Teams prioritizing data flexibility and open-source use-cases	API replication at any scale builds a new connector in 10 minutes	$175/month (For 10GB, 5M rows)

List of top Estuary alternatives:

1. Hevo Data

Hevo is an acclaimed no-code data integration tool hosted on Amazon’s AWS that simplifies data integration and makes data analytics easier. The platform’s analyst-friendly features help both technical and non-technical users to set up and monitor ELT pipelines. It is fully automated and enables teams working with SaaS applications to extract, transform, and load data into BigQuery, Snowflake, and Redshift.

Unlike traditional ETL workflows, Hevo automates the entire data collection process. From collecting data from diverse data sources such as SaaS applications, storage files, and databases to processing it before loading for further analytics, Hevo’s ELT data pipelines are a perfect choice for organizations with expanding data needs.

Hevo’s standout feature is its real-time visibility into data pipelines for better monitoring. This includes detailed graphs, counts, and UI indicators like latency tracking and pipeline speed to facilitate quick resolution. Its automated data recovery mechanisms and tracking tools ensure data integrity and prevent data loss during downtimes, making it a reliable choice.

Key features:

• Integrators: The platform facilitates over 150+ integrations across SDKs, streaming services, and databases. This requires just a five-minute setup, after which you can replicate data from any source to your preferred data warehouse.
• Processing: Hevo supports real-time data streaming for complex datasets. Its strength is Change Data Capture (CDC) for databases to enable incremental updates.
• Workspaces: Hevo enables its existing users to create multiple workspaces under the same domain name. They can join new workspaces or create new ones with up to five teams.
• Auto mapping: Hevo replicates event types and data from the source to the destination tables and columns. Auto-mapping of event types handles future schema changes and prevents human intervention in pipeline workflows.

User review: “I love the simplicity and ease-free nature of setting up pipelines. As some members in our team who come from non-tech backgrounds have knowledge in data, this tool helps them get the work done faster without having to worry about the programming and infrastructure side of it.”

Pros:

• Loads historical data following the recent data first approach to get the latest events.
• Offers an on-demand credit option to purchase events once the quota is exhausted.
• Automated management and handling of schema upgrades.
• Provides “within-the-product” customer support through live chats.

Cons:

• Ideal for cloud-based integrations and offers limited on-premise support.
• Can experience latency issues with vast datasets.

Why choose Hevo over Estuary?

• Hevo’s intuitive interface allows pipelines to be set up faster.
• Python-based drag-and-drop transformation to clean and prepare data.
• Advanced debugging capabilities with pre-built logging and alerting mechanisms.
• Multi-tenant platform model for ensuring optimal use of resources to generate the best ROI.

Pricing:

• Starts as a free tier with limited connectors up to 1 million events
• Starter: $239/month up to 5 million events
• Professional: $679/month up to 20 million events
• Business: Custom pricing

A short case study:

Company: CoinList, a San Francisco-based cryptocurrency platform with over $3 billion in trading volume and $6 billion in assets.

Data challenges: Existing data processes couldn’t adapt to growing trading data volumes.

Hevo’s Solution: CoinList adopted AWS Redshift as its data warehouse to streamline data processing. Hevo provided a secure, scalable, and maintenance-free ETL solution, enabling real-time data synchronization across multiple sources.

Result: Accurate unification of 150 M+ events, saved 10+ hours weekly, latency to produce analytics-ready data reduced to 15 minutes.

2. Fivetran

Fivetran is a cloud-based data integration platform developed for enterprises to manage ETL workflows. The tool automates the process of extracting data from databases, ERPs, and data lakes and loading it into data warehouses. The platform’s core offering is connectors that facilitate data movement at lightning speed.

Fivetran provides pre-built data models to standardize data and ensure smooth integration with your BI and visualization tools. The platform either gathers data or sends a request to the source to push data, which Fivetran collects. This automation of data pipelines reduces manual labor, which makes it suitable for both start-ups and enterprises.

Fivetran offers a wide range of deployment models—SaaS, hybrid, and self-hosted. It is designed to handle large data volumes and has custom development options. The ability to handle schema drifts and facilitate the secure movement of terabytes of data makes it a considerable choice for organizations aiming to scale.

Key features:

• Connector library: Fivetran supports 700+ connectors that handle complex data replication scenarios. This includes SaaS applications, databases, and cloud services.
• Schema migration: Fivetran maintains data integrity by resolving issues and preserving existing datasets to ensure flow in downstream systems. This manages the connector’s schema and accommodates source data in the destination.
• Data blocking: It prevents the exposure of Personally Identifiable Information (PII) and syncs only relevant data. This is done by eliminating specific tables and blocking data replication to enhance security, save time, and storage space.

User review: “Fivetran truly simplifies the process of connecting and syncing data across systems. Its out-of-the-box connectors are very robust, requiring minimal setup and almost no ongoing maintenance.”

Pros:

• Connects data sources and destinations in minutes to foster quick deployment.
• Enables teams to replicate data from warehouses into operational systems.
• Offers robust security through its built-in recovery and error detection mechanism.

Cons:

• Expensive, as prices depend on Monthly Active Rows (MAR).
• Limited flexibility for custom connectors.
• Lacks true event-driven streaming functionality.

Why choose Fivetran over Estuary?

• Better performance for enterprise-level, large-scale batch ELT.
• Handles destination failures through a set of progress cursors while syncing data.
• Requires minimal setup and auto-handles schema upgrades to reduce operational burden on data teams.

Pricing:

Based on MAR, which means you are charged depending on the number of unique rows you update, insert, or delete in a month. You can try their 14-day free trial plan to experience the platform.

3. Stitch

Stitch is one of the cloud-native, open-source ETL tools that automate ETL workflows across various data sources. It replicates the sourced data into a centralized data warehouse with minimal engineering effort. The platform seamlessly integrates with databases like MySQL, MongoDB, and SaaS applications like Zendesk and Salesforce. 

Stitch extracts data and stores it in data pipelines through the import API system. The stored data is transformed into raw and useful datasets to make it compatible with your preferred destination. Automating data pipelines and replication jobs makes it suitable for data teams who want to avoid complex coding and facilitate effortless analytics.

Stitch fulfills batch-oriented and scalable integration needs without requiring an additional workforce. The platform is fully managed and easy to set up, without requiring infrastructure management. It functions as a batch ELT tool that extracts and loads data at scheduled intervals.

Key features:

• Security: Stitch’s servers are hosted in AWS, which complies with SOC 2 Type II certification, ISO/IEC 27001, GDPR, and CCPA. It operates within Amazon’s VPC, which has specified security levels and firewalls to monitor network access.
• Connectors: Stitch’s integration uses the Singer tool to connect with 140+ pre-built data sources. It replicates data from diverse sources to your preferred destination.
• No-code: Its user-friendly, no-code interface allows users with limited technical expertise to set up and manage data pipelines quickly, reducing the need for custom coding or extensive IT support.

User review:

“Easy to set up, straightforward configuration, connects with most major sources (databases) and destinations (data warehouses). Not a lot of fuss to get going with a straightforward CDC setup.”

Pros:

• Additional analytics tools to enable data visualization and BI.
• Provides detailed documentation and personalized guidance.
• Has a usage-based pricing model that ensures transparent pricing.

Cons:

• Falls short on real-time data streaming needs.
• Users often experience slow and unresponsive customer support.
• Offers basic data transformation, but requires Talend integration for extensive data transformation.

Why choose Stitch over Estuary?

• Deferring transformations to the warehouse enables engineers to leverage native SQL and dbt workflows, eliminating the need for complex custom code.
• As part of Talend, Stitch ensures high standards of data governance and broader data lifecycle management.
• The row-based subscription model gives small and mid-sized firms a detailed view of costs and scales linearly with usage.

Pricing:

• Standard: $100 monthly
• Advanced: $1,250 monthly
• Premium: $2,500 monthly

4. Integrate.io

Integrate.io is a fully managed data pipeline tool for analysts to streamline ETL, API integration, and data observability workflows in a low-code cloud-based workspace. The platform’s UI enables non-engineers to easily establish transformations like select, clone, limit, filter, sort, join, etc. It excels at syncing data in real-time for teams who require CRON-grade orchestration and cluster auto-scaling.

Its jargon-free environment helps businesses to benefit from big data opportunities without investing in hardware and related personnel. Integrate.io ingests data from databases and SaaS apps to apply transformations and load it into popular data warehouses. The platform is suitable for data engineers who want to create, maintain, and monitor ETL pipelines without writing complex code.

The tool’s ability to refine data in real-time and keep it up-to-date enhances data replication. The process focuses on preparing analytics-ready data with minimum latency. If your primary need is to prepare, transform, and load data for business intelligence, reporting, or scheduled analytics, Integrate.io’s approach is more streamlined and accessible.

Key features:

• Built-in alerts: It provides an observability layer to monitor the freshness, SLA breaches, and schema drifts. Alerts are immediately launched through its built-in logging and alerting tools.
• REST API: Integrate.io features a REST API source component that makes data accessible from a variety of applications and systems.
• Interactive interface: Offers a drag-and-drop interface, which makes it easy to use and suitable for non-technical users.

User review:

“The system was easy to integrate and flexible for all of our systems. The system is reliable with little or no maintenance or intervention needed to maintain consistent data flows.”

Pros:

• Strong compliance with industry-standard security regulations and government policies.
• Facilitates seamless integration with over 150 pre-built connectors for batch processing.
• Has a dedicated customer support and solution engineer team to resolve queries while using the tool.

Cons:

• Not ideal for real-time data streaming requirements.
• Doesn’t have robust user documentation, which hinders product integration.
• The featured UI doesn’t scale with additional ETL jobs and complexities.

Why choose integrate.io over Estuary?

• The Salesforce destination component allows sourcing data from anywhere and storing it in Salesforce Sales Cloud.
• Optimized infrastructure for batch-oriented workflows.
• The fixed-fee pricing model helps in predicting costs and offers good ROI.

Pricing:

Available at $1,999 per month, offering complete platform access, 60-second pipeline frequency, and unlimited connectors. You can further customize plans and add more features to the plan.

5. Airbyte

Airbyte is a highly rated, open-source ETL platform offering extreme flexibility. It is designed to develop data pipelines and replicate data from source to destination. The tool features a centralized data management system that consolidates data from diverse sources into a single warehouse to facilitate comprehensive analytics.

Users can develop custom connectors with their no-code connector builder in a YAML format. Airbyte handles schema upgrades and incremental syncs while moving data from a source to a destination. It supports both cloud and on-premise environments to build end-to-end pipelines within minutes. Designed for teams who prioritize flexibility and cost control, so they can extend and customize data pipelines.

The open-source model allows you to switch between self-managed and cloud-managed infrastructure to complement changing data needs. It supports modern data use cases, including ETL workflows and AI/ML pipelines, and doesn’t restrict you to a single operational model. Airbyte’s open-source framework, rapid innovation, and developer-centric features make it a considerable alternative.

Key features:

• Connector variety: Offers 600+ OSS and 550+ on-cloud connectors for diverse sources and destinations. Each connector has a specified support level and is officially backed by Airbyte.
• AI acceleration: Irrespective of unstructured and semistructured data, it enables integration from any source into vector databases and LLM frameworks to accelerate AI applications.
• Deployment: Offers flexible deployment options to support every scenario. This includes:
  • Local- for small teams
  • Cloud provider- AWS, Azure, etc.
  • Managed service- Airbyte cloud

User review:

“Airbyte is very quick to get setup and straight forward to use. We went from hacked together bash scripts to having Airbyte handle our ETL processes in a matter of hours.”

Pros:

• Community-driven connector library with ongoing contributions.
• Built for horizontal scaling for both start-ups and enterprises.
• Cost-efficient plans for every business size.

Cons:

• Setting up pipelines demands prior technical expertise.
• New connectors are not ready-to-use and are under-documented.
• Self-hosted features don’t have user management solutions.

Why choose Airbyte over Estuary?

• The tool’s infrastructure can be embedded into other products, making it an optimal choice for SaaS companies.
• Provides detailed documentation and an “Airbyte Academy” training course that helps in creating a connection to any endpoint.
• Compatibility with tools like dbt, Airflow, and Kubernetes allows seamless integration.

Pricing:

Airbyte provides a free, self-hosted open-source plan as a starter pack. Followed by the Cloud plan, which follows a volume-based pricing model. However, it offers a 14-day free trial plan for Airbyte Cloud. Team and Enterprise plans are based on business needs.

6. MuleSoft Anypoint Platform

Presented by Salesforce, MuleSoft Anypoint Platform is an enterprise integration platform that offers unified solutions to establish connections across on-premise and cloud environments. It specializes in connecting disparate systems through APIs and enables organizations to build secure application networks.

The platform provides API design and management tools for testing and managing APIs. MuleSoft transforms IT assets and applications into agent-ready assets. This simplifies agent-to-agent communication and lays the foundation for safe collaborations and multi-agent systems, helping IT teams, API product managers, and operational teams.

MuleSoft’s core offering is its API-led connectivity that operates as a messenger and translator between diverse software applications. Unlike Estuary, MuleSoft offers a comprehensive integration solution that supports data transformation, workflow automation, and lifecycle management of API integration.

Key features:

• Batch processing: The batch processing framework facilitates the asynchronous processing of large datasets. The Batch Job component breaks down the source data to synchronize datasets between business applications.
• Connectors: Offers reusable extensions that integrate Mule with standard integration protocols and software applications. This reduces complexity in code generation and enables the Mule app to connect seamlessly with any target system.
• Access management: Create an Anypoint Platform account and set customized configurations to determine platform access. It features a REST API that allows programmatic access to management-related resources.

User review:

“The AnyPoint Platform from MuleSoft has tons of connectors and tools for making integration super easy, whether on-site or in the cloud.”

Pros:

• Real-time monitoring for integrations and APIs.
• Robust security and enhanced debugging tools.
• Simplified authentication against target systems.

Cons:

• Expensive for small projects.
• Requires specialized skills for implementation and management.
• Not ideally efficient for large-scale integrations.

Why choose MuleSoft Anypoint Platform over Estuary?

• Large pre-built connector library to connect with any endpoint with a low-code interface.
• Adoption of the latest Java programs (Java 17) to improve governance and language-level enrichment.
• Provides built-in dashboards to monitor deployment with vital application metrics.

Pricing:

Pricing depends on business needs and the number of additional features.

7. IBM DataStage

IBM DataStage is an enterprise-level data integration tool that supports both ETL and ELT workflows. It is a part of the IBM Infosphere Server Suite, which follows modern ELT processes to make datasets compatible for various data integration needs. Moreover, DataStage transforms data silos into AI-ready data and automates integration across diverse environments.

The primary focus is assisting data teams in executing ETL/ELT pipelines across on-premises, private, and multiple public clouds. DataStage features a Flow Designer that leverages machine learning to expedite intelligent pipeline designs, optimization, and anomaly detection. Ideal for organizations that want to design, deploy, and manage complex data pipelines at scale.

DataStage is designed to handle parallel processing and distribute workloads across hybrid and multi-cloud environments. It naturally connects with IBM Cloud Pak to modernize applications, automate processes, ensure end-to-end integration, and function as a reliable ELT tool, making it a notable contender among Estuary alternatives.

Key features: 

• Data formats: DataStage supports popular table formats like Apache Iceberg and Delta Lake to manage data warehouses and easily coordinate modern analytics and AI.
• No-code: Offers a no-code UI with multiple pre-built connectors to simplify pipeline building and move data between cloud sources and data warehouses like IBM Netezza and IBM Db2.
• Deployment: Has a containerized deployment model that promotes rapid provisioning, simplified management, and consistent environments in OpenShift and Kubernetes ecosystems.

User review:

“Integration with several data sources, data transformation capabilities and series of data quality checks and master data management abilities.”

Pros:

• Supports reusable components and version-controlled pipelines.
• Reduces cost by automating CI/CD pipelines.
• Integrates with IBM Data Fabric to move data across cloud sources.

Cons:

• Requires technical skills and training to operate.
• Expensive deployment for small organizations.
• Lack of integrated source control features.

Why choose IBM DataStage over Estuary?

• Tightly integrates with the IBM ecosystem, featuring IBM Cloud Pak for Data, AIOps, and other IBM analytics tools.
• The DataStage Debugger sets breakpoints on the ongoing job link for testing and debugging data flow.
• Provides two separate connectors, one for relational databases (data source connectors) and the other for files (file connectors).

Pricing:

• Lite: Free
• Standard: $1.828/Capacity Unit-Hour
• Enterprise: Custom packages for Small (5000 CUH), Medium (10,000 CUH), and Large (25000 CUH) enterprise bundles.

8. AWS Glue

AWS Glue is a fully managed, serverless ETL service designed to simplify and fast-track data integration. It consolidates data integration capabilities into a single service and extends flexible support for streaming and ETL/ELT workflows. This includes modern ETL, centralized cataloging, and data discovery. It is best for helping organizations operate efficiently and innovate faster.

AWS Glue leverages other AWS services for ETL workflows to develop data warehouses and data lakes. The tool expedites API operations to create runtime logs, transform datasets, and deliver notifications for monitoring jobs. It configures and allocates resources required to orchestrate workloads, making it a noteworthy solution for organizations with complex data integration needs.

Apparently, AWS operates one of the largest cloud infrastructures with a robust security framework, network firewalls, and compliance certifications. Since AWS services are tightly integrated, it ensures secure data flow between analytics, storage, and compute services. While Estuary focuses on real-time data streaming and integration, AWS goes beyond integration and presents more versatile solutions.

Key features:

• Data transformation: AWS Glue enables continuous data consumption and cleans the source data in transit. This transforms it into analytics-ready datasets within seconds in your preferred data store.
• Interactive sessions: AWS offers interactive sessions to help users explore and compose data. Using IDE, you can experiment and process data interactively to overcome ETL challenges.
• Monitoring: Use Spark or Ray engines to initiate AWS Glue jobs and monitor with built-in automated monitoring mechanisms and AWS CloudTrail. You can enhance monitoring with the Apache Spark UI.

User review:

“AWS Glue is a pretty cool tool for handling data integration and ETL tasks. It’s like having a handy assistant that takes care of all the technical stuff, so you don’t have to worry about managing the behind-the-scenes infrastructure.”

Pros:

• Effortlessly integrated with other AWS services.
• Supports connection with over 100 data sources with a centralized data catalog.
• Provides visual tools like DataBrew to increase accessibility for non-technical users.

Cons:

• Supports only two languages – Python and Scala.
• Pricing is often difficult to predict.
• Not ideal for integration beyond the AWS ecosystem.

Why choose AWS Glue over Estuary?

• AWS Glue crawlers automatically discover and organize existing and updated data sources to reduce manual efforts.
• The “AWS Glue Unified connections” develop connectors that are reusable for multiple AWS services.
• It automatically scales resources with evolving workloads and requirements.

Pricing:

• Standard ETL: $0.44 per DPU hour, billed per second with a 1-minute minimum.
• Flexible execution: $0.29 per DPU hour.
• Data catalog: Free for the first million objects/month.

9. Oracle Data Integrator (ODI)

Oracle Data Integrator is a comprehensive enterprise-grade integration tool designed to help organizations build, deploy, and manage complex data warehouses and integration projects. From large-scale, high-performance batch loads, event-driven integration processes, to SOA-enabled data services, Oracle covers it all.

ODI offers a unified solution for creating, deploying, and maintaining advanced data warehouses as a subset of data-centric architectures in SOA or BI environments. It combines the key elements of data integration and ensures accurate, consistent, and timely data across complex systems. Ideal for analysts creating data mappings through a no-code graphical interface.

The edition-based redefinition (EBR) features enable system upgrades with zero downtime. It unifies data silos by modifying large data volumes and processing events in real-time using the CDC framework. Overall, ODI fulfills flexibility, productivity, modularity, and performance criteria of an integration platform, which positions it as an eminent substitute to Estuary.

Key features:

• Integration: Integrates seamlessly with products in the Oracle ecosystem like GoldenGate, Fusion Middleware, and Enterprise Data Quality. 
• Knowledge modules (KMs): ODI leverages reusable and modular KMs as plug-ins for a variety of tasks like data extraction, Java and SQL-based transformations, and data loading into warehouses.
• Data support: ODI offers heterogeneous data support across a broad range of systems, including:
  • Relational databases (Oracle, SQL Server)
  • Cloud platforms (AWS, Oracle Cloud)
  • Big data (Kafka, Spark)
  • JSON, REST APIs

User review:

“Easy to use. Works with the cloud services. I like data integration. I like that it can be monitored from a single solution along with other Oracle technologies and applications.”

Pros:

• Pre-built monitoring tools for easy troubleshooting of complex pipelines.
• Has an enterprise-grade security framework with industry-level access control, auditing, and logging.
• Speeds up processing through parallel data loading and transformation fulfillment across nodes.

Cons:

• Often complex for new users.
• Requires substantial setup costs.
• Limited support for real-time data streaming.

Why use ODI over Estuary?

• Optimized for handling large, complex batch workloads, making it suitable for enterprises with extensive data processing needs.
• Designed to provide robust support for on-premise and hybrid environments.
• Follows a declarative design approach to simplify complex data transformations and identify workflow gaps.

Pricing:

Oracle has a traditional pricing model that finalizes pricing after a direct consultation with the sales team.

10. Matillion

Matillion is an ETL/ELT data integration platform developed specifically for cloud-based databases such as Delta Lake, Amazon Redshift, and Snowflake. The tool operates through three distinct layers: orchestration, transformation, and administration, allowing for rapid setup in seconds. It is exceptional at pushing down transformations to the cloud and processing millions of rows instantly.

The tool features a push-down ETL/ELT functionality with a modern, browser-based UI. It connects data sources to cloud platforms using Python and SQL scripts. The sourced data is transformed for BI and data science analytics. Matillion aims at improving the data lifecycle from movement to transformation, helping data engineers streamline data pipeline management.

It is specifically designed for cloud environments and leverages the scalable capabilities of platforms like Google Cloud and Azure. Matillion allows you to develop ETL jobs and initiate testing to make them production-ready. Once fully tested, you can run it in production environments. This flexibility makes it a versatile choice for teams looking for Estuary alternatives to manage data pipelines efficiently.

Key features:

• Interface: The low-code/no-code interface simplifies pipelines and supports complex data transformations. It has a user-friendly UI that makes it accessible to users with varying technical expertise.
• Connectors: Offers 250+ pre-built connectors and allows custom connectors to extract data via REST API. Connectors decide the maximum row limit that can be loaded.
• Matillion Copilot: Matillion has an AI-powered assistant that helps users craft SQL queries, document workflows, and build transformation steps. It also builds data pipelines using natural language and saves time.

User review:

“Matillion’s simple UI makes the user comfortable to configure and easy to understand the flow. And its well maintained configuration section is a big plus for this platform. So even new users can easily understand this platform’s features and design.”

Pros:

• Ideal for non-technical users to build and manage data pipelines.
• Built-in security and management mechanisms.
• Provides knowledge-based customer support and a reliable user community.

Cons:

• Managing complex data transformations is challenging.
• Not ideal for real-time data streaming and CDC solutions.
• Limitations with common data sources despite multiple connectors.

Why choose Matillion over Estuary?

• Designed for large-scale batch processing needs, making it perfect for heavy batch workloads.
• Pushes data back into operational systems through real-time data synchronization.
• Facilitates extensive authorization and authentication features to assign and remove user access to specific areas of Matillion ETL.

Pricing:

• Developer: Free
• Basic: $1,000 /month for 500 prepaid credits
• Advanced: $2,000/month for 750 prepaid credits
• Enterprise: Custom pricing

Factors to Consider When Choosing an Estuary Alternative

Here are the key factors to consider when choosing Estuary alternatives:

Processing method

Does your organization prioritize real-time streaming or batch processing? Batch processing tools are often sufficient, but if you require CDC, you’ll need a tool with strong streaming capabilities.

Setup

Find low-code/no-code tools with a user-friendly UI and minimal technical requirements. Ensure that the tool is suitable for users belonging to both technical and non-technical backgrounds.

Connector availability

Check if the platform supports your preferred data sources. A broad range of connectors helps in sourcing data from anywhere without any interruption. Also, confirm if you can build custom connectors for specific systems.

Customer support

Evaluate the responsiveness of support teams and the user community for effective learning. Also, check whether the platform provides detailed documentation that explains the onboarding process and related features properly.

Deployment

Do you need cloud-based, on-premise, or hybrid deployment? Prioritize platforms that align with your objectives and don’t require extensive resources. A versatile deployment model allows businesses to make decisions after considering their budget, regulations, and technical constraints.

Pricing model

Choose a pricing model you fully understand. Pricing based on data volumes is suitable for batch data needs, while usage-based pricing is ideal for real-time workloads.

Hevo: The No-Code Estuary Alternative Built for Speed and Scale

Choosing the right Estuary alternative can become a critical task. From ensuring scalability, handling large-scale data volumes, to executing ETL/ELT workflows, you must finalize a platform that excels at every aspect. Some tools offer custom connectors, while some provide fully automated workflows.

If you want a no-code, scalable ELT platform, Hevo Data is the right fit. Don’t just take our word. With its massive pre-built connector library, automated workflows, and built-in quality checks, Hevo empowers teams to move data faster without lifting a finger. It simplifies ETL workflows and makes scaling easier.

Sign up for Hevo’s 14-day free trial and witness how it makes data integration effortless.

FAQs