---
title: 10 Best REST API ETL Tools to Consider in 2026
description: REST API ETL tools connect, transform, and load API data automatically. Compare the 10 best options for 2026 by features, pricing, and use case fit.
canonical_url: https://hevodata.com/etl-tools/rest-api/
published_at: 2026-08-18T12:08:25.846243+00:00
updated_at: 2026-09-08T17:51:00.679608+00:00
author: Kamlesh
tags: [Data Integration]
category: Data Integration
content_type: article
word_count: 5211
source: https://hevodata.com/etl-tools/rest-api.md
---
# 10 Best REST API ETL Tools to Consider in 2026

> REST API ETL tools connect, transform, and load API data automatically. Compare the 10 best options for 2026 by features, pricing, and use case fit.

Trusted by 2,000+ companies worldwide: Shopify, Favor, Postman, Gartner, Deliverr.

## Key takeaway

REST API ETL tools extract data from REST APIs and load it into a warehouse automatically. Here is a quick breakdown of the 10 most widely used options in 2026:

- **Hevo Data** is fully managed ELT with **reliable** auto-healing pipelines, **simple** no-code setup, automatic schema mapping, and event-based pricing
- **Fivetran** offers pre-built REST API connectors with zero maintenance and automated replication
- **Stitch** provides lightweight cloud ETL for small teams needing fast setup and row-based pricing
- **Airbyte** is an open-source ELT platform for teams that want self-hosted, customizable API connectors
- **Apache Airflow** is an open-source orchestration tool for engineering teams building code-first API pipelines
- **Matillion** provides warehouse-native ELT that runs transformations inside Snowflake, BigQuery, or Redshift
- **Rivery (Boomi Data Integration)** offers cloud-native ELT with support for multi-step API call chaining
- **Talend (Qlik Talend Cloud)** combines enterprise data integration, quality, and governance
- **Pentaho** is a visual ETL platform from Hitachi Vantara for on-premises and hybrid environments
- **Microsoft SSIS** provides on-premises ETL built into SQL Server for Windows-based data environments
- For teams that want production-ready API pipelines without engineering overhead, **Hevo** offers no-code setup, automatic schema mapping, and full pipeline visibility out of the box.

An engineer writes a script to pull data from an API, schedules it, and moves on. Then the API changes a field name, the script fails silently, and nobody notices until a report comes out wrong. Now someone has to find the break, patch the script, and test it again, on top of everything else on their plate that week.

That time adds up fast. [Postman's](https://www.postman.com/state-of-api/2025/#whos-behind-the-data) 2025 State of the API Report found that 69% of developers spend more than 10 hours a week on API-related work, and over a quarter spend more than 20 hours. A meaningful chunk of that is integration upkeep, not new development.

REST API ETL tools exist to take that work off an engineer's plate. Connect the API once, and the tool handles extraction, transformation, and loading on its own, no script to patch every time an endpoint changes.

We looked at how practitioners actually talk about these tools, in community threads, on G2, and in product documentation, then narrowed the list to the 10 worth your evaluation time in 2026. This guide breaks down what each one does best, who it fits, and where it falls short.

## Overall Comparison of the 10 Best REST API ETL Tools to Consider in 2026

| Type | Tool | Best For | Top Use Case | Starting Price |
| --- | --- | --- | --- | --- |
| No-Code & Managed Platform | Hevo | Teams wanting REST API pipelines that are reliable with self-healing architecture, simple to set up in minutes with no scripting required, and transparent with full visibility into every sync | Connecting any REST API to a warehouse without writing or maintaining scripts | Free up to 1M events/month; paid plans from $239/month |
| No-Code & Managed Platform | Airbyte | Teams needing simple REST API connectors built fast through a visual builder | Quick connector setup for straightforward APIs without nested routes or complex auth | Open-source (self-hosted, free); Cloud Standard from $10/month, usage-based |
| No-Code & Managed Platform | Fivetran | Teams wanting pre-built connectors with minimal setup for common APIs | Automated pipelines for well-documented, high-traffic APIs | Free tier up to 500K monthly active rows; paid plans usage-based, custom quote |
| No-Code & Managed Platform | Stitch | Small teams needing a simple, fast ETL setup on a budget | Lightweight REST API syncs for teams just getting started | $100/month (Standard, row-based) |
| No-Code & Managed Platform | Matillion | Cloud-native teams needing orchestration alongside API ingestion | Transforming and loading API data inside a cloud data warehouse | Custom quote only, no public pricing |
| No-Code & Managed Platform | Rivery | Teams handling advanced API logic like chained, multi-step calls | Complex API workflows that simpler low-code tools can't handle | Free starter edition; paid usage at $0.9 per BDU credit |
| Code-First & Open-Source Library | Apache Airflow | Engineering teams orchestrating and scheduling custom API scripts | Scheduling and monitoring Python-based API ingestion jobs | Free, open-source (self-hosted); managed hosting billed separately by provider |
| GUI-Based ETL Tool | Talend (Qlik Talend Cloud) | Teams with existing Qlik or Talend infrastructure needing API connectivity | Data integration within a broader Qlik data fabric | Custom quote only, no public pricing |
| GUI-Based ETL Tool | Pentaho Data Integration | Teams needing visual ETL inside the Hitachi Vantara ecosystem | Visual ETL workflows with broad connector support | Custom quote only, no public pricing; no free edition since 2024 |
| Enterprise ETL | Microsoft SSIS | Teams already licensing SQL Server needing native ETL | Legacy enterprise ETL bundled with existing Microsoft infrastructure | Included with SQL Server license; Azure-hosted runtime from ~$0.84/hour |

## What are REST API ETL Tools?

REST API ETL tools are specialized solutions designed to extract, transform, and load data from RESTful APIs into target systems such as databases, data warehouses, or analytics platforms.

Organizations depend on data from SaaS platforms, cloud services, and third-party applications. REST API ETL tools enable seamless integration of live data into internal systems, providing a structured way to connect, manage, and move API-based data efficiently.

### Key features of REST API ETL tools:

- **Real-time data access**: By directly connecting with RESTful web services, these tools ensure that data is always up to date. Real-time access enables organizations to track changes as they happen, supporting faster responses and more accurate reporting.
- **Standardized data handling**: Using standard HTTP methods like GET, POST, PUT, and DELETE, REST API ETL tools ensure consistency in data operations. You can work with multiple APIs while maintaining uniformity across systems.
- **Improved data quality**: These tools feature built-in features for [cleansing](https://hevodata.com/learn/data-cleansing/), [validating](https://hevodata.com/learn/data-validation/), and enriching data. Businesses can access accurate, high-quality information loaded into their systems, reducing errors downstream.
- **Business agility**: With continuous access to updated, trustworthy data, decision-making becomes faster and more informed. Businesses adapt quickly to market changes and maintain a competitive edge.

## Top 10 REST API ETL Tools to Consider in 2026

### 1. Hevo Data

_G2: 4.4/5_

[Hevo](https://hevodata.com/) is a **no-code, fully managed data pipeline platform** built for teams that need to move data from REST APIs into their warehouse without writing a single line of code. Setup is simple and takes a few minutes, pipelines are transparent end-to-end, and the platform handles authentication, pagination, [schema mapping](https://docs.hevodata.com/pipelines/schema-mapper/), and error recovery automatically, so your pipelines keep running reliably even when something upstream breaks.
What sets Hevo apart is its **real-time replication engine**. Unlike batch-only tools that sync on a fixed schedule, Hevo pushes data continuously, giving your analytics team access to fresh, warehouse-ready data at all times. It also includes built-in transformation capabilities, so you can clean and enrich data mid-pipeline without needing a separate tool.
With **150+ connectors**, support for 2,000+ data teams across 40+ countries, and **transparent event-based pricing**, Hevo is built to scale with your stack without surprising you on your monthly bill.
[**Customer Success Story: Postman**](https://hevodata.com/success-stories/postman/)
Postman, the API platform used by more than 30 million developers, switched to Hevo after their previous integration tool kept breaking against API changes, costing the team at least half a day of engineering work every time a pipeline failed. After moving to Hevo, Postman connected 40+ sources and now saves 30 to 40 developer hours every month, including more than 10 hours that used to go specifically into fixing breakages.

#### Key features

- **Auto-healing pipelines** with intelligent retries and fault-tolerant architecture keep REST API data flowing reliably, so a failed sync recovers on its own instead of waiting for someone to notice.
- **Automatic schema mapping** adjusts when a REST API response structure changes, so a renamed or added field never breaks the pipeline, with no manual intervention needed.
- **Simple no-code REST API connector** with support for No Auth, Basic Auth, and OAuth 2.0, so teams can connect to almost any API without custom authentication code.
- **Transparent pipeline visibility** through unified dashboards, detailed logs, and anomaly detection, so issues surface before they become a support fire.
- **Drag-and-drop transforms** for [data transformation](https://hevodata.com/learn/data-transformation/), plus dbt integration and Python scripting when a team needs more control than the visual builder gives.
- **Enterprise-grade compliance** with SOC 2 Type II, HIPAA, and GDPR.

**Pros**

- No-code setup accessible to technical and non-technical users.
- Real-time replication rather than fixed-schedule batches.
- Automated schema mapping across 150+ connectors.
- Transparent event-based pricing.

**Cons**

- Event-based pricing requires estimating monthly volume upfront.
- Advanced governance features sit on higher tiers.

**Pricing**

| Plan | Price | What's included |
| --- | --- | --- |
| Free | $0 | Up to 1M events/month, limited connectors, up to 5 users. |
| Starter | From $239 / month | Up to 5M events/month, all connectors, dbt integration, up to 10 users. |
| Professional | From $679 / month | Up to 20M events/month, unlimited users, Hevo APIs for pipeline automation. |
| Business Critical | Custom pricing | Streaming pipelines, RBAC, SSO, VPC peering, multiple workspaces. |

> Hevo Data makes setting up and maintaining data pipelines extremely simple. The no-code interface, wide range of connectors, and automated schema mapping reduce the effort of integrating multiple data sources into a central warehouse.
>
> — Ravi Shankar S., Full stack developer — G2 Review

### 2. Airbyte

_G2: 4.4/5_

[Airbyte](https://airbyte.com/) is an open-source data integration platform designed for engineering teams that want full control over their pipelines. It supports syncing data from a wide range of sources, including REST APIs, to data warehouses and lakes, with the option to self-host for free or use the managed cloud version. Its connector library is one of its biggest strengths, with 350+ connectors and an open-source Connector Development Kit (CDK) that lets teams build custom connectors for niche or proprietary APIs. The tradeoff is operational overhead: self-hosted Airbyte can require Kubernetes expertise, ongoing maintenance, and infrastructure spend.

#### Key features

- **350+ connectors**: Connect data from a wide range of sources, including REST APIs, databases, SaaS applications, warehouses, and lakes.
- **Open-source Connector Development Kit**: Build and customize connectors for APIs and other sources not already supported.
- **Self-hosted or managed cloud deployment**: Run Airbyte yourself for free or use the fully managed cloud platform.
- **dbt integration**: Transform data in the warehouse using dbt as part of the data pipeline workflow.
- **Enterprise security and compliance**: Cloud plans support standards and requirements including SOC 2, GDPR, and ISO 27001.

**Pros**

- Open-source and highly customizable
- Strong community support and a large connector ecosystem
- Self-hosted deployment provides full control over data and infrastructure
- Connector Development Kit enables custom API connector development
- Cloud and self-managed deployment options provide flexibility

**Cons**

- Self-hosted deployment can require significant technical expertise
- Infrastructure management adds operational overhead
- Some connectors may require customization or maintenance
- Advanced cloud capabilities can become expensive at higher usage levels

**Pricing**

| Plan | Starting Price | Includes |
| --- | --- | --- |
| Self-Managed Core | Free | Open-source, self-hosted, full connector access, community support |
| Cloud Standard | From $10/month | Usage-based credits, 1-hour sync frequency |
| Cloud Plus | Custom (~$25,000/year reported) | Capacity-based Data Worker pricing, faster syncs |
| Cloud Pro / Enterprise Flex | Custom | Advanced governance, dedicated support |

> Open-Source & Flexibility: Airbyte OSS stands out for its open-source approach. It's both free and self-hostable, providing full control over data and infrastructure while eliminating vendor lock-in.
>
> — Hardik S., Marketing Expert — G2 review

### 3. Fivetran

_G2: 4.3/5_

[Fivetran](https://www.fivetran.com/) is a fully managed ELT platform built for teams that want reliable, hands-off data replication from APIs and databases into their warehouse. Its connectors are maintained in-house, providing stronger reliability and SLA guarantees than community-maintained alternatives. Fivetran offers 500+ pre-built connectors and automated schema migration, helping pipelines continue running when source APIs change. It is particularly well suited to enterprise teams that prioritize uptime, compliance, and minimal maintenance, although its usage-based pricing can become expensive as data volumes and connector counts grow.

#### Key features

- **500+ pre-built connectors**: Connect APIs, databases, SaaS applications, and other data sources using connectors maintained by Fivetran.
- **Automated schema migration**: Automatically adapts pipelines when source schemas or APIs change, reducing manual maintenance.
- **Change Data Capture (CDC)**: Replicate database changes with near real-time synchronization.
- **Enterprise security and compliance**: Supports SOC 2, ISO 27001, HIPAA, and GDPR compliance requirements.
- **Cloud warehouse integrations**: Provides deep integrations with Snowflake, BigQuery, Redshift, Databricks, and other modern data platforms.

**Pros**

- Quick setup with a large library of pre-built connectors
- Automated schema migration reduces pipeline maintenance
- Fully managed infrastructure minimizes engineering overhead
- Strong reliability and enterprise-grade compliance
- Wide range of connectors for APIs, databases, and SaaS applications

**Cons**

- Pricing can become expensive at large data volumes
- Usage-based pricing can make costs harder to predict
- Limited customization compared with code-first or open-source tools
- Connector-based pricing can increase costs as the number of systems grows

**Pricing**

| Plan | Starting Price | Includes |
| --- | --- | --- |
| Free | $0 | Up to 500K MAR/month, 1 destination |
| Standard | Usage-based, from ~$2.50/M MAR (reported) | 15-minute sync frequency, $5 minimum per connection |
| Enterprise | Custom quote | 1-minute syncs, enterprise database connectors |
| Business Critical | Custom quote | Customer-managed keys, PCI DSS Level 1, private networking |

> The best thing about Fivetran is the wide range of connectors with almost every data ingestion service and the ease of use. Automated schema handling and incremental syncs make it particularly strong for scaling ingestion across many systems.
>
> — Dharna H., Data Engineer — G2 review

### 4. Stitch Data

_G2: 4.4/5_

[Stitch Data](https://www.stitchdata.com/) is a cloud-based ETL service designed for simplicity. It allows teams to replicate data from 100+ SaaS tools, databases, and API sources to their data warehouse with minimal configuration. Now part of the Qlik ecosystem following Talend's acquisition, Stitch is a good fit for small to mid-sized teams that need straightforward API-to-warehouse pipelines without the complexity of enterprise platforms. Its Singer-compatible open-source connector framework provides access to a broad community of connectors, although maintenance quality can vary. Stitch is primarily a raw data loader, so teams typically need a separate tool such as dbt for in-warehouse transformations.

#### Key features

- **100+ integrations**: Connect SaaS tools, databases, and APIs to major cloud data warehouses.
- **Simple pipeline setup**: Create and configure data pipelines through a guided interface without coding.
- **Singer-compatible connector framework**: Use an open-source connector ecosystem to extend data integration capabilities.
- **Replication logs and monitoring**: Track pipeline activity and troubleshoot synchronization issues with detailed replication logs.
- **Major warehouse support**: Load data into platforms including Snowflake, Amazon Redshift, and Google BigQuery.

**Pros**

- User-friendly interface makes pipeline setup straightforward
- Supports a wide variety of SaaS, database, and API sources
- Simple approach works well for small and mid-sized teams
- Singer-compatible framework provides access to open-source connectors
- Fast setup with a 14-day trial available

**Cons**

- Limited transformation capabilities
- Requires separate tools such as dbt for advanced transformations
- Pricing can become expensive for larger datasets
- Connector maintenance quality can vary across the Singer ecosystem

**Pricing**

| Plan | Starting Price | Includes |
| --- | --- | --- |
| Standard | $100/month | 5M-300M rows (configurable), credit card billing |
| Advanced | $1,500/month (billed annually) | 100M rows, 3 destinations, unlimited enterprise sources |
| Premium | $3,000/month (billed annually) | 1B rows, mission-critical SLA support |

> Certainly, this is one of the best ETL service providers. It integrates data from various sources within and outside the organization swiftly. A 14-day trial is certainly the cherry on top.
>
> — Yash B., Analyst — G2 review

### 5. Matillion

_Capterra: 4.3/5_

[Matillion](https://www.matillion.com/) is a cloud-native ETL and transformation platform built for teams that need more than basic data movement. It combines API and source ingestion with powerful in-warehouse transformation capabilities, making it an end-to-end option for SQL-centric data teams. Matillion is widely used with Snowflake, BigQuery, Redshift, and Azure Synapse, and its visual pipeline builder makes it accessible to analysts and engineers. Support for SQL, Python, dbt, and warehouse-native processing gives advanced teams additional flexibility. The main considerations are cost and complexity, as credit-based pricing can be difficult to estimate upfront and users need some technical familiarity to take full advantage of its transformation capabilities.

#### Key features

- **Visual low-code pipeline builder**: Build and orchestrate data workflows visually while using SQL and Python for advanced transformations.
- **Cloud data warehouse integrations**: Provides native integrations with Snowflake, BigQuery, Redshift, and Azure Synapse.
- **Built-in orchestration and scheduling**: Create, schedule, and manage complex data pipeline jobs within the platform.
- **Warehouse-native transformations**: Push transformations down to cloud data warehouses for scalable SQL-based processing.
- **Generative AI capabilities**: Provides AI-assisted pipeline building and connectivity with vector databases for modern data workflows.

**Pros**

- Easy integration with major cloud data warehouse platforms
- Robust transformation capabilities for complex data workflows
- Visual interface makes pipeline development accessible to analysts and engineers
- Built-in orchestration and scheduling reduce the need for separate workflow tools
- Supports SQL, Python, and dbt for advanced data engineering workflows

**Cons**

- Requires some technical knowledge to use advanced features effectively
- Higher cost can be a challenge for small businesses
- Credit-based pricing can make costs difficult to estimate upfront
- Advanced transformation capabilities can add complexity to simple ETL workflows

**Pricing**

| Plan | Starting Price | Includes |
| --- | --- | --- |
| Developer | Custom quote (reported ~$1,000/month) | Single environment, individual use |
| Teams | Custom quote (reported ~$2,000/month) | Multi-environment workflows, collaboration features |
| Scale / Enterprise | Custom quote | Advanced governance, unlimited scale |

> Matillion is a great out of the box product with minimal requirements. You spin the machine up, allow your database's firewall to communicate with Matillion. Start creating jobs, schedule them, and sit back. It helps you focus on visualizing your data.
>
> — Erick B., Business System Analyst — Capterra review

### 6. Apache Airflow

_G2: 4.4/5_

[Apache Airflow](https://airflow.apache.org/) is an open-source workflow orchestration platform originally built at Airbnb. While it is not a dedicated ETL tool, it is widely used by data engineering teams to schedule, monitor, and orchestrate complex data pipelines that pull from REST APIs and other sources. Airflow uses Python-based DAGs (Directed Acyclic Graphs) to define workflows, giving engineers precise control over pipeline logic, dependencies, scheduling, and error handling. This makes it highly flexible but also highly technical. It is best suited for teams with strong Python expertise that want a customizable orchestration layer for custom-built API scripts and existing ingestion tools. Managed options such as Astronomer and AWS MWAA reduce the infrastructure burden, but Airflow still requires more engineering investment than purpose-built ETL platforms.

#### Key features

- **Python-based DAG authoring**: Define fully customizable workflow logic, dependencies, scheduling, and error handling using Python.
- **REST API and cloud integrations**: Use a rich operator and hook ecosystem to connect REST APIs, databases, cloud services, and other systems.
- **Built-in scheduler and retry logic**: Schedule workflows, configure automatic retries, and monitor pipeline execution through the Airflow UI.
- **Extensive open-source ecosystem**: Benefit from a large community and broad collection of operators, hooks, providers, and plugins.
- **Managed deployment options**: Reduce infrastructure management through services such as AWS MWAA and Astronomer.

**Pros**

- Highly customizable workflows
- Strong open-source community and support
- Precise control over dependencies, scheduling, and error handling
- Large library of operators and hooks for APIs, databases, and cloud services
- Flexible self-hosted and managed deployment options

**Cons**

- Steep learning curve for beginners
- Requires technical setup and infrastructure management when self-hosted
- More engineering effort than purpose-built ETL platforms
- Python expertise is required for advanced workflow development

**Pricing**

| Plan | Starting Price | Includes |
| --- | --- | --- |
| Self-hosted | Free (open-source) | Full feature set, self-managed infrastructure |
| Managed hosting (e.g., AWS MWAA, Astronomer) | Varies by provider | Provider-specific support, infrastructure handled externally |

> It is easiy to deploy with docker. Provide secure authentication. A better UI in airlfow3.x. There is many method, operator, hooks are added. easily to add dependecy. A better workflow monitoring tool.
>
> — Rajesh K., Senior Cloud Software Engineer — G2 review

### 7. Talend (Qlik Talend Cloud)

_G2: 4.5/5_

[Talend (Qlik Talend Cloud)](https://www.qlik.com/us/products/qlik-talend-cloud) is a comprehensive data integration platform covering ETL, ELT, data quality, governance, and master data management in a single suite. It supports APIs and data sources across on-premises, cloud, and hybrid environments, making it well suited for enterprise teams that need end-to-end control over data movement, transformation, quality, and lineage. The platform offers extensive connectivity and governance capabilities, but its broad feature set can introduce complexity, while advanced functionality requires a paid enterprise license.

#### Key features

- Comprehensive ETL, ELT, data quality, and governance capabilities in one platform
- 1,000+ pre-built connectors for APIs, databases, and cloud platforms
- Visual job designer with drag-and-drop pipeline building
- Native support for hybrid and multi-cloud deployments
- Active metadata management and data lineage tracking

**Pros**

- Active community and enterprise support
- Extensive data transformation capabilities
- Strong data quality and governance features
- Broad API, database, and cloud connectivity
- Supports hybrid and multi-cloud environments

**Cons**

- Can be complex to set up and learn
- Advanced features require a paid license
- Enterprise pricing can be difficult for smaller teams
- Broad feature set may be more than simple ETL use cases require

**Pricing**

| Plan | Price | Includes |
| --- | --- | --- |
| Starter | Custom quote | SaaS replication, limited databases, no CDC |
| Standard | Custom quote | Real-time movement, CDC where supported |
| Premium | Custom quote | ETL/ELT transformations, API integration and design |
| Enterprise | Custom quote | Master data management, highest capacity tier |

> Upsolver enabled us to generate real time tables over streaming inputs, a problem that took us endless resources to solve. The interface is fairly intuitive and API first.
>
> — Assaf L., Data Platform Engineer — G2 Review

### 8. Pentaho Data Integration

_G2: 4.3/5_

[Pentaho Data Integration](https://pentaho.com/) is a visual ETL platform designed for enterprise data integration, transformation, and processing at scale. It enables teams to extract data from multiple databases and systems, transform it through a visual pipeline interface, and load it into target platforms. As part of the Hitachi Vantara ecosystem, Pentaho is particularly suited to long-time enterprise users with existing Pentaho deployments who need robust ETL capabilities and integration with enterprise systems.

#### Key features

- Visual ETL designer for building data integration workflows
- Supports large-scale data processing and millions of data files
- Connectivity to databases, enterprise applications, and data sources
- Premium connectors for Oracle, Salesforce, SAP, and similar systems
- Enterprise deployment and professional implementation services

**Pros**

- Handles large volumes of data efficiently
- Visual interface simplifies ETL workflow development
- Strong database and enterprise-system connectivity
- Well suited for established enterprise deployments
- Backed by the Hitachi Vantara ecosystem

**Cons**

- Enterprise licensing can be expensive
- Premium connectors may require additional costs
- Implementation can require professional services
- Best suited to organizations with established enterprise data infrastructure

**Pricing**

| Plan | Price | Includes |
| --- | --- | --- |
| Enterprise license | Custom quote (reported ~$80,000+ for ~20 named users) | Core platform, named-user licensing |
| Premium connectors | Custom quote (add-on) | Connectors for Oracle, Salesforce, SAP, and similar systems |
| Implementation services | Custom quote (reported $25,000–$100,000) | Hitachi professional services for setup |

> The most like about Pentaho report data integration is it can handle large, millions of data files with no hussle, You can extract data from different databases with such a small amount of time.
>
> — Senando B., Management information system — G2 Review

### 9. Microsoft SSIS

_TrustRadius: 8/10_

[Microsoft SQL Server Integration Services (SSIS)](https://learn.microsoft.com/en-us/sql/integration-services/) is Microsoft's enterprise ETL and data integration platform for extracting, transforming, and loading data across databases, applications, and other sources. SSIS is particularly valuable for organizations already invested in SQL Server because it provides native ETL capabilities without requiring an additional data integration vendor. It supports advanced transformations, workflow orchestration, and both on-premises and cloud execution through Azure Data Factory's Azure-SSIS Integration Runtime.

#### Key features

- Native ETL and data integration capabilities within the Microsoft SQL Server ecosystem
- Visual workflow and package designer for building complex data pipelines
- Advanced data transformations, cleansing, and workflow control
- Integration with SQL Server databases and a wide range of enterprise data sources
- Azure-SSIS Integration Runtime for cloud-hosted SSIS package execution

**Pros**

- Strong integration with SQL Server and Microsoft technologies
- Powerful ETL and transformation capabilities
- Well suited for enterprise-scale data warehouses
- No additional ETL vendor required for SQL Server customers
- Supports both on-premises and Azure-based deployments

**Cons**

- Best suited to organizations already using the Microsoft ecosystem
- Licensing can become expensive at the Enterprise level
- Traditional SSIS deployments can require significant administration
- Less flexible for modern cloud-native and API-first use cases

**Pricing**

| Plan | Price | Includes |
| --- | --- | --- |
| Express / Developer editions | Free | Limited features, non-production use |
| Standard edition | Bundled with SQL Server Standard license | Core ETL functionality |
| Enterprise edition | Bundled with SQL Server Enterprise license (up to ~$14,256/core) | Full feature set including advanced transforms |
| Azure-SSIS Integration Runtime (cloud) | From ~$0.84/hour (with Hybrid Benefit) | Cloud-hosted execution via Azure Data Factory |

> SQL Server is the legacy Database and Data Warehouse in the company. SSIS is used for most ETLs where the data is sourced from / put into SQL Server, depending on the use case.
>
> — Vishal Shah, Data Engineer — TrustRadius Review

### 10. Rivery (Boomi Data Integration)

_G2: 4.7/5_

[Rivery (Boomi Data Integration)](https://rivery.io/) is a cloud-based ELT platform that combines data ingestion, transformation, and orchestration in a single interface. It supports REST APIs and 150+ other data sources, with built-in logic layers for creating end-to-end workflows without switching between multiple tools. Rivery stands out for its unified approach, making it useful for teams that want to reduce tool sprawl and manage ingestion, transformation, and orchestration in one place. Its usage-based pricing can make costs harder to predict for variable data volumes, and the platform may be expensive for smaller teams.

#### Key features

- Connects to REST APIs and 150+ data sources
- Combines data ingestion, transformation, and orchestration in one platform
- Supports advanced API logic and multi-step API call chaining
- Python support with CI/CD, API, and CLI access on Professional plans
- Advanced governance and CDC capabilities on Enterprise plans

**Pros**

- Supports a wide range of data sources
- User-friendly interface
- Combines ingestion, transformation, and orchestration
- Strong support for complex API workflows
- Can reduce data-tool sprawl by consolidating workflows

**Cons**

- Pricing can be high for small businesses
- Usage-based pricing can make costs difficult to predict
- Limited customization options
- May be more functionality than smaller teams need

**Pricing**

| Plan | Price | Includes |
| --- | --- | --- |
| Base | Free | One environment, two users, unlimited connections |
| Professional | Pay-as-you-go from $0.9/BDU credit | Two environments, unlimited users, Python support, CI/CD, API & CLI access |
| Enterprise | Custom quote | Advanced governance, CDC for additional sources |

> Rivery is an incredibly user-friendly platform that stands out for its stability and exceptional customer service. Not one day goes by without us using Rivery—it’s been a massive time-saver, significantly boosting productivity for me and my team.
>
> — Inbal R., Product Analytics Team Lead — G2 Review

## Factors to Consider When Selecting the Right REST API ETL Tool

Here is a detailed breakdown of what to evaluate before committing to a REST API ETL solution in 2026:

### 1. Does it connect to the APIs you actually use?

The tool should support a wide range of REST API authentication methods including OAuth 2.0, API keys, and Bearer tokens, and handle pagination, rate limiting, and schema changes automatically. As organizations now average hundreds of SaaS applications, your ETL tool needs to connect to both common and niche APIs without requiring custom engineering for each one.

### 2. Can it clean and shape data before it reaches your warehouse?

Look for tools that can cleanse, enrich, and reshape API responses before loading them into your target system. With AI and analytics workloads increasingly depending on high-quality, structured data, transformation is no longer optional. Strong in-pipeline or in-warehouse transformation support reduces your dependency on additional tools like dbt.

### 3. Does it support real-time data or only scheduled syncs?

Choose a tool that supports both batch and streaming pipelines so you can adapt to different API data refresh needs. In 2026, real-time data access is increasingly a baseline requirement, particularly for teams building AI-powered workflows, live dashboards, or event-driven architectures that cannot tolerate hour-old data.

### 4. Can non-technical teams build and manage pipelines without engineering help?

A no-code or low-code interface allows business and analytics teams to build and manage pipelines without involving a data engineer at every step. As more organizations push toward self-service data access, tools that require heavy technical setup create bottlenecks that slow down the entire team. The best REST API ETL tools today let non-technical users connect sources, map fields, and schedule syncs without writing a single line of code.

### 5. Can you predict your monthly bill before it arrives?

With per-connector and volume-based pricing models becoming increasingly complex, transparent pricing is more important than ever. Look for tools where you can accurately forecast monthly costs based on your data volume, number of connectors, and sync frequency, without hidden overages or tier-gated features.

### 6. Will it handle your data volume as you grow?

Your ETL tool should handle increased API data loads without pipeline degradation or unexpected cost spikes. As businesses grow and add more data sources, scalable tools ensure API extractions remain fast and reliable, whether you are processing five million or five hundred million events per month.

### 7. Does it meet the compliance standards your business requires?

Ensure the tool encrypts data in transit and at rest, supports secure authentication methods, and complies with regulations relevant to your industry such as GDPR, HIPAA, or SOC 2. With data governance failures affecting a growing share of organizations, compliance can no longer be treated as an afterthought or reserved for enterprise-tier plans.

### 8. Is it built to feed your AI and analytics workflows?

This is a 2026 addition worth evaluating. As teams build AI pipelines, RAG systems, and agentic workflows, the ETL tool needs to deliver clean, real-time, and well-structured data to support model training and inference. Tools that offer low-latency replication, strong data quality controls, and support for vector database destinations are better positioned for AI-driven data stacks.

## Why should you migrate from Public API’s to Data Warehouse?

- Data migration to a data warehouse will allow organizations to bring information from multiple APIs into one single source, which will help make such information much more accessible and easier to analyze.
- Enhanced Data Analysis: A data warehouse supports advanced analytics, which helps businesses to gain actionable insights from large datasets efficiently.
- Data Quality and Consistency: ETL helps in maintaining quality and consistency of data, and it makes the process of keeping proper and accurate records and reports relatively easy.

## Conclusion

Moving data from public APIs to the data warehouse is important data management and analysis. [Advanced ETL tools](https://hevodata.com/learn/8-best-cloud-etl-tools/), such as Hevo, Airbyte, and Fivetran, can help organizations streamline their data integration processes and ensure access to the most updated information.

Discover powerful data extraction tools to streamline data retrieval from various sources for analytics and integration. Learn more at [Data Extraction Tools](https://hevodata.com/learn/best-data-extraction-tools/).

When selecting an appropriate ETL solution, compatibility, scalability, and user-friendliness become matters of consideration, and in return, you are optimizing the entire data strategy to drive valuable insights. Then, investment in the appropriate tools will enable businesses to extract more value from their data assets while the data landscape continues to evolve.

## FAQ

### What is ETL in API?

ETL in API stands for extracting data from different APIs and then transforming the same into a suitable format so that it can be loaded into a target system like data warehouse, thereby enabling effective consolidation and analysis of data from multiple sources.

### What are the 4 types of ETL tools?

a. On-Premises ETL Toolsb. Cloud-Based ETL Toolsc. Open-Source ETL Toolsd. Real-Time ETL Tools

### What is the difference between API and ETL tools?

APIs are interfaces that allow the communication between different software systems and facilitate exchanges of data, while ETL tools extract data from different sources, transform it, and load it into a centralized repository where the data is analyzed.

### Why should you migrate from public APIs to a data warehouse?

Migrating API data to a data warehouse brings all your information from multiple sources into one central location, making it far easier to access, analyze, and act on. A warehouse also supports advanced analytics that individual API sources cannot, giving your team the ability to run queries across datasets and surface insights that would otherwise stay hidden. It also improves data quality and consistency, ensuring your reports and records stay accurate over time.