9 Best Salesforce ETL Tools in 2026: The Ultimate Guide

For many organizations, Salesforce becomes the de-facto reference point for customer data. With all sales pipelines and marketing interactions flowing through it, it is tempting to treat Salesforce as the single source of truth. 

But there’s a catch: although Salesforce excels at business process data, it does not naturally capture the full layer of product usage, billing details, or support interactions that live across other systems. Trying to cram it all into Salesforce quickly leads to storage constraints and reporting complexity – because that kind of behavioral telemetry belongs in the warehouse. To unlock its full value, Salesforce must become part of a broader data ecosystem – integrated with platforms like Snowflake, BigQuery, or Redshift.

Salesforce provides native tools for that, like Data Loader and Import Wizard. They work – but only for simple, manual tasks. Some teams attempt to solve this by building custom Python scripts against the Salesforce API. This seems a smart way around at first, until it hits maintenance walls and becomes a project by itself: updating APIs, keeping authentication up-to-date, and modifying retry logic.

If Salesforce is your system of record, choosing the right data loader determines whether your data remains operational – or becomes analytical. In this article, we review the top Salesforce data loader tools for 2026 – not by simply listing features, but by analyzing how they handle Salesforce-specific challenges like API limits, complex object relationships, and Reverse ETL.

Table of contents

1. Why Native Salesforce Tools Aren’t Enough
2. 3 Critical Features to Look for in Salesforce ETL 
3. Top 9 Salesforce ETL Tools in 2026
4. Best All-in-One Cloud Solution (No-Code)
   • Skyvia
5. Modern Data Stack (ELT Only) 
   • Fivetran
   • Airbyte
   • Matillion
6. Enterprise & Developer Heavyweights 
   • MuleSoft 
   • Informatica
   • Talend (Qlik)
   • Workato
   • Jitterbit
7. Comparison Matrix: Top Salesforce ETL Tools
8. How to Choose the Right Salesforce ETL Tool  
9. Best Practices for Salesforce Data Integration
10. Conclusion

Why Native Salesforce Tools Aren’t Enough

The “Silo” Problem 

Salesforce is designed to handle operational events in real time: creating leads, updating opportunities, and moving deals across pipeline stages. These capabilities are enabled by its platform architecture, which is optimized for transaction processing (OLTP).

On the contrary, analytical queries are entirely different. They often require joining large datasets across multiple systems and comparing behavioral trends over time – something that Salesforce cannot handle efficiently. True cross-system joins, especially across millions of records, belong in a data warehouse designed for analytical workloads (OLAP).

Without integration, Salesforce remains operationally strong but analytically isolated.

The “Manual” Trap 

When a team realizes it needs Salesforce data in another system, the first instinctive integration pattern is a manual export. Generate a report, click “Export,” download a CSV – simple and fast. Until those needs snowball from stand-alone reports into an avalanche of business-critical syncs.

Beyond being error-prone and insecure, manual exports cannot scale or be automated. They may work for simple, one-time operations – but not for modern pipelines that require continuous synchronization, automation, and large data volumes.

3 Critical Features to Look for in Salesforce ETL 

1. API Limit Management (Bulk API vs. REST) 

Everyone who has ever worked with Salesforce knows about its API limits. Enforced within a rolling 24-hour window, these restrictions are partly technical (because Salesforce operates as a multi-tenant platform) and partly aligned with pricing tiers (the higher the plan, the higher the API quotas). One way or another, this is a serious factor to reckon with – because once the limit is reached, requests are simply blocked.

This is where many lightweight or low-cost ETL tools struggle, primarily those that rely on the standard REST API. While suitable for small queries and low data volumes, REST-based integrations can quickly exhaust the daily API quota when handling large datasets or frequent synchronization jobs. The results are notorious – failed jobs, delayed syncs, or incomplete data loads.

For high-volume operations, Salesforce provides a different mechanism: the Bulk API (specifically Bulk API 2.0). Unlike REST, Bulk API is designed for high-volume workloads. It processes records in large batches and minimizes API overhead per job, making it far more suitable for warehouse-scale exports or bulk inserts.

A robust Salesforce ETL solution should intelligently switch between REST and Bulk API 2.0 based on workload size. Small queries can run via REST for responsiveness, while high-volume jobs should automatically leverage Bulk API to prevent limit exhaustion.

2. Handling Object Relationships

When moving data in or out of Salesforce, simply transferring independent records is not enough. It is critical to preserve the relationships between objects – because this is how Salesforce structures its data:

• An Account is linked to Contacts.
• Opportunities belong to Accounts.
• Cases may relate to both Contacts and Accounts.
• Custom objects often depend on parent records.

In Salesforce, some objects can exist independently (Lookup), while others are structurally dependent on a parent record (Master-Detail). Improper relationship mapping during export or import may result in child records failing to load, become duplicated, or end up orphaned.

The ETL tool must understand record dependencies and enable loading data in the right sequence while preserving relationship integrity.

3. Reverse ETL Capabilities

In the past, the primary goal was to extract data from Salesforce into a warehouse for reporting and analytics. Today, analytics must also flow back into operational systems. Given Salesforce’s central role in business processes, it often becomes the primary destination for Reverse ETL.

Data teams build models in the warehouse – calculating metrics such as lead scores, churn risk, or product adoption indicators. Reverse ETL delivers these insights back into the CRM, where they can reach the teams who act on them.

For example, sales teams can prioritize outreach based on a “Lead Score” field, while customer success teams can proactively support accounts identified as high “Churn Risk.”

A modern ETL tool should support bidirectional synchronization — not just extracting records, but updating and enriching them based on warehouse logic. In the case of Salesforce, this also means respecting object relationships and operating within API limits.

Top 9 Salesforce ETL Tools in 2026

All tools in this list support Salesforce connectivity as a baseline, but they differ in integration complexity and the type of organization they are best suited for. To simplify the choice, we’ve grouped them into three practical categories.

Best All-in-One Cloud Solution (No-Code)

Skyvia

Skyvia is a cloud-based data integration platform that covers ETL/ELT, data sync, backup, and Reverse ETL in one unified interface.

It allows users to quickly copy Salesforce data to a data warehouse as is or configure an ETL process with data transformations to fit an existing schema. When needed, modeled or enriched data can be pushed back into Salesforce (Reverse ETL). Alternatively, data can be exposed via OData and connected to Salesforce as External Objects.

Key Features

• Efficient Salesforce API usage: uses Bulk API by default for loading data to Salesforce, and allows configuring API modes to match account capabilities and limits.
• Relationship preservation: visual mapping tools maintain Lookup and Master-Detail relationships during data transfers.
• Transparent pricing: plans based on integration packages and feature sets. 
• Salesforce backup & restore (optional): available as a separate product subscription within the Skyvia platform.

Best For

Small and medium-sized businesses seeking a unified platform for ETL, ELT, and Reverse ETL without enterprise-level complexity.

Pros

• User-friendly, with no-code interface and easy setup.
• Over 200 connectors.
• Advanced transformation capabilities with powerful mapping.
• A number of different data-related tools in one platform.
• Bidirectional data sync between cloud apps and databases.
• Comprehensive documentation and tutorials.

Cons

• No support for NoSQL databases like MongoDB.
• Access to advanced features requires higher subscription plans. 
• No on-premise option.

Modern Data Stack (ELT Only) 

Fivetran

Fivetran is an end-to-end data platform that allows you to ingest, model, and activate data — all within a single interface. This is a fully-managed data integration software that requires minimal maintenance once configured.

The platform offers one of the industry’s largest libraries of pre-built connectors, with over 700 sources and 200+ destinations for Reverse ETL. 

Fivetran respects Salesforce metadata during extraction. It preserves object relationships during replication, but requires explicit configuration of parent-child sequencing in write-back scenarios. 

Fivetran uses both REST API and Bulk API, with Bulk reserved for historical/high-volume incremental syncs, and REST for other cases.

Key Features

• Offers automated ELT connectors for SaaS, databases, and event sources.
• Supports Reverse ETL / data activation via the integrated Census layer.
• Automatically handles schema management and source changes.
• Supports incremental data ingestion with CDC and log-based replication.
• Provides native integration with dbt for transformation and modeling.

Best For

Teams that want reliable, set-it-and-forget-it pipelines for ELT workloads.

Pros

• Full stack end-to-end platform.
• Scalable for large datasets and multi-system pipelines.
• Strong dbt and transformation workflows.
• Enterprise-grade security and compliance.

Cons

• Pricing can be high for startups or small teams.
• Not ideal if you only need reverse ETL without warehouse ingestion.

Airbyte

Airbyte is an open-source data-movement engine that excels in replication and ingestion tasks. With a growing library of connectors (600+) and active community support, Airbyte is an advanced developer-centric tool.

For the Salesforce connector, Airbyte uses bulk-first design to avoid burning API calls, with documented conditions under which it will use REST instead.

During extraction Airbyte preserves foreign key references and level structure of Salesforce objects. However, it relies on workflow design for correct parent-child load orchestration.

Reverse ETL is available for supported destinations, including Salesforce.

Key Features

• Schema discovery and configurable mappings at the pipeline level.
• Support for filtering and conditional logic. 
• Scalable and customizable for varied workflows.
• Built-in error handling and reporting.

Best For

Developer-oriented teams who prioritize flexibility and control over convenience, and are willing to own their data infrastructure. 

Pros

• Extensive connector library. 
• Transparent and customizable due to its open-source nature. 
• Flexible deployment options: cloud, self-hosted, hybrid. 

Cons

• Requires DevOps maintenance for the self-hosted version. 
• Documentation for certain connectors may be insufficient.   

Matillion

Matillion is a cloud-native ELT platform designed for scalable data extraction, transformation, and BI workloads.

The platform offers deep integration with major cloud data warehouses like Snowflake, Redshift, and BigQuery. With its visual interface and support for both no-code and SQL-based transformations, teams can accelerate development without sacrificing flexibility.

Provides field mapping and sequencing capabilities, with relationship handling defined within job logic rather than inferred automatically.

Matillion works both with the Salesforce Bulk API and the standard REST API defined as different components. The platform requires users to pick the component depending on workload size:

• Salesforce Query for the standard Salesforce REST API.
• Salesforce Bulk Query for the Salesforce Bulk API.

Matillion supports writes to Salesforce, but it is not a Reverse ETL-focused tool.

Key Features

• Visual drag-and-drop interface for orchestration and transformation jobs.
• Extensive library of pre-built connectors for SaaS applications, databases, and cloud platforms.
• ELT architecture with pushdown transformations executed directly in the cloud data warehouse.
• Cloud-native design for deployment on AWS, Azure, and GCP.
• Support for custom integrations via REST API components.
• Git integration (GitHub, GitLab, Bitbucket) for version control of jobs and configurations.
• Built-in scheduling and dependency management.

Best For

Data engineers and analytics teams who conduct SQL-driven workflows inside Snowflake, Redshift, or BigQuery. 

Pros

• Highly scalable with a strong warehouse-first transformation logic.
• Native support for major cloud data warehouses, including Snowflake, Databricks, Amazon Redshift, Google BigQuery, and Azure Synapse.
• Integrable with on-premise datasets via third-party connectivity layers (e.g., Progress DataDirect Hybrid Data Pipeline).
• Support for custom API extraction using built-in REST/HTTP components.

Cons

• Advanced workflows require solid SQL knowledge. 
• Must be deployed inside your cloud account. 
• Pricing and infrastructure requirements may be too heavy for startups and smaller teams. 

Enterprise & Developer Heavyweights  

MuleSoft

MuleSoft is an API-led integration and connectivity platform of enterprise level. It is built for large organizations to design, manage, and orchestrate APIs and integrations at scale.

Since MuleSoft is part of Salesforce, its dedicated connector is deeply maintained and aligned with Salesforce API evolution. The connector supports REST, SOAP and Bulk APIs, but the mode selection must be configured within the integration flow.

MuleSoft does not auto-infer Salesforce schema graphs. Object relationships are handled via field mapping and support of external IDs. 

MuleSoft supports writing back to Salesforce, including updating, upserting and deleting operations. 

Key Features

• Event-driven and microservices-oriented architecture. 
• Offers hundreds of prebuilt connectors and API templates via Anypoint Exchange.
• Enterprise-level API management.
• Handles hybrid and cross-system orchestration reliably at scale. 

Best For

Enterprises that treat integrations as long-term resources rather than one-time pipelines; organizations with a hybrid deployment model (cloud, on-prem, VPC, containers). 

Pros

• Reusable API assets and exchange. 
• Strong fit for hybrid / legacy integration.
• Enterprise-grade security and compliance controls.
• Scales well for complex enterprise ecosystems.

Cons

• Steep learning curve. 
• Enterprise-tier pricing excludes SMB companies. 
• Requires architectural planning. 
• Overkill for simple Salesforce-warehouse replication.

Informatica

Informatica is an integrated data management platform that comprises several solutions under its umbrella. This is a mature, well-established tool with a long track record of serving enterprise customers.

Informatica has deep data management capabilities — especially around metadata cataloging, lineage, and governance. With its recent acquisition, Salesforce plans to improve its integration patterns by making them more schema-aware and context-driven.  

Informatica’s Salesforce connector is optimized to manage API consumption; it supports Bulk API for high-volume workloads and standard API modes for smaller, record-at-a-time operations.

Complex Salesforce object relationships are handled via metadata-driven mapping and dependency-aware transformation design. 

The platform supports both reads and writes back to Salesforce. 

Key Features

• Advanced integration capabilities, including complex transformation logic, reusable mappings, and data enrichment.
• Intelligent mapping and data transformation via visual designer.  
• Enterprise-level metadata management and data lineage.
• Broad range of deployment options across hybrid, multi-cloud and on-premise environments.    
• Built-in data quality and governance for regulated industries. 
• Enterprise-grade security and compliance controls. 

Best For

Enterprises with mature data governance frameworks and structured integration processes.

Pros

• Deep Salesforce ecosystem alignment. 
• Handles scale and complexity on the enterprise level. 
• Strong fit for regulated environments. 

Cons

• High complexity and steep learning curve.
• Heavyweight infrastructure and setup.
• Enterprise-level pricing makes it cost-prohibitive for small companies. 
• Overkill for simple ETL use cases.

Talend (Qlik)

Talend (now part of Qlik) is an enterprise data management platform that brings ETL/ELT, data quality, governance, and API logic together under a single integration layer.

Talend supports both Bulk and standard Salesforce APIs, but requires API behavior to be configured within integration jobs.

Although Talend provides field-level mapping and support for external IDs, it does not automatically infer full schema dependency graphs, requiring relationship ordering to be designed beforehand. 

It allows loading data back into Salesforce.

Key Features

• Visual job designer with code generation.
• Developer-oriented, with Java code behind every job. 
• Metadata management and lineage tracking. 
• Supports REST/SOAP integration and API orchestration.
• Built-in data quality and profiling tools. 
• Supports hybrid and multi-cloud deployment models. 

Best For

Technically skilled teams requiring flexible, customizable integration workflows.

Pros

• Mature enterprise integration platform. 
• Highly flexible and configurable. 
• Fine-grained transformation control. 
• Data quality tooling built-in. 

Cons

• Steeper learning curve.
• Requires technical expertise.
• API mode often requires manual configuration.
• Enterprise pricing tiers for full functionality.

Workato

Workato is an AI-driven iPaaS with a strong focus on data integration and business workflow automation. 

With its automation-first design, Workato exhibits its best when integrating Salesforce within broader business workflows rather than performing standalone ETL operations. It supports Salesforce Bulk API for high-volume tasks, but it must be explicitly configured inside the recipe (workflow).

Workato supports field-level mapping, lookup resolution, and record creation with reference IDs. However, it does not provide deep schema-aware dependency management like enterprise ETL tools.

Workato allows enriched warehouse data to be written back into Salesforce; yet it is better suited for operational integrations than high-loaded analytical pipelines.

Key Features

• Low-code visual interface.
• Extensive library of pre-built connectors (over 1000).  
• Event-triggered workflows connecting SaaS apps, databases, and APIs.
• Allows exposing integrations as APIs and orchestrating multi-step workflows.
• Enterprise security & governance controls.

Best For

Businesses seeking no-code platforms with AI-driven automation.  

Pros

• Convenient UI/UX and easy implementation.
• Reusable connectors and managed file transfers.
• AI and ML capabilities.
• Real-time data processing. 
• Support of event-driven architecture. 

Cons

• Steep learning curve.
• Not designed for high-volume batch ETL.
• Pricing is quote-based and not publicly detailed. 

Jitterbit

Jitterbit is an API-led integration platform where data movement occurs not as standalone pipelines, but as part of broader business workflows. With this application-centric approach, Jitterbit prioritizes API orchestration over traditional ETL.

Jitterbit supports both Bulk and standard Salesforce APIs, with configuration options based on the workload size. 

Object relationships are handled through field mapping and workflow sequencing. However, the relationship logic requires prior configuration, and is not automatically inferred from Salesforce metadata.

Jitterbit enables Reverse ETL by supporting bidirectional data movement.

Key Features

• API-led architecture: designed around API orchestration rather than warehouse-centric data pipelines.
• Application workflow orchestration: embeds data movement within broader business processes and API-driven workflows.

• Hybrid deployment flexibility: supports cloud, on-premise, and hybrid architectures.
• Advanced mapping and transformation: field-level mapping and data manipulation within workflows.
• Enterprise iPaaS capabilities: supports application integration, API management, and service orchestration beyond traditional ETL.

Best For

Companies building service-oriented architectures; teams that prefer reusable services around Salesforce and internal systems over one-off pipelines.

Pros

• Enables teams to design integrations as reusable API services.
• Keeps API logic, data syncs, and application workflows together under a single umbrella.
• Works well in hybrid environments, with systems scattered across on-prem and cloud platforms. 

Cons

• Learning curve for API-oriented design.
• Not warehouse-native; less optimized for analytics-centric ELT workloads.
• Enterprise-level pricing can be too high for smaller companies.

Comparison Matrix: Top Salesforce ETL Tools

Tool	Support for Bulk API	Reverse ETL	Pricing Model	Skill Level
Skyvia	Yes (configurable; Bulk supported for high-volume ops)	Yes	Subscription tiers (integration packages + features)	Low-medium
Fivetran	Yes (managed internally)	Yes	Volume-based (monthly active rows)	Low
Airbyte	Yes (connector-based; configurable)	Partial / Configurable	Volume-based (cloud) / Open-source self-hosted	Medium
Matillion	Yes (configurable in jobs)	Yes	Instance-based (infrastructure + license tier)	Medium-high
Talend (Qlik)	Yes (explicit configuration)	Yes	Subscription / Enterprise license	High
Informatica	Yes (enterprise-grade support)	Yes	Enterprise license (contract-based)	High
MuleSoft	Yes (via Salesforce connector; configurable)	Yes	Enterprise contract-based	High
Workato	Yes (bulk actions must be configured in recipes)	Yes	Usage-based (tasks / recipes; quote-based)	Medium
Jitterbit	Yes (configurable in workflows)	Yes	Contract-based / Tiered enterprise	Medium-high

How to Choose the Right Salesforce ETL Tool  

Looking for the “right” Salesforce ETL tool, align your choice with a few key factors: your company’s size, the technical expertise of the teams involved, your integration goals, and your budget.

Let’s move from theory to practice and look at a few realistic scenarios.

Scenario A: “I have no engineers and a fixed budget” 

Suppose your team is mostly business users — marketing, sales, RevOps — and there’s no dedicated data architect around. Your priorities include operational simplicity and easy setup without heavy pipeline engineering. You’re not looking for deep customization, but you still need a reliable solution that covers core ETL and Reverse ETL use cases.

In that case, Skyvia is likely a good fit. It’s a configurable integration SaaS that supports ETL, ELT, and Reverse ETL without requiring deep technical expertise. The pricing model is predictable as well, which is an advantage when you’re working within a fixed budget.

Scenario B: “I have an unlimited budget and need raw data in Snowflake” 

In this case, you need to replicate Salesforce data into Snowflake as-is, preserving schema and object hierarchy. You’re not interested in digging into API modes, tuning batch sizes or debugging API limits. 

Even if you have data engineers, you’d rather have them focus on modeling, defining business logic, or building analytics – not maintaining integration pipelines. What you’re looking for is something stable and predictable, to which you could handle lower-leverage work without constant babysitting. And you’re willing to pay extra for that convenience.

In that situation, Fivetran makes sense. It’s a managed, warehouse-first tool optimized for ELT into platforms like Snowflake, and it’s built around the idea of minimizing pipeline maintenance.

Scenario C: “I need to connect legacy on-prem systems to Salesforce” 

Unlike the two previous cases, scenario C is not about simply moving data from A to B. It’s about safely integrating Salesforce into a complex, hybrid – and possibly regulated – enterprise architecture.

One of the main challenges related to legacy systems is that they often don’t expose modern REST APIs, making a point-to-point connection technically difficult. 

An API-led platform like MuleSoft wraps the legacy system in an API, exposing it as a managed service through a unified interface. This makes the integration safer, reusable, and less fragile over time.

Compliance is another important aspect. When connecting legacy systems to Salesforce, you are often dealing with sensitive data – financial, medical, or personal. Tools with enterprise API governance, like MuleSoft or Informatica, provide access control, monitoring, and audit logging, helping ensure compliance and traceability.

Best Practices for Salesforce Data Integration

Incremental Loading 

Avoid pulling the full dataset every time. Spend your API calls sparingly by using incremental loading. Apply filters like LastModifiedDate or a CDC mechanism to extract only what has changed since the last sync to reduce load times and API consumption.

Sandbox Testing

Deploying new mappings directly into production is risky. Even a small field mapping mistake can break relationships or create duplicate records. Don’t wait for that to happen – always test your ETL flows in a Salesforce Sandbox first, so you can catch issues before they affect real customer data.

Monitoring API Limits

We have already discussed the importance of respecting Salesforce 24-hour API limits.

To avoid exceeding them, actively monitor API consumption. Most integration tools provide usage dashboards or logging features. Set alerts when usage reaches around 70-75% of your daily quota. This gives your team time to investigate before hitting the hard limit.

Conclusion

In this article, we looked at nine different tools for Salesforce data integration. There is no single “best” option for everyone. The right tool should match your integration goals and fit the way your team actually works – whether that means a no-code interface or SQL-driven pipelines managed by a data team.

API limits, complex object relationships, and hybrid environments are real challenges in Salesforce integrations. But they shouldn’t slow down your operational workflows or analytics initiatives. The right tool quietly supports your architecture in the background, absorbing complexity instead of creating additional friction.

For those who want to automate Salesforce data pipelines in a fast and reliable way, Skyvia data integration platform could be a good fit. You can start with a practical setup that supports ETL, ELT, and Reverse ETL – no overengineering or paying for raw data volume. There is a free trial available to see whether it fits your needs, before making a long-term decision.

FAQ for ETL Tools for Salesforce