Scattered systems lead to scattered insights. Between real-time events, cloud apps, and legacy databases, data moves fast but often in the wrong direction. The right pipeline tool gives your team control, whether you’re powering machine learning workflows, syncing into a cloud data warehouse, or supporting real-time analytics.

This guide compares 9 leading data pipeline tools — from open-source platforms to fully managed solutions — and what to consider when choosing 1 for your team.

Pressed for time? Skip straight to the step-by-step decision guide.

Top 9 data pipeline tools

We’ll start with Fivetran — our solution — then cover 8 other leading tools.

1. Fivetran

Best for: Automating ELT pipelines across multiple sources with minimal engineering overhead

Fivetran offers low-code ELT pipelines that centralize data from over 700+ sources — including cloud platforms, SaaS applications, and databases like SAP and Oracle. Its pre-built data connectors, automated maintenance, and schema drift handling help teams reduce engineering overhead, enhance their data management capabilities, and keep teams and tools synced.

Key features include:

• Connector reliability: All connectors offer 99.9% uptime and adapt to vendor changes like schema updates or API modifications, ensuring the freshest data for analytics.
• Fault tolerance: Our data pipelines are built for fault tolerance, automatically recovering from disruptions, and maintaining data normalization for immediate analysis.
• Active metadata: Source metadata provides visibility into lineage, access, and changes — supporting governance, auditability, and lineage tracking.
• Supported data destinations: Fivetran connects to a range of destinations, including Snowflake, Databricks, Google Cloud, Azure, Amazon Redshift, and Amazon S3. Syncing data reliably across tools and teams helps ensure that downstream users always work from current, consistent information.

Users value Fivetran because it simplifies and automates pipeline maintenance and monitoring. It’s a low-code solution that saves developers a lot of time they would’ve otherwise spent on integration setup and testing.

Learn more about our pricing plans, including our free tier.

2. AWS Data Pipeline

Best for: Scheduled data movement in legacy AWS stacks (now replaced mainly by Glue or Step Functions)

AWS Data Pipeline automates data transfers between AWS compute and storage services and AWS and on-premises data sources. This web service allows you to schedule complex data processing workflows at specified intervals.

Note: Data Pipeline is an older service. For most modern workflows, AWS recommends alternatives like AWS Glue or Step Functions.

AWS Data Pipeline key features include:

• Drag-and-drop console: Allows you to manage pipelines with an intuitive interface.
• Scalable design: Handle millions of files with the same ease as processing a single file.
• Predefined logic and conditions: Use built-in preconditions, eliminating the need for custom code.
• Pipeline templates: Templates for complex tasks, such as running periodic SQL queries, without starting from scratch.
• Fault-tolerant workflows: Create sophisticated data processing workflows that are fault-tolerant.
• Automated error handling: Activities automatically retry on failure, with notifications sent via Amazon Simple Notification Service (Amazon SNS).

Learn more on the Fivetran + Amazon AWS partner page.

3. Hevo Data

Best for: No-code ELT with real-time sync and basic transformation for fast-growing teams

Hevo Data is a fully managed data pipeline platform that automates and simplifies data integration and transformation processes. It allows you to move data from multiple sources to your cloud data warehouse or data lake in real time without coding.

While it lacks hands-free schema drift management and its connector library is limited compared to tools, Hevo does support over 100 data sources, including databases, SaaS applications, and cloud storage platforms.

Hevo Data’s key features include:

• Real-time data streaming: Provides continuous, real-time data flow for immediate analysis.
• Automated ETL: Manages extraction, transformation, and loading (ETL) processes
• Multiple integrations: Supports over 100 data sources, including popular databases and SaaS applications.
• No coding required: Lets users configure pipelines, set sync schedules, and manage connectors through a visual UI — no SQL or scripting needed.
• Data transformation: Offers data transformation capabilities to prepare data for analysis.
• Scalability and reliability: Automatically scales compute and storage based on workload demands, so teams don’t have to provision resources manually as data volumes grow.

Read a comparison of Fivetran and Hevo Data.

4. Stitch Data

Best for: Lightweight EL pipelines with simple setup and post-load dbt modeling

Stitch is a user-friendly data pipeline tool that connects and replicates data from various sources to your cloud data warehouse without requiring any coding. It integrates with many databases, including MySQL, and SaaS applications like Salesforce and Zendesk.

With Stitch, you can set the replication frequency to keep your data up-to-date and ready for analysis whenever needed. While it can support basic transformations via dbt or Singer, it’s not designed for heavy data modeling.

Stitch Data’s key features include:

• Security compliance: SOC 2 Type II, PCI, GDPR, and HIPAA compliant, providing enhanced security and regulatory adherence for your data.
• Range of integrations: Offers connections to over 140+ SaaS applications, making it easy to centralize data.
• Cloud destination support: Supports cloud destinations like Microsoft Azure Synapse Analytics, Snowflake, Amazon Redshift, and Google BigQuery.
• User-friendly interface: Simple-to-use interface that allows team members to get started quickly.
• Reliability and redundancy: Handles critical workloads and includes multiple redundant safeguards to keep data safe during outages.
• Continuous updates: Pipelines automatically update, reducing the need for ongoing maintenance.

Pricing: $100 per month.

5. Apache NiFi

Best for: Visual, real-time data routing with granular flow control and full data lineage

Apache NiFi automates data flow between systems with real-time data ingestion, routing, and transformation capabilities.

It lets teams design complex data flows with an easy-to-use drag-and-drop interface — no custom coding required.

Apache NiFi’s key features include:

• User-friendly interface: Intuitive web-based UI for designing, managing, and monitoring data flows.
• Scalability: Can scale from a single node to a cluster of nodes to handle large data volumes.
• Data provenance: Detailed tracking and visualization of data as it flows through the system.
• Security: Fine-grained access control and data encryption to maintain data integrity and security.
• Integration: Supports various data sources and destinations, including databases, cloud services, and messaging systems.
• Extensibility: Custom processors and connectors to meet specific data flow requirements.
• Reliability: Built-in fault tolerance and guaranteed delivery to ensure reliable data processing.

Pricing: Apache NiFi is open source and free to use.

6. Apache Airflow

Best for: Custom, code-based workflow orchestration across complex data pipelines

Apache Airflow is an open-source platform that lets data teams build and schedule workflows with precise control. It uses Directed Acyclic Graphs (DAGs) to map out each step so that engineers can visualize and manage task order and dependencies across pipelines.

Apache Airflow’s key features include:

• Supports custom scripting, plugins, and third-party service connections
• Ideal for scheduling complex or interdependent data jobs
• Flexible deployment options: on-premise, cloud, and hybrid infrastructure
• Backed by an active open-source community
• Requires engineering resources to set up and maintain

Airflow is best for teams that want complete visibility and don’t mind managing infrastructure directly.

Pricing: Free to use, but total cost depends on hosting, setup time, and ongoing upkeep.

7. Google Cloud Dataflow

Best for: Stream and batch processing in Google Cloud environments

Google Cloud Dataflow is a fully managed service for building batch and streaming data pipelines within the Google Cloud ecosystem. It’s built on Apache Beam and designed for auto-scaling, so teams don’t have to manage servers or resources manually.

Google Cloud Dataflow’s key features include:

• Handles both real-time and batch data processing
• Ideal for event-driven pipelines and large-scale data workflows
• Serverless design with autoscaling to match workload demand
• Integrates tightly with BigQuery, Pub/Sub, and other GCP services

Dataflow works best for teams already working in Google Cloud and looking for a low-maintenance way to manage complex pipelines.

Pricing: Pay-as-you-go model based on resources used; costs can be hard to predict.

Learn more on the Fivetran + Google Cloud partner page.

8. Databricks

Best for: Unified batch/stream processing, analytics, and ML in a single platform

Databricks is a cloud-native platform built for data engineering, analytics, and machine learning. It’s known for its Lakehouse architecture, which combines the flexibility of a data lake with the structure of a warehouse — useful when working with raw event data and cleaned, modeled tables.

Databricks’ key features include:

• Handles batch and streaming pipelines with Delta Lake
• Includes collaborative notebooks for writing and testing transformations
• Supports version control, job scheduling, and real-time data processing
• Used for training models directly within ETL workflows

Databricks is a good choice for teams that want to keep analytics and ML workflows in one place. It’s often used to prep data for BI tools and train models on up-to-date inputs.

Pricing: Billed by compute and storage; total cost depends on workload size and usage tier.

Learn more on the Fivetran + Databricks partner page.

9. Snowflake

Best for: High-performance cloud data warehousing with flexible compute and storage scaling

Snowflake is a cloud data platform for storing, querying, and analyzing structured and semi-structured data. It separates storage from compute so teams can scale each independently based on workload demand.

Snowflake’s key features include:

• Handles structured, semi-structured, and JSON-like data
• Performs SQL-based transformations
• Integrates with most major ETL platforms, including Fivetran and dbt
• Creates separate virtual warehouses for different workloads
• Supports reporting, analytics, and data sharing between teams

Snowflake is popular among teams that need fast query performance, predictable scaling, and flexible storage across cloud providers. It’s commonly used as a central data destination in modern data stacks.

Pricing: Consumption-based; billed by compute usage, storage, and optional services like Snowpipe.

Learn more on the Fivetran + Snowflake partner page.

Choosing a data pipeline tool

Data pipeline tools range from open-source frameworks you manage yourself to fully managed services that handle setup and maintenance for you.

The key differences often come down to how much control you need, how much upkeep your team can handle, and how well a solution fits into your existing stack. Understanding these trade-offs helps teams focus on what actually matters: how well a tool fits their real-world needs, not just what features it includes.

Real-time vs. batch data pipelines

Real-time pipelines are useful when immediate visibility is essential, like when monitoring transactions or updating inventory. However, they require always-on infrastructure, which can drive up costs.

Batch pipelines run at scheduled intervals, making them more efficient for processing large volumes of data overnight or syncing reports at specific times. They’re easier to maintain and often less resource-intensive.

Bottom line

Real-time offers speed and responsiveness, while batch trades speed for simplicity and lower cost. The better fit depends on your timing needs and the systems you already have.

Open source vs. commercial data pipeline tools

Open source tools let businesses tailor the technology to their unique requirements while avoiding vendor lock-in. While they offer more flexibility and customization potential at a lower upfront cost, open source options come with significant technical overhead and limited support.

Bottom line

The choice depends on your internal resources, technical needs, and how much control you want over the tool’s configuration and maintenance.

Cloud-based vs. self-hosted data pipeline tools

Cloud-based pipelines reduce infrastructure overhead and scale easily, making them easier to launch and manage.

Self-hosted pipelines require more hands-on upkeep but give teams tighter control over security and infrastructure.

Bottom line

The better option depends on how much autonomy your team needs — and whether you’re set up to manage the environment yourself.

Quick decision guide

If you're still weighing your options, answer the questions below to find the best solution based on your team's priorities — whether that's in-house expertise, real-time functionality, customization, or warehouse compatibility.

Question 1: Do you want a fully managed, low-maintenance solution?

• Yes → Go to Question 2
• No → Consider Apache Airflow (code-driven orchestration) or Apache NiFi (hybrid/on-prem setups)

Question 2: Do you need broad, reliable connector coverage across SaaS apps, databases, and cloud services?

• Yes → Fivetran (Prebuilt connectors, automated syncs, and no-code setup)
• No → Go to Question 3

Question 3: Are you building custom pipelines with limited sources or specialized logic?

• Yes → Consider GCP Dataflow, AWS Data Pipeline, or Databricks (For stream/batch workflows, Spark-based ML, or AWS-native orchestration)
• No → Go to Question 4

Question 4: Do you need automatic CDC and schema drift handling to reduce engineering overhead?

• Yes → Fivetran (Fully managed CDC, auto-adapts to schema changes without manual intervention)
• No → Consider Stitch or Hevo Data (Basic support—may require custom transformations or connector modifications)

Case study: Fivetran and Westwing

Westwing, an e-commerce retailer specializing in home products, enhanced its marketing ROI by integrating Fivetran into its data architecture. The automation provided by Fivetran saves Westwing 40 hours of engineering time each week, allowing the team to focus on its strategic initiatives.

This shift to automation also allows the company to scale more effectively. The result is a more agile infrastructure that can keep pace with the company's growth.

The key advantages Westwing experiences with Fivetran include:

• Increased ROI from paid marketing campaigns through better analysis.
• Significant time savings in engineering, focusing resources on strategic growth.
• Enhanced customer insights, understanding interactions more clearly.
• Faster scalability without complex manual builds.
• Future-proofed data architecture to support ongoing expansion.

These benefits demonstrate how Fivetran can be a valuable asset for organizations seeking to streamline their data pipeline architecture while limiting resource expansion.

Read the Westwing case study.

Optimize your data workflows with Fivetran

Each tool offers specific benefits, but their value depends on your infrastructure, data volume, team size, and how often your sources change.

If minimizing maintenance and downtime is your top concern, consider Fivetran.

Our fully managed connectors automatically handle schema changes, support various destinations, and adapt to vendor updates with minimal oversight. This gives your team more time to analyze data instead of wrangling it.

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