Most data pipeline tools are built for data engineers. They assume you know Python, understand DAGs, and have time to manage infrastructure.

That works great if you have a dedicated data engineering team. It doesn’t work if your analysts, finance team, or operations managers are the ones who actually need the data to move.

This guide covers the best data pipeline software available right now, organized by who each tool is actually built for.

What does data pipeline software do?

A data pipeline moves data from a source to a destination, usually with some transformation in between.

At the basic level: connect to a source, clean or reshape the data, send it somewhere useful. In practice it gets more complex, but that’s the core job.

Most tools follow either the ETL pattern (extract, transform, load) or ELT (extract, load, transform in the warehouse). The right pattern depends on your infrastructure and how much transformation you need.

The best data pipeline software

1. Mammoth Analytics: Best for business user pipelines

Mammoth is the only data pipeline tool on this list that was built for people who are not data engineers.

You connect your sources, describe what you want to happen to the data in plain language, and Mammoth builds the pipeline. No Python. No SQL. No YAML. Each transformation becomes a step in a reusable pipeline that runs automatically on a schedule.

One customer, a Python developer, built 20 to 30 pipelines in Mammoth specifically so his customer success team could own and maintain them. His exact words: he was no longer the bottleneck.

What the pipeline can do

Mammoth’s visual pipeline builder supports joins, filters, conditional logic, aggregations, text parsing, date calculations, deduplication, and more, all without code. You can also use natural language to describe a transformation and the platform generates the pipeline step automatically.

Pipelines run on a schedule and can be triggered by file arrivals, data consolidation events, or external API calls. Email notifications go out automatically when pipelines complete or fail.

What it connects to

Mammoth connects to hundreds of sources including Salesforce, SAP, BigQuery, Snowflake, Databricks, Redshift, HubSpot, MySQL, PostgreSQL, Google Sheets, and more. See the full connector list. Custom connectors can be built and deployed within about a week.

Outputs

Pipelines can push to Tableau, Power BI, Looker, BigQuery, Redshift, Snowflake, PostgreSQL, or export as CSV, Excel, or a live URL. Multiple output views can be created from the same source pipeline, which is useful when different teams need the same data shaped differently.

Who it’s for

Analysts, finance teams, and operations managers who need automated data pipelines but don’t want to rely on a data engineer to build and maintain them. It also works well for technical users who want to build pipelines their non-technical colleagues can actually run.

Pricing

Business tier starts at $16/month billed annually. Enterprise pricing is custom. See Mammoth pricing for details.

Proof

Starbucks processes over one billion rows monthly across 17 countries through Mammoth pipelines. A manufacturing customer reduced monthly close preparation time by 90% after automating 40+ manual steps into a single orchestrated pipeline.

Good fit if:

• Your analysts or ops team need to own and run pipelines themselves
• You want to automate recurring reports without involving IT
• You’re replacing messy Excel processes with structured, repeatable workflows

Not the right fit if:

• You need real-time streaming pipelines at massive scale
• Your team wants to write pipeline logic in Python or SQL
• You need complex cross-database query logic across dozens of databases on the same server

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2. Fivetran: Best for automated data ingestion

Fivetran is the go-to tool for getting data into a cloud warehouse reliably. It manages over 500 source connectors, handles schema drift automatically, and requires almost no ongoing maintenance once pipelines are set up.

Fivetran’s job is ingestion, not transformation. Most teams pair it with dbt for the transformation layer.

Pros:

• Extremely reliable, most connectors maintain 99.9% uptime
• Handles schema changes at the source automatically
• Minimal engineering time to maintain after initial setup
• Strong native integrations with Snowflake, BigQuery, Databricks, and Redshift

Cons:

• No transformation built in, needs a separate tool
• Usage-based pricing gets expensive at high data volumes
• Still requires technical resources to configure and operate
• Not usable by business users without engineering support

Bottom line: The strongest managed ingestion tool available. Pair it with dbt if you also need transformation. Not a fit if you need business users to own pipelines.

3. Apache Airflow: Best for custom pipeline orchestration

Apache Airflow is the most widely used open-source pipeline orchestration platform. You define workflows as DAGs (directed acyclic graphs) in Python, schedule them, and monitor them through a web UI.

It’s free, highly flexible, and runs on virtually any infrastructure. It’s also complex to set up, requires Python expertise, and demands ongoing maintenance.

Pros:

• Free and open source
• Extremely flexible, can orchestrate almost any workflow
• Large community and extensive plugin ecosystem
• Strong monitoring and logging capabilities

Cons:

• Steep learning curve, can take months to fully master
• Setup and infrastructure management require DevOps expertise
• Not suitable for non-technical users under any configuration
• Debugging complex DAGs can be painful

Bottom line: The standard choice for data engineering teams that need full control over complex, multi-step pipeline orchestration. Not appropriate for teams without dedicated Python engineers.

4. dbt: Best for SQL-based transformation on cloud warehouses

dbt (data build tool) has become the standard for transformation on cloud data warehouses. You write transformation logic in SQL, dbt compiles it and runs it in Snowflake, BigQuery, Redshift, or Databricks. Tests, documentation, and lineage come built in.

dbt doesn’t move data. It transforms data that’s already in your warehouse. It’s typically paired with Fivetran or Airbyte for ingestion.

Pros:

• Free open-source version with a large community
• Brings software engineering practices (testing, version control, documentation) to data transformation
• Excellent lineage and impact analysis
• dbt Cloud offers a managed, hosted version with a UI

Cons:

• Requires solid SQL knowledge to use effectively
• Only works inside a cloud data warehouse, not for multi-source preparation before the warehouse
• Doesn’t move data, needs an ingestion tool alongside it
• Business users cannot operate it

Bottom line: The standard transformation layer for warehouse-native data teams. Essential if you’re running an ELT stack. Not useful without a warehouse or SQL skills.

5. AWS Glue: Best for serverless pipelines on AWS

AWS Glue is Amazon’s serverless ETL service. It auto-generates PySpark code, runs transformations in a serverless environment, and integrates deeply with S3, Redshift, Athena, and the rest of the AWS ecosystem.

Pros:

• Serverless, no infrastructure to provision or manage
• Pay-per-use pricing avoids upfront licensing costs
• Built-in Glue Data Catalog simplifies metadata management
• Native integration with the full AWS data stack

Cons:

• Requires Python and Spark knowledge for real-world use
• Debugging and testing pipelines is more complex than in purpose-built tools
• Limited to AWS, poor fit for multi-cloud or hybrid environments
• No business user accessibility

Bottom line: Cost-effective for data engineering teams on AWS. Exclusively a technical tool.

6. Azure Data Factory: Best for pipelines in the Microsoft ecosystem

Azure Data Factory is Microsoft’s cloud ETL and orchestration service. For teams already running on Azure, SQL Server, Synapse Analytics, or Power BI, it fits naturally into the existing stack.

ADF has a visual pipeline designer but it’s not a no-code tool in practice. Configuring linked services, integration runtimes, and data flows requires solid Azure and data engineering knowledge.

Pros:

• Strong native integration with Azure Synapse, SQL Server, and Power BI
• Pay-as-you-go pricing
• Good hybrid support for on-premises and cloud sources
• Broad connector library

Cons:

• Requires data engineering expertise to build and maintain pipelines
• Costs grow quickly with data volume
• Poor fit for teams not already on Azure
• Business users cannot operate it independently

Bottom line: Good for data engineering teams on Azure. Not appropriate for self-service use.

7. Airbyte: Best for open-source data ingestion

Airbyte is an open-source alternative to Fivetran. It offers a large connector catalog (300+), a visual UI for configuring connections, and the ability to run self-hosted or through Airbyte Cloud.

The main appeal over Fivetran is cost control and flexibility. You can build custom connectors, host your own instance, and avoid usage-based pricing that scales with data volume.

Pros:

• Open source with a large, active community
• 300+ connectors, with the ability to build custom ones
• Self-hosted option keeps data within your own infrastructure
• Airbyte Cloud provides a managed option for teams that don’t want to self-host

Cons:

• Self-hosted requires infrastructure and DevOps expertise to maintain
• Connector quality varies, some are community-built and less reliable
• Transformation capabilities are limited, typically paired with dbt
• Still a technical tool, not suitable for business users

Bottom line: A strong open-source alternative to Fivetran for ingestion. Best for teams with engineering capacity who want flexibility and cost control.

8. Matillion: Best for ELT transformation on cloud warehouses

Matillion is a cloud-native ELT platform built specifically for Snowflake, BigQuery, Redshift, and Databricks. Its pushdown processing architecture runs transformations directly in the warehouse, which is typically faster and cheaper than moving data to a separate compute layer.

Pros:

• Pushdown architecture maximizes performance on cloud warehouses
• Visual interface with AI-assisted pipeline generation
• More accessible than code-first tools like dbt for less technical users
• Strong enterprise features including lineage and governance

Cons:

• Requires a cloud data warehouse as the foundation
• Still needs data engineering expertise for complex transformations
• Usage-based pricing can get expensive at scale
• Not designed for business user self-service

Bottom line: The strongest visual ELT tool for warehouse transformation teams. More accessible than dbt but still not for non-technical users.

How to choose

Who’s going to build and maintain the pipelines?

If the answer is data engineers, all of the tools above can work. If the answer is analysts, ops managers, or finance teams, Mammoth is the only option on this list built for that.

What is your primary use case?

• Move data into a cloud warehouse with minimal maintenance: Fivetran or Airbyte
• Transform data already in your warehouse: dbt or Matillion
• Orchestrate complex multi-step workflows in code: Airflow
• Build and automate pipelines without technical expertise: Mammoth Analytics
• Everything from source to dashboard in one platform: Mammoth Analytics

What infrastructure are you on?

• Fully on AWS: AWS Glue or Fivetran
• Fully on Azure: Azure Data Factory
• Cloud warehouse (Snowflake, BigQuery, Redshift): Fivetran plus dbt, or Matillion
• Multi-source with business user teams: Mammoth Analytics

Do you want to own the infrastructure or use a managed service?

• Managed, minimal maintenance: Fivetran, Mammoth, Matillion, ADF
• Self-hosted, maximum control: Airflow, Airbyte, dbt Core

Frequently asked questions

What is data pipeline software?

Data pipeline software automates the movement and transformation of data from source systems to a destination, like a data warehouse, BI tool, or database. It handles extraction, cleaning, reshaping, and loading so your team works with fresh, reliable data without manual effort.

What’s the difference between ETL and ELT?

ETL (extract, transform, load) cleans and reshapes data before loading it into the destination. ELT (extract, load, transform) loads raw data first, then transforms it inside the warehouse. Most modern cloud tools use ELT because warehouses like Snowflake and BigQuery are powerful enough to handle transformation efficiently.

Can non-technical teams build data pipelines?

With most tools, no. Airflow, dbt, AWS Glue, and Airbyte all require technical expertise. Fivetran and ADF need configuration by an engineer. Mammoth is the exception. Its visual pipeline builder is designed specifically so analysts and business teams can build, run, and maintain pipelines without writing code.

How does Mammoth handle pipeline scheduling and automation?

Mammoth supports scheduled dataset refreshes, automated file collection from Google Drive, Dropbox, OneDrive, and SFTP, data consolidation when new files arrive, and automated email notifications on completion or failure. Pipelines can also be triggered via API for external system integration.

What’s the best data pipeline tool for a small team?

It depends on your team’s technical makeup. For small teams with engineering capacity, Fivetran plus dbt is a fast, low-maintenance stack. For small teams without engineering resources who need to automate data workflows themselves, Mammoth Analytics is typically the better fit.

The bottom line

Most data pipeline tools are built on the assumption that a data engineer will build and own the pipelines. If that describes your organization, you have good options. Fivetran plus dbt is the modern standard for warehouse-native teams. Airflow gives you full control if you need custom orchestration. Matillion is the strongest visual option if your team lives in Snowflake or BigQuery.

But if your analysts are still moving data manually, your finance team is running monthly reports out of Excel, and your ops leads are waiting on IT for every data update, the tools above won’t change that. They move technical work from one engineer to another.

Mammoth Analytics is built for the team that actually uses the data. Pipelines built by the people who understand the business, maintained by the people who run the reports, without a bottleneck in the middle.

Request a Mammoth demo