• What is a data pipeline?

  • How to use a Data Pipeline

  • Different ways to build data pipelines

  • Top 10 Data Pipeline Tools

  • Zoho DataPrep
  • Hevo
  • Fivetran
  • Airbyte
  • Alteryx Designer
  • Matillion ETL
  • Snowflake
  • Azure Data Factory
  • Apache Airflow
  • Integrate.io

  • Conclusion

  • Access Zoho dataprep

What is a data pipeline?

Data pipelines are automated systems where data is extracted from different data sources; transformed into a suitable format by cleaning, summarizing, and combining the information; and finally loaded into a specific database such as a data warehouse. Data pipelines are essential for companies to avoid the challenge of data movement and availability.

How to use a data pipeline

Step 1: Identify the purpose of your pipeline: Before moving any data, define what business question the pipeline needs to answer. For instance, if your goal is to build a 360-degree customer view, then the pipeline design should consider all customer interaction points throughout your entire system.

Step 2: Extract: Collect all necessary data from multiple sources- like CRMs, marketing tools, databases, and spreadsheets- that help achieve certain business goals. For instance, while working on a comprehensive customer perspective, extract data from the company’s CRM, support desk, and billing system.

Step 3: Transform:Prepare raw data by cleaning, filtering, aggregating, restructuring, removing duplicates, normalizing date formats, joining tables, and calculating new metrics.

Step 4: Load: Transfer the prepared data into Snowflake, Google BigQuery, or some other BI tool, like Zoho Analytics.

Step 5: Orchestrate: Set up automatic pipeline runs, deal with errors using retry logic, and make sure that the data is always fresh enough for business needs.

Step 6: Build strong data culture: Adopt the same pipeline pattern in multiple teams across the company in order to establish trust between them and facilitate data asset sharing.

Step 7: Automate and scale: Re-run, recreate, and enhance existing pipelines as data volumes grow.

Different ways to build data pipelines

There are two methods for building data pipelines: manual coding or self-service.

Manual data pipelines

Manual ETL pipelines are usually made using custom scripts through programming languages-such as Python with Pandas, SQL procedures, and even shell scripts-along with the help of library programs such as Apache Airflow for scheduling and orchestration purposes. They can handle larger data volumes with a high degree of control, making them suited for complex business requirements, but that flexibility comes at the cost of significant engineering effort, ongoing maintenance, and the risk of logic errors creeping in over time.

Automated and cloud-based data pipelines

If you prefer a visual approach, self-service ETL tools like Zoho DataPrep, Hevo, and Fivetran automate the pipeline creation process through cloud-based platforms. They include prebuilt connectors, transformation interfaces, automated schema drift detection capabilities, real-time data replication, and reverse ETL-which can sync data from data warehouses to operational systems such as CRM and marketing platforms-all while helping data engineers and business analysts build pipelines together. The downside is that complex multistage transformations on large datasets may still require coding, security needs careful attention, and data lineage and quality checks must be monitored to ensure accuracy.

Top 10 data pipeline tools

1. Zoho DataPrep

Zoho DataPrep is an AI-powered data transformation and ETL pipeline orchestration tool that lets users clean, transform, enrich, and move data between systems. Designed with an intuitive visual pipeline interface, it empowers non-technical users to build complete ETL workflows without coding skills. The platform offers a built-in AI assistant, Ask Zia, to easily prepare data and set up powerful automations to move data between various systems, including cloud data warehouses, CRMs, and databases.

Pros

User-friendly interface: Navigate the platform easily, even without a technical background. Build end-to-end pipelines visually.

AI-powered pipeline creation: Prepare, transform, and join data by simply chatting with Zia in natural language. AI-powered transforms are now fully powered by Zoho's own LLM.

MCP server integration: Zoho DataPrep supports (MCP Server) Model Context Protocol, allowing users to command pipelines using natural language directly from tools like Claude and Cursor.

Code Studio: A built-in Python scripting environment is now available within pipelines across the US, India, and EU data centers, giving power users first-class support for custom logic alongside the no-code interface.

Built-in functions: Get over 250 built-in transforms for joining, pivoting, appending, aggregating, and scheduling data, speeding up pipeline development.

90+ connectors: Zoho DataPrep now supports 90+ connectors, with further expansion on the roadmap.

Automation workflows: Create templates to simplify pipeline design and set up automated workflows to seamlessly move data on a schedule or with trigger-based events

Seamless integration: Easily connect with other Zoho products and numerous third-party applications, creating a cohesive ecosystem for existing Zoho users.

Databridge for hybrid environments: Seamlessly integrate on-premise data with cloud-based platforms through Databridge.

Security and compliance: Get encryption, user access controls, and certifications including GDPR, SOC 2, and HIPAA.

Cons

Primarily cloud-based: While Databridge helps with on-premise data integration, organizations looking for a fully on-premise pipeline solution may find limitations.

Learning curve for advanced features: Custom scripting and complex multi-branch workflows take time to master.

Who it's best suited for

Zoho DataPrep is best suited for business analysts, data teams, and organizations that want a user-friendly, AI-assisted way to build data pipelines without heavy coding. It's especially beneficial for companies already utilizing Zoho's suite of tools. Non-technical users will find the AI-powered pipeline creation and automation workflows particularly useful, while power users can extend workflows with Code Studio for advanced transformations.

2. Hevo

Hevo is a fully managed ETL solution that integrates data from over 150 different sources including databases, SaaS platforms, and SDKs - and ingests them to cloud data warehouses like Snowflake, BigQuery, and Redshift. In a recent announcement, Hevo announced an architectural change that claims to make its data replication process up to 20–40 times faster and reduce its total cost of ownership by 50–80%.

Pros

150+ prebuilt connectors: Get battle-tested integrations across databases, SaaS apps, and SDKs.

New microservices architecture: The overhaul delivers significantly faster replication and a modern Control Plane for observability.

Automatic schema handling: Schema changes at the source are detected automatically without breaking pipelines.

No-code interface: Build entire pipelines without writing a single line of code.

Fault-tolerant architecture: Use automatic retry logic and error management.

Cons

Pricing scales with events: The free tier includes one million events per month; the Starter plan begins at $239/month for 150+ connectors and up to 10 users. Costs can climb quickly as event volume grows.

Limited reverse ETL:Reverse ETL capabilities exist but are not as mature as dedicated tools.

Who it's best suited for

Hevo is highly suitable for startup ventures and midsize organizations that require managed ETL solutions with low infrastructural burdens. This tool can be greatly used by firms where source data schema is often subject to change.

3. Fivetran

Fivetran remains the industry gold standard for managed ETL, famous for seamless, maintenance-free replication of numerous sources into cloud warehouses. The brand promise of “set it and forget it” still stands despite drastic changes in its pricing model and feature set.

Pros

Zero maintenance: Automatically adapt to source API changes and new schemas.

Enterprise-grade reliability:Get 99.9% uptime SLA with robust monitoring and alerting.

700+ connectors: The Standard tier now provides access to over 700 fully managed connectors, a substantial jump from previous years.

Reverse ETL via Activations:Fivetran launched Activations, a cloud-based product enabling managed and automated reverse ETL pipelines without writing code, closing a historical gap.

Column-level lineage: Get end-to-end visibility into data flow.

Cons

Pricing has become more expensive:Fivetran transitioned to charging Monthly Active Rows (MAR) per connector rather than per account, eliminating bulk discounts. Inserts, updates, and deletes all count toward paid MAR, and a $5 minimum charge applies to every standard connection generating between one and one million. Costs can escalate quickly at scale.

Limited transformation: Fivetran focuses on extract-load; transformations are expected to happen inside the warehouse.

Who it's best suited for

For organizations operating on big budgets who emphasize consistency, a wide array of connectors, and automation over budget, Fivetran will prove to be a good fit. With the addition of Activations, Fivetran has become an even more attractive option for those requiring a solution for both ETL and reverse ETL.

4. Airbyte

Airbyte is the most popular open-source ETL tool used to replicate data from a huge number of sources to various destinations. According to Airbyte’s official connector catalog, there are over 600 connectors available, while the company’s community Connector Builder has allowed the creation of more than 10,000 custom connectors, which means that Airbyte manages the replication of data from an incredible amount of different sources.

Pros

600+ official connectors and 10,000+ community connectors:This is a dramatic expansion from previous years, with a Connector Builder that lets teams create and share custom connectors in Python, Java, or low-code.

Open-source and flexible:The self-hosted version has no per-row or per-seat costs, giving teams complete control over data sovereignty.

New pricing tiers:Airbyte currently offers Free, Pro, and Enterprise pricing, with Airbyte Cloud using a credit-based model.

Major performance improvements:High-volume Snowflake syncs are now up to 95% cheaper, and PostgreSQL-to-S3 transfers that previously took two days now complete in about two and a half hours.

No vendor lock-in:Run on your own VPC, Kubernetes cluster, or on-premise servers.

Cons

Self-hosting requires engineering:Managing Airbyte at scale requires solid Kubernetes knowledge, and infrastructure upgrades and scaling remain a non-trivial ongoing commitment.

Monitoring less mature than Fivetran:Operational dashboards and alerting, while improving, still lag behind fully managed competitors.

Transformation limited:The primary focus is extract-load; transformation is expected elsewhere (for example, dbt).

Who it's best suited for

Airbyte is ideal for those data engineers who require full flexibility in terms of pipeline infrastructure management, require specific connectors for certain sources, or cannot afford row-by-row pricing. The recent introduction of Plus and Pro tiers made it even more appealing.

5. Alteryx Designer

Alteryx Designer offers all-around ETL, pipelining, and advanced analytics software. The application features a drag-and-drop tool with more than 300 ready-made features that allow data extraction, transformation, blending, and loading. With Alteryx Designer, you can design complicated pipelines and run code for advanced data transformation.

Pros

300+ built-in tools:There's an extensive library for joining, filtering, aggregating, spatial analysis, and predictive analytics within a single pipeline.

User-friendly interface:The drag-and-drop workflow builder simplifies pipeline construction.

Code-friendly: The Formula tool and Run Command tool allow Python, R, and SQL integration for complex transformations.

End-to-end pipelines:It handles extraction, transformation, loading, and analytics in one visual environment.

Security:It has compliance certifications including HIPAA, SOC 1 and 2, and GDPR.

Cons

Steep learning curve: The sheer number of tools and configuration options can overwhelm non-technical users.

Limited OS support:The desktop version runs on Windows only.

Separation between desktop and cloud:Alteryx Designer Desktop and Alteryx Analytics Cloud remain separate products with no easy migration path between them.

Pricing: Alteryx is generally one of the more expensive options, and pricing varies by edition and team needs.

Who it's best suited for

Alteryx Designer is best suited for data analysts, data scientists, and business intelligence analysts requiring a powerful ETL and advanced analytics solution in the Windows environment.

6. Matillion

Matillion is a cloud-based data pipeline solution designed for cloud data warehouses like Amazon Redshift, Snowflake, Google BigQuery, and Microsoft Azure Synapse Analytics. Matillion's ETL tool comes with a drag-and-drop interface that allows users to extract data from different sources, transform them, and load the output into a cloud data warehouse. Additionally, Matillion releases new updates that feature enhanced connectors for Salesforce, HubSpot, and Shopify.

Pros

User-friendly interface:Drag-and-drop design makes pipeline building approachable for users without deep coding experience.

Easy setup: It has fast deployment and quick time-to-value.

Advanced customization:SQL scripting and Python-based transformations are available for complex data manipulation.

Cloud-native:There's no infrastructure maintenance, and it integrates seamlessly with major cloud warehouses.

Orchestration built in:It has native scheduling, dependency management, and pipeline chaining.

Security and compliance:Encryption, access control, and compliance with GDPR and SOC 2 come standard.

Cons

Learning curve for advanced workflows:Sophisticated transformations still require effort to master.

Cloud platform dependency: It's tightly coupled to your chosen cloud data warehouse, making migrations difficult.

Cost considerations:Cloud warehouse compute costs can climb with high data volumes and processing frequency.

Limited SaaS connectors:With 150+ prebuilt connectors, Matillion still has fewer native SaaS connectors than Fivetran or Hevo.

Who it's best suited for

Matillion ETL works well for data engineers and analysts who have chosen a cloud data warehouse like Snowflake, Redshift, BigQuery, or Synapse and want a complete pipeline solution that sits alongside it. It's a strong choice for teams that want cloud-native ETL without giving up orchestration and dependency management.

7. Snowflake

Snowflake is mainly a cloud data warehouse; however, due to the addition of Dynamic Tables, Streams, and Tasks, it acts as a highly efficient pipeline engine. With Dynamic Tables, you can create declarative and streaming pipelines without using any external orchestrators. It can all be done with simple SQL commands.

Pros

No external movement:Data stays inside Snowflake, so there's no egress costs or security boundaries.

Declarative pipelines:Define your desired state with Dynamic Tables; Snowflake handles incremental refresh.

Streaming native:Snowpipe Streaming supports sub-second latency.

Immutability constraints:Immutability constraints let users lock specific portions of Dynamic Tables so they don't change during refreshes, reducing recomputation costs.

Filter-by-current-date and backfill:Recent additions bring meaningful cost optimization to long-running pipelines.

Governance built in:Row- and column-level security and data masking apply automatically.

Cons

SQL-only interface:Non-technical users cannot build pipelines visually.

Snowflake lock-in: Pipelines are not portable to other warehouses.

Costs can spiral: Compute credits add up, especially for high-frequency refreshes.

Who it's best suited for

Snowflake as a pipeline engine is ideal for data engineers and analysts who work with Snowflake regularly. It would benefit organizations that enjoy creating their pipelines in SQL without dealing with the hassle of an orchestrator.

8. Azure Data Factory (ADF)

Azure Data Factory is a Microsoft cloud-based data integration platform that features more than 90 prebuilt connectors along with a drag-and-drop visual interface for building pipelines. However, there's a significant strategic shift underway: Microsoft has moved its primary development focus to Fabric Data Factory, and few significant ADF updates were released.

Pros

Deep Microsoft integration: It integrates seamlessly with Azure Synapse, Power BI, Logic Apps, and SQL Server.

Hybrid capabilities: Self-hosted runtimes connect to on-premise and VNet data sources.

SSIS lift-and-shift: Run existing SQL Server Integration Services packages in the cloud.

90+ native connectors: It has a broad base of integrations for Azure-centric environments.

Visual pipeline designer: Map data flows with a drag-and-drop interface.

Cons

Now effectively in maintenance mode:The recent roadmap shows limited new feature investment in ADF itself. New capabilities like mirroring and copy jobs are being built exclusively in Fabric Data Factory and are not being backported. A migration assistant recently launched, signaling the direction Microsoft expects customers to take.

Complex pricing:Charges for pipeline orchestration, data movement, and data flow activity are calculated separately.

Steep learning curve:Many options and services can overwhelm new users.

Limited real-time:It's primarily batch-oriented; real-time scenarios require additional services.

Who it's best suited for

ADF is still a reasonable choice for organizations deeply invested in Microsoft Azure with existing SSIS workloads or hybrid data landscapes. However, teams starting new projects should seriously evaluate Fabric Data Factory instead, given where Microsoft's investment is headed.

9. Apache Airflow

Apache Airflow is the industry-standard workflow orchestration platform. It neither extracts nor loads any data but manages complicated workflows implemented in Python as Directed Acyclic Graphs (DAGs).

Pros

Programmatic control:Define pipelines as code in Python, enabling version control, testing, and CI/CD.

Extremely flexible:Integrate with any system via 500+ custom or community operators.

Airflow 3.2.0:The latest release introduces major features like asset partitioning and multi-team support, a significant evolution for large organizations running shared Airflow deployments.

Active open-source community:There are thousands of contributors and extensive documentation.

Rich UI: Visualize DAGs, task logs, retries, and historical runs.

Cons

Not a no-code tool:It requires strong Python skills; business users cannot build pipelines directly.

Orchestration only:It doesn't extract, load, or transform data on its own, so it must be combined with other tools.

Operator maintenance:Community operators can lag behind source API changes.

Who it's best suited for

Airflow is perfect for data engineering teams building complex orchestrations with infrastructure as code-for example, pipelines that run a Python script, wait for a file to land on S3, trigger a dbt job, and notify Slack. With multi-team support in 3.2.0, it's now significantly more attractive for large organizations running Airflow as a shared platform.

10. Integrate.io

Integrate.io offers a cloud-based ETL and data integration tool that supports API integrations and allows the creation of no-code workflows. The prebuilt connectors for databases, applications, and data warehouses enable seamless data integration without having to bother about any infrastructure costs

Pros

Easy-to-use interface:The drag-and-drop builder makes pipeline creation simple for non-technical users.

Prebuilt connectors:Streamline integration with a wide range of data sources.

Cloud-native: There's no local infrastructure to manage.

Fixed-fee pricing: Integrate.io offers fixed-fee pricing starting at $1,999/month, including unlimited data volumes, unlimited pipelines, and unlimited connectors-a predictable model that contrasts sharply with consumption-based competitors.

Security and compliance:It complies with GDPR, HIPAA, and SOC 2.

Cons

Advanced features take time to master:Complex transformations have a learning curve.

Hybrid configuration: Connecting both on-premise and cloud data may require additional setup effort.

Optional scripting: Mastering scripting for advanced transformations adds complexity for some teams.

Who it's best suited for

Integrate.io is very useful for data engineers and analysts who need a simple yet efficient cloud-based solution for ETL and API integrations. With its fixed price structure, it's great for businesses dealing with varying amounts of data volumes that don't want surprise row charges.

Conclusion

Choosing the right data pipeline tool comes down to your engineering resources, budget model, and your team's technical comfort level. If you have the budget and Python expertise, Fivetran and Airflow remain incredibly powerful for enterprise-scale work, and Fivetran's new Activations product means it can now handle reverse ETL, too. If you want open-source flexibility with massive connector coverage, Airbyte's 600+ official and 10,000+ community connectors give you complete control. If predictable pricing matters more than anything, Integrate.io's flat-fee model is hard to beat.

But if you want to eliminate maintenance, empower your analysts with AI-assisted pipeline building, and get your data moving in minutes without writing a single line of code, try Zoho DataPrep. With AI-powered transformations via Ask Zia, MCP integration for natural-language control from tools like Claude, Code Studio for Python power users, 250+ built-in functions, seamless integration across the Zoho ecosystem, and 90+ third-party connectors, Zoho DataPrep turns messy, siloed data into clean, pipeline-ready information without the complexity.

Try Zoho DataPrep for free today or book a personalized demo and see why it's the #1 choice for teams who value both power and simplicity.

Set up your first integration for free today.

Get Started