Data pipeline tools form the data pipeline method and its processes. The method itself serves to load raw data from various data sources (databases, spreadsheets etc), transform it and then store it in a data warehouse, data lake or database. Basically, data pipeline tools move data from one point to another, while transforming it during the transport.
For each stage of the process, you may need different ones. There are many available on the market, both open-source and commercial. Sometimes, one tool can cover multiple stages.
How to select the optimum data pipeline tools?
Scalability
Can your current systems handle your current and projected data needs? Your new data pipeline tools must be able to handle increasing amounts of data. Cloud-based pipeline solutions provide scalability. Keep in mind that there are no end-to-end solutions, especially if you have specific needs, which most organisations do.
Data sources
Make sure that the tools you select support your current data sources and analytics solutions, as well as those you may need in the future.
Data integration
Companies want to make better business decisions, which in the case of data means achieving operational efficiency using automated workflows, real-time data processing and consolidating data scattered across different sources.
The best data pipeline tools resolve these challenges by automating outdated manual processes, such as ETL (extract-transform-load). They clean, transform and integrate data. Also, they integrate well with your existing infrastructure.
Security and compliance
Is the tool of your choice compliant with data protection regulations such as GDPR and CCPA? Does it provide data encryption, privacy and security? Check with your legal team.
Support and documentation
Since the data pipeline is a critical factor in your business operations, choose providers that are skilled, knowledgeable and have fast response times. See if the documentation is detailed and comprehensive.
Cost
Take into account the cost of initial investment, license, implementation, maintenance and evaluate the total cost of ownership of your data pipeline tool. See if it provides a good return on investment.
With numerous tools available, choosing the right one can be challenging. Here, we explore some of the best data pipeline tools available in 2025, categorized based on their primary role in the ETL process.
1. Extraction Tools (Data Ingestion & Collection)
Best Commercial: Fivetran
Description: Fully managed ELT service that automates data extraction from various sources.
Most Common Use Cases: Syncing SaaS applications, databases, and cloud storage to data warehouses.
Location: Cloud-based, operates globally with support for major cloud providers.
Competitors: Stitch, Matillion, Airbyte
Best Open-Source: Airbyte
Description: Open-source ELT tool with a growing list of connectors to extract data from diverse sources.
Most Common Use Cases: Ingesting data from APIs, databases, and cloud apps into warehouses like Snowflake or BigQuery.
Location: Self-hosted or cloud-deployed (offers managed cloud version too).
Competitors: Singer, Meltano, Apache NiFi
Most Widespread: Apache NiFi
Description: Data ingestion tool designed for real-time, high-volume data streams.
Most Common Use Cases: IoT data streaming, real-time log collection, and enterprise data flows.
Location: Self-hosted, commonly deployed in large-scale distributed environments.
Competitors: Apache Kafka, Google Dataflow, AWS Kinesis
2. Transformation Tools (Processing & Enrichment)
Best Commercial: Matillion
Description: A cloud-native ELT tool focused on transforming data within modern cloud data warehouses.
Most Common Use Cases: Cloud-based transformations for Snowflake, Redshift, and BigQuery.
Location: SaaS-based, integrates with major cloud platforms.
Competitors: Fivetran, Talend, Informatica
Best Open-Source: DBT (Data Build Tool)
Description: SQL-based transformation tool enabling analytics engineers to build robust data models.
Most Common Use Cases: Transforming raw data into analytics-ready models within cloud data warehouses.
Location: Cloud and self-hosted options available.
Competitors: Dataform, Meltano, Apache Beam
Most Widespread: Apache Spark
Description: Distributed computing engine for large-scale data transformation and analytics.
Most Common Use Cases: Big data processing, machine learning workflows, and streaming analytics.
Location: Deployed on-premise, in Hadoop clusters, or cloud environments (AWS EMR, Databricks, etc.).
Competitors: Apache Flink, Google Cloud Dataflow, Dask
3. Loading Tools (Storage & Destination Management)
Best Commercial: AWS Glue
Description: A serverless data integration service designed for automating ETL workloads.
Most Common Use Cases: Loading structured and unstructured data into AWS-based storage solutions.
Location: Cloud-based, deeply integrated with AWS ecosystem.
Competitors: Google Cloud Dataflow, Azure Data Factory, Informatica
Best Open-Source: Singer
Description: Open-source standard for writing reusable ETL scripts to load data into various destinations.
Most Common Use Cases: Moving data from APIs, databases, and files to warehouses with minimal setup.
Location: Self-hosted, with compatibility across multiple cloud and on-prem solutions.
Competitors: Airbyte, Meltano, Talend Open Studio
Most Widespread: Talend
Description: A comprehensive data integration platform supporting ETL, data quality, and governance.
Most Common Use Cases: Enterprise-grade ETL, data lake loading, and cloud migration.
Location: Hybrid, available as both self-hosted and cloud-based solutions.
Competitors: Informatica, IBM DataStage, Microsoft SSIS
4. Orchestration & Workflow Automation (Managing End-to-End Pipelines)
Best Commercial: Astronomer (Managed Apache Airflow)
Description: A managed platform for deploying and scaling Apache Airflow pipelines.
Most Common Use Cases: Automating workflows, task dependencies, and scheduled jobs in a scalable way.
Location: Cloud-based, with support for hybrid deployments.
Competitors: Prefect Cloud, AWS Step Functions, Google Cloud Composer
Best Open-Source: Prefect
Description: Workflow orchestration tool designed for dynamic pipeline execution.
Most Common Use Cases: Automating and monitoring ETL jobs with Python-first workflows.
Location: Self-hosted or cloud-based (Prefect Cloud).
Competitors: Dagster, Apache Airflow, Luigi
Most Widespread: Apache Airflow
Description: The de facto standard for workflow scheduling and orchestration.
Most Common Use Cases: Managing DAG-based workflows for ETL, machine learning, and cloud operations.
Location: Self-hosted, widely used across cloud and on-premises environments.
Competitors: Prefect, Dagster, Google Cloud Composer
5. Lakehouse & Hybrid Tools (Storage, Processing & Orchestration Combined)
Best Commercial: Databricks
Description: A unified analytics platform built on Apache Spark, combining data lake storage, transformation, and AI/ML workflows.
Most Common Use Cases: Big data processing, data science, AI/ML model training, and analytics.
Location: Cloud-based, available on AWS, Azure, and Google Cloud.
Competitors: Snowflake, Google BigQuery, AWS Athena
Best Open-Source: Apache Iceberg
Description: An open-source table format designed for large-scale data lakes, offering ACID transactions and schema evolution.
Most Common Use Cases: Managing structured data in a lakehouse, improving query performance, and simplifying analytics workflows.
Location: Self-hosted, integrates with major cloud and on-prem storage solutions.
Competitors: Delta Lake, Apache Hudi, Snowflake
Most Widespread: Snowflake
Description: A cloud-based data warehouse with built-in lakehouse capabilities, supporting ELT and AI-driven analytics.
Most Common Use Cases: Storing and querying structured & semi-structured data, enabling large-scale BI and machine learning.
Location: Cloud-based, available on AWS, Azure, and Google Cloud.
Competitors: Databricks, Google BigQuery, AWS Redshift
Conclusion
Selecting the right data pipeline tool depends on factors such as scalability, integration needs, real-time processing requirements, and budget constraints. Whether you need an open-source workflow orchestrator like Apache Airflow, a fully managed service like AWS Glue, or an automated data integration tool like Fivetran, each option has unique strengths. By evaluating your specific needs, you can choose the best data pipeline tool to enhance your data processing workflows.
Contact us now to discuss your data engineering needs:
A big thank you to Petar Apolonio, who co-authored this article. Petar is Manager of Data Science Excellence at Q agency. A mathematician and data enthusiast, he is a data engineer by profession. Petar oversees all the projects that involve data science, data engineering and data analysis for our clients, as well as internal ones.
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Luka Maras
