TL;DR Short answer: Parquet is a file, so the question is where it sits, not what it is. Most of these tools read it from S3, GCS or Azure Blob and handle the schema automatically. Where they differ is whether you can self-host, whether they preserve types cleanly, and what happens when the schema changes. Thirteen tools:
Airbyte Fivetran Stitch Matillion Airflow Talend Pentaho Informatica PowerCenter Microsoft SSIS Singer Rivery Hevo Data Meltano What is Parquet, and how does it fit with ETL? Parquet is a columnar storage format built for analytics. It compresses well, supports schema evolution, and because it stores data column by column, a query that touches three columns out of fifty reads only those three. That is what makes it fast and cheap to query at scale, and it is why Parquet is the default file format in most data lakes. For ETL purposes the practical question is narrower than it sounds: Parquet is a file, so what matters is whether your tool can reach the object storage it sits in, and whether it preserves the schema and data types on the way out.
For simplicity, this guide uses "Parquet ETL" to refer to all data integration tools, ETL and ELT alike, that can read Parquet files.
Why move Parquet data into a warehouse? Business intelligence: Parquet File data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operationsCompliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.
Tool
Type
Connectors
Hosting
Open source
Reads Parquet from
Airbyte ELT
700+
Cloud and self-hosted
Yes
S3, GCS, Azure Blob, local files
Fivetran ELT
700+
Managed cloud
No
Cloud object storage
Stitch Extract and load
140+
Cloud
No, built on the open Singer spec
Cloud object storage
Matillion ELT
100+
Your own cloud account
No
S3, GCS, Azure Blob
Apache Airflow Orchestrator, not ETL
Not applicable
Self-hosted
Yes
Wherever you write code to reach
Talend Integration platform
Hundreds
Cloud and on-premises
No, Open Studio retired January 2024
Local files and object storage
Pentaho ETL and analytics
100+
Self-hosted
Open core
Local files and HDFS
Informatica PowerCenter ETL
200+
On-premises
No
Local files and HDFS
Microsoft SSIS ETL
Microsoft ecosystem
On-premises
No
Via custom script components
Singer Tap and target spec
Community taps, many stale
Self-hosted
Yes
Whatever the tap supports
Rivery ELT
150+
Cloud
No
Cloud object storage
Hevo Data ELT
150+
Cloud
No
Cloud object storage
Meltano ELT, CLI-first
Singer taps via Meltano Hub
Self-hosted
Yes
Whatever the tap supports
Which Parquet ETL tools should you consider? Here are the top Parquet File ETL tools based on their popularity and the criteria listed above:
1. Airbyte Airbyte is the leading open-source ELT platform, created in July 2020. It offers 700+ connectors and a community of more than 25,000 members. Major users include Siemens, Calendly and AngelList. Airbyte integrates with dbt for transformation and with Airflow, Prefect and Dagster for orchestration, and offers a UI, an API and a Terraform provider.
What's unique about Airbyte? Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch, and a no-code Connector Builder that handles most REST APIs without a local development environment.
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
2. Fivetran Fivetran is a closed-source, managed ELT service created in 2012, with 700+ vendor-built connectors. Connectors are maintained for you and schema changes are applied automatically, though you cannot change how a connector behaves.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran? Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Stitch is a cloud extract-and-load platform with 140+ connectors, originally built on the open-source Singer specification. It has no user-defined transformations and no log-based CDC.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch? Since Qlik acquired Talend, and Stitch with it, in 2023, Stitch has become one product line inside a much larger portfolio, and Qlik now publishes a formal migration path from Stitch to Qlik Talend Cloud. It is still quick to set up, but that direction of travel is worth weighing first.
Here are more insights on the differentiations between Airbyte and Stitch .
What else should you consider? 4. Matillion Matillion is an ELT platform created in 2011, built around pushdown transformation that runs inside your cloud warehouse. It supports 100+ connectors and covers extract, load and transform. It also integrates with dbt, which has shipped with the product since version 1.70.
What's unique about Matillion? Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.
Here are more insights on the differentiations between Airbyte and Matillion .
5. Airflow Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow? Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow .
6. Talend Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend? Talend pairs integration with data quality and governance, which suits teams whose Parquet files feed regulated reporting. Two things to check before shortlisting it: Qlik acquired Talend in 2023 and now sells it as Qlik Talend Cloud, and Talend Open Studio, the free open-source edition, was retired on 31 January 2024, so there is no free tier or self-serve route in.
7. Pentaho Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho? What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
8. Informatica PowerCenter Informatica PowerCenter is a mature enterprise ETL tool covering data profiling, cleansing and transformation, deployed in the customer's own infrastructure with no self-serve option. Two things to check before shortlisting it: Salesforce completed its acquisition of Informatica in November 2025, and PowerCenter 10.5 left standard support in March 2026.
9. Microsoft SSIS MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.
10. Singer Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
11. Rivery Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
12. Hevo Data HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
13. Meltano Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
All those ETL tools are not specific to Parquet File, you might also find some other specific data loader for Parquet File data. But you will most likely not want to be loading data from only Parquet File in your data stores.
How should you choose a Parquet ETL tool? As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your Parquet File ETL solution.
Parquet File's API gives access to various types of data, including:
• Structured data: Parquet files can store structured data in a columnar format, making it easy to query and analyze large datasets.
Overall, Parquet File's API provides access to a wide range of data types, making it a versatile tool for data storage and analysis in a variety of applications.
How do you start pulling data from Parquet files? If you decide to test Airbyte, you can start analyzing your Parquet File data within minutes in three easy steps:
Step 1: Set up Parquet File as a source connector 1. Open the Airbyte dashboard and select Sources.
Step 2: Set up a destination for your extracted Parquet File data Choose from one of 50+ destinations where you want to import data from your Parquet File source. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.
Step 3: Configure the Parquet File data pipeline in Airbyte Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes , or Airbyte Cloud which you can try here , free for 14 days.
Which Parquet ETL tool should you choose? This article outlined the criteria that you should consider when choosing a data integration solution for Parquet File ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing Parquet File ETL and how to best do it.
💡Suggested Reads MongoDB ETL Tools
N8n ETL Tools
Snowflake Data Cloud ETL Tools
Open Source ETL Tools
Data Integration Tools
.webp)