TL;DR Short answer: Parquet carries its own schema and types, so loading it is easier than CSV, and most warehouses and engines read it natively: Snowflake, BigQuery, Redshift, Databricks and DuckDB all load Parquet without an ETL tool. A pipeline tool earns its place when Parquet files keep landing in cloud storage and need to reach a database that cannot read them, such as Postgres, or sit alongside your other sources on one schedule. Ten options:
Airbyte Fivetran DuckDB Snowflake COPY INTO BigQuery load jobs Amazon Redshift Databricks Auto Loader Apache Spark Matillion Talend One Parquet constraint: Parquet types do not always map one-to-one to your destination. Nanosecond timestamps, high-precision decimals, unsigned integers and nested lists or maps are the usual casualties, and some loaders silently truncate or convert them. Check how your tool maps these types on a sample file before loading history.How is Parquet different from CSV? Parquet is a columnar file format built for analytics; CSV is row-based text. That difference changes how you load each:
Schema and types: Parquet stores its schema in the file, with real types for integers, decimals, timestamps and nested structures. CSV has no types, so every loader has to guess.Size and speed: Parquet compresses column by column, typically with Snappy or Zstandard, and readers can skip columns and row groups they do not need. The same data is usually several times smaller than CSV and far faster to scan.Readability: CSV opens in any text editor or spreadsheet; Parquet is binary and needs a tool to read, which is why converting Parquet to CSV is such a common request.For loading, Parquet's embedded schema means fewer surprises than CSV: no delimiter or encoding problems, and no type inference from a sample. What remains is type mapping into the destination and handling schema evolution as new files add columns.
How do the Parquet ETL tools compare? Tool Best for Destinations Picks up new files Pricing basis Airbyte Recurring files beside other sources Any, including Postgres Yes Free self-hosted; Cloud volume-based; Pro and Flex capacity-based Fivetran Managed recurring loads Any Yes Monthly active rows DuckDB Querying and converting files CSV, Postgres, MySQL, local tables No, you script it Free, open source Snowflake COPY INTO Files in a Snowflake stage Snowflake With Snowpipe Snowflake compute BigQuery load jobs Files in Cloud Storage BigQuery With scheduled transfers Free to load Amazon Redshift Files in S3 Redshift, or query in place With auto-copy or scheduling Redshift compute Databricks Auto Loader Lakehouse ingestion Delta tables Yes Databricks compute Apache Spark Very large datasets Any, including JDBC databases With your job logic Free; your cluster Matillion In-warehouse transformation Snowflake, Databricks, Redshift Yes Credits Talend Governed enterprise pipelines Broad With job design Quoted subscription
Which Parquet ETL tools should you consider? The list mixes pipeline tools, which load Parquet on a schedule alongside other sources, with the warehouses and engines that read Parquet natively and are often all you need.
1. Airbyte Airbyte is an open-source data integration platform with 700+ connectors and a community of 25,000+ data engineers. Its file-based sources for Amazon S3, Google Cloud Storage, Azure Blob Storage and SFTP read Parquet files matched by a glob pattern, take the schema from the files themselves, and pick up only new or modified files on each sync. That makes it a practical way to land Parquet in destinations that cannot read it natively, such as Postgres or MySQL, alongside your other sources.
What's unique about Airbyte? It works in both directions: Airbyte reads Parquet as a source and writes it through its S3 and data lake destinations, so the same platform can feed a lake and load from it. Connectors are open source and editable, and the Connector Development Kit covers anything else. It runs as self-hosted open source, on Airbyte Cloud, or as Enterprise Flex.
2. Fivetran Fivetran is a closed-source, managed ELT service with 700+ connectors, including file connectors for S3, Google Cloud Storage, Azure Blob Storage, FTP and SFTP that read Parquet along with CSV, JSON and other formats. It detects new files, applies schema changes automatically and bills on monthly active rows.
Here is a closer look at Airbyte vs Fivetran .
3. DuckDB DuckDB is a free, in-process analytical database with first-class Parquet support. It queries Parquet files in place with read_parquet or a plain file path, reads many files at once with a glob, reads directly from S3 or HTTP, and pushes filters down so it scans only the columns and row groups it needs. It can save the result as a table, write it out as CSV, or insert it into an attached Postgres or MySQL database, which makes it the quickest route for most one-off Parquet jobs.
4. Snowflake COPY INTO Snowflake loads Parquet natively. INFER_SCHEMA reads column definitions from staged files so you can create a matching table with CREATE TABLE USING TEMPLATE, and COPY INTO with MATCH_BY_COLUMN_NAME loads each column by name rather than position, which copes with files whose column order differs. Snowpipe runs the same load automatically as new files arrive, and external tables query Parquet in place.
5. BigQuery load jobs BigQuery loads Parquet from Cloud Storage with the schema taken from the files, including nested and repeated fields, and loading is free; you pay for storage and queries. BigQuery Data Transfer Service can schedule recurring loads from a bucket, and external tables or BigLake tables can query Parquet without loading it at all.
6. Amazon Redshift Redshift loads Parquet from S3 with COPY using FORMAT AS PARQUET, and Redshift Spectrum queries Parquet in S3 through external tables without loading it, which suits large, rarely queried history. Both read the column types from the files. It is the natural route when Parquet already sits in S3 and the warehouse is Redshift.
7. Databricks Auto Loader Auto Loader incrementally ingests new Parquet files from cloud storage into Delta tables, tracking which files it has processed and handling schema evolution as new columns appear. Databricks' COPY INTO covers simpler batch loads. It is the standard route for Parquet into a Databricks lakehouse, with the logic written in SQL or Python.
8. Apache Spark Apache Spark has read and written Parquet natively since the format's early days and is still the workhorse for very large Parquet datasets. A Spark job can read a partitioned dataset, transform it and write to any JDBC database, including Postgres, MySQL and SQL Server, or to another lake format. It needs a cluster, whether self-managed, EMR, Dataproc or Databricks, and you write the job yourself.
9. Matillion Matillion loads Parquet from cloud storage into Snowflake, Databricks or Redshift with storage load components and then transforms it inside the warehouse, so large files use compute you already pay for. It has dbt support since v1.70 and bills in credits tied to job runtime.
Here is a closer look at Airbyte vs Matillion .
10. Talend Talend has Parquet input and output components in its big data tooling and pairs integration with data quality and governance, which suits teams whose Parquet files feed regulated reporting. Qlik acquired Talend in 2023 and now sells it as Qlik Talend Cloud, and Talend Open Studio, the free edition, was retired on 31 January 2024, so there is no free tier or self-serve route in.
How do you convert Parquet to CSV? The quickest way is DuckDB, in a single statement: COPY (SELECT * FROM 'data.parquet') TO 'data.csv' (HEADER, DELIMITER ','). A glob such as 'folder/*.parquet' converts many files into one CSV, and adding a WHERE clause or a column list exports only what you need. In Python, pandas does the same with read_parquet and to_csv, and Spark handles datasets too large for one machine.
Expect the CSV to be several times larger and to lose type information: dates become strings, decimals may lose precision, and nested fields have to be flattened or serialised as JSON text first. If the CSV is only a step on the way into a database, it is usually better to load the Parquet directly.
How do you load Parquet into Postgres? Postgres cannot read Parquet natively, so there are three common routes:
DuckDB: attach the Postgres database with DuckDB's postgres extension and run INSERT INTO pg.my_table SELECT * FROM 'data.parquet'. Fast and free for one-off or scripted loads.A Postgres extension: the open-source pg_parquet extension adds COPY support for Parquet files, if you can install extensions on your server.A pipeline tool: for files that keep arriving in S3 or GCS, Airbyte reads them incrementally and writes to a Postgres destination on a schedule, with no code.How do you start loading Parquet files with Airbyte? Step 1: Set up your Parquet files as a source In Airbyte, open Sources, choose New source and select the connector for where the files live: Amazon S3, Google Cloud Storage, Azure Blob Storage or SFTP Bulk. Enter the bucket or container and credentials, such as an access key pair for S3, a service account for GCS, or a shared key or SAS token for Azure. Add a stream with a glob pattern such as warehouse/orders/*.parquet, choose the Parquet format, and run the connection test. Airbyte reads the schema from the files.
Step 2: Set up a destination Choose where the data lands: a database such as Postgres or MySQL, or a cloud warehouse such as Snowflake, BigQuery or Redshift.
Step 3: Configure the connection Use incremental sync so only new or modified files are read, check the mapped types for timestamps and decimals on a first run, and schedule syncs to match when files arrive.
The process is the same in Airbyte Open Source, which you can deploy yourself , and in Airbyte Cloud, which you can try free for 14 days .
Which Parquet ETL tool should you choose? If your warehouse is Snowflake, BigQuery, Redshift or Databricks, start with its native Parquet loader; it is fast, cheap and type-aware. For one-off conversions or loads into Postgres, use DuckDB. For Parquet that keeps arriving and needs to reach a database without native support, or land alongside your other sources on one schedule, use Airbyte, self-hosted or in Cloud, or Fivetran fully managed. Spark is for datasets too big for one machine, and Matillion and Talend fit where they already run.
For tools that cover more than Parquet files, see our guide to the top data integration tools .
Load Parquet files with Airbyte Pick up Parquet files from S3, GCS, Azure or SFTP incrementally and land them in Postgres, Snowflake, BigQuery or any Airbyte destination, self-hosted or in Airbyte Cloud.
Try Airbyte Cloud free for 14 days or talk to our team .
Frequently Asked Questions (FAQs) 1. Is Parquet better than CSV for loading data? For analytics, usually yes. Parquet carries its schema and types, compresses to a fraction of CSV's size and lets readers skip columns they do not need, so loads are faster and avoid type guessing. CSV wins when people need to open the file directly or a system can only read text.
2. How do you convert Parquet to CSV? With DuckDB, run COPY (SELECT * FROM 'data.parquet') TO 'data.csv' (HEADER, DELIMITER ','). In Python, pandas read_parquet followed by to_csv does the same. Nested fields need flattening first, and types such as dates become text in the CSV.
3. Can Postgres read Parquet files? Not natively. Use DuckDB with its postgres extension to insert Parquet into Postgres, install an extension such as pg_parquet that adds COPY support, or use a pipeline tool such as Airbyte for recurring loads from cloud storage.
4. How do you view a Parquet file? Parquet is binary, so you need a reader. DuckDB can preview it with SELECT * FROM 'file.parquet' LIMIT 10 and show its schema with DESCRIBE, and pandas, Spark and many desktop data tools can open it too.
Suggested reads CSV File ETL Tools
JSON File ETL Tools
Snowflake Data Cloud ETL Tools
Open Source ETL Tools
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