DuckDB and Amazon S3: A Practical Data Validation Pipeline for Analytics

What the pipeline does

A tutorial published on dev.to walks through building a lightweight data validation pipeline with DuckDB, the open-source in-process analytical database, and shows how the same pattern extends to Amazon S3 and other AWS data services.

According to the dev.to guide, the project takes a raw sales CSV and passes it through DuckDB running inside a plain Python script. SQL rules flag common data quality problems: missing order or customer IDs, duplicate order IDs, zero or negative amounts, and unrecognized order statuses. The script then produces two outputs — one file of clean records and one of rejected rows, each with a reason attached.

The appeal, the author argues, is that DuckDB runs inside the application process rather than as a separate database server. That makes it a natural fit for lightweight ETL, data quality checks, filtering and aggregating large files, and preparing datasets before they reach a warehouse.

How the validation works

The script loads the CSV into a DuckDB table using the engine's automatic CSV reader, then applies a single SQL query built around a CASE expression. Each row gets a computed validation_error column: the first rule that matches sets the error message, and rows where no rule fires pass through with a NULL value. Duplicate detection is handled by a subquery that groups on order_id and keeps any ID that appears more than once.

The results are exported with DuckDB's COPY command into separate valid and rejected CSV files, and the script prints summary counts of total, valid and rejected rows.

On the six-row sample dataset — which deliberately includes a duplicated order, a missing customer ID, a negative amount and an unknown status — only one record survives. Both copies of the duplicated order are rejected, leaving a single valid row.

The author notes that the design reports only the first matching error per row, and suggests that production systems capture multiple errors per record and add stricter schema and type checks.

Extending the pattern to AWS

The second half of the tutorial moves the same logic into an AWS-shaped architecture. Raw files are uploaded to an S3 bucket (the guide uses the AWS CLI), a compute step runs the DuckDB validation — an AWS Glue job or an ECS container are suggested as suitable environments — and valid and rejected outputs are written to separate S3 prefixes such as validated/ and rejected/. From there, curated data can be registered in the AWS Glue Data Catalog and queried with Amazon Athena.

For analytical workloads the author recommends writing Parquet rather than CSV, since columnar storage reduces the amount of data scanned by queries. The guide also points to an AWS demonstration of DuckDB running inside Glue to read Parquet from S3 and write Apache Iceberg tables to Amazon S3 Tables, which is relevant for pipelines that combine SQL-based processing with lakehouse storage.

A comparison in the article positions DuckDB as lightweight analytical SQL and validation, S3 as object storage for raw and processed data, Glue as managed ETL, Athena as serverless SQL over S3, and Redshift as the warehousing layer — complementary tools rather than substitutes, with the right architecture depending on data volume and requirements.

Context: AWS and DuckLabs

The guide opens with a clarification worth repeating: DuckDB is not a newly launched managed AWS database service. According to the article, AWS announced a planned acquisition of DuckLabs, the company behind DuckDB, in August 2026, and has published integration examples pairing DuckDB with Glue and S3 Tables.

Why it matters

Data quality failures tend to surface downstream — in dashboards, financial reports and AI applications — where they are expensive to trace. A validation step that separates good rows from bad ones, with explicit reasons attached, makes those failures visible at ingestion time instead of letting them silently flow into downstream systems.

DuckDB's in-process model keeps that check cheap enough to run without standing up additional infrastructure, and the S3-based layout slots into existing AWS pipelines. With AWS moving to acquire DuckLabs and already demonstrating DuckDB inside Glue, integrations between the two are likely to deepen — making this pattern a low-cost way to add quality gates to analytics workloads today.