Eliminate ETL Latency: Query Apache Iceberg and Parquet Directly from Amazon Aurora PostgreSQL

When you rely on reverse ETL pipelines to stitch together live transactional data and years‑old S3 archives, you’re paying a hidden price that shows up in every line‑item of your budget. Data duplication forces you to store the same records twice—once in your operational database and again in a downstream warehouse—so storage costs balloon while data‑drift bugs creep in unnoticed. The infrastructure needed to run, monitor, and scale those pipelines adds servers, networking, and a never‑ending stream of engineering tickets.

Beyond the obvious expense, the real pain is keeping historical S3 data in lockstep with fresh transactions. Each new schema change, latency spike, or data‑quality issue triggers a cascade of manual fixes, and the effort grows exponentially as you add more AI agents that need ad‑hoc access to any dataset.

• Duplication drains resources: Storing two copies of the same data inflates storage bills and creates synchronization headaches.
• Infrastructure overhead multiplies: Separate ETL jobs, orchestration tools, and monitoring dashboards demand dedicated ops time.
• Syncing historical S3 with live records is fragile: Every schema tweak or latency change can break the pipeline, leading to stale or missing insights.

Enter a smarter approach: query your data lake directly from your operational database. Amazon Aurora PostgreSQL now lets you run familiar PostgreSQL queries against Apache Iceberg and Parquet files stored in S3, eliminating the need for reverse ETL pipelines altogether. By leveraging the embedded DuckDB engine, Aurora can read historical data on‑the‑fly, apply predicate pushdown and column pruning, and return results in a single, low‑latency query.

Switching to direct lake querying delivers strategic benefits:

• Zero data duplication: One source of truth, no extra storage costs.
• Reduced infrastructure: No separate pipelines, less ops overhead, and fewer failure points.
• Instant synchronization: Live and historical data are always in sync, empowering real‑time dashboards and AI agents without pre‑replicating every dataset.

By retiring costly reverse ETL pipelines, you free up budget, simplify architecture, and give your teams the confidence that every query reflects the most accurate, up‑to‑date information—whether it lives in Aurora or in your S3 data lake.

When you try to combine live transactional data with years‑old records stored in a data lake, you’re forced into costly reverse‑ETL pipelines, duplicated datasets, and endless synchronization headaches. Now Amazon Aurora PostgreSQL lets you query Apache Iceberg and Parquet files directly—no data movement, no extra infrastructure.

With the Aurora Analytics extension, your existing PostgreSQL tools and applications can reach into Amazon S3, read Iceberg tables managed by the AWS Glue Data Catalog, and join that data with fresh rows in Aurora—all in a single, familiar SQL statement. The query runs inside Aurora, so there are no extra network hops and no need to pre‑stage historical data.

• Unified view, zero ETL: Join operational and lake data without building pipelines.
• Lower cost, lower risk: Pay only for the compute you use; avoid duplicate storage.
• Performance built‑in: Predicate push‑down, column pruning, and automatic caching keep scans fast even as your lake grows.
• Future‑proof: Powered by DuckDB, every open‑source improvement flows straight into Aurora.
• Simple setup: Enable the aurora_analytics extension, attach an IAM role, and create foreign tables that point to your Iceberg or Parquet files.

Imagine a real‑time dashboard that shows today’s sales alongside five years of historical trends, or an AI agent that enriches a transaction with past behavior—all without a single data‑copy operation. The same query that once required a nightly batch now runs on demand, delivering fresh insights exactly when you need them.

For hot‑data scenarios, you can materialize lake results into native Aurora tables with familiar CREATE TABLE AS SELECT or INSERT … SELECT commands, giving you sub‑millisecond latency while still preserving the low‑maintenance, single‑source‑of‑truth model.

Start today in any AWS region at no extra charge—just the Aurora compute you consume and the S3 read requests. Unlock a truly unified data experience and let your applications reason over live and archived data with the simplicity of PostgreSQL.

When you try to blend fresh transactional rows with years‑old S3 data, the usual answer is a costly ETL pipeline that duplicates data, adds latency, and forces you to maintain two separate systems. Embedded DuckDB inside Aurora PostgreSQL eliminates that friction, letting you run a single SQL statement that reaches both live tables and Parquet or Iceberg files without ever leaving the Aurora cluster.

Here’s how it works: the aurora_analytics extension activates DuckDB’s analytical engine directly inside Aurora. DuckDB reads the columnar layout of your S3 objects, applies predicate push‑down and column pruning, and streams only the rows you need. Meanwhile, Aurora continues to serve your OLTP workload, handling uncommitted writes and standard PostgreSQL features. The result is a unified query plan that executes in‑process—no extra network hops, no data movement.

• Speed up insight generation: Analytical scans of Parquet/Iceberg data happen at native DuckDB performance, while operational rows are fetched instantly from Aurora.
• Preserve existing investments: Use the same PostgreSQL drivers, tools, and application code you already trust—no learning curve, no rewrites.
• Cut infrastructure costs: Eliminate duplicate storage and the compute required for reverse‑ETL pipelines; you only pay for the incremental Aurora compute and S3 request fees.
• Future‑proof performance: As DuckDB evolves, Aurora automatically inherits engine improvements without any migration effort.

From a strategic perspective, this integration lets you enrich real‑time dashboards, power AI agents with both current and historical context, and materialize hot slices of lake data on‑demand using familiar commands like CREATE TABLE AS SELECT. Because read queries can run on any Aurora replica, you can offload heavy analytical scans away from your primary writer, preserving transaction throughput.

In short, embedding DuckDB transforms your data lake from a distant archive into an instantly queryable extension of your operational database—delivering high‑performance analytics while keeping your PostgreSQL workflow intact.

Getting Aurora Analytics up and running shouldn’t feel like building a rocket. The biggest friction most teams hit is wiring a secure bridge between their live Aurora PostgreSQL cluster and the massive data lake in Amazon S3. Once that bridge is in place, you instantly unlock the ability to query Iceberg or Parquet files side‑by‑side with your transactional tables—no ETL pipelines, no duplicated data, no endless sync jobs.

Step 1: Enable the aurora_analytics extension

• Run once, stay forever: Connect with any PostgreSQL client (psql, DBeaver, etc.) and execute CREATE EXTENSION aurora_analytics;
• This injects the DuckDB engine directly into Aurora, letting you read lake files without leaving the database.

Step 2: Attach an IAM role with AuroraAnalytics permissions

• Secure, least‑privilege access: In the RDS console, attach an IAM role that grants s3:GetObject and glue:GetTable on the buckets and catalogs you’ll query.
• The role is the key that lets Aurora reach your S3 objects and the AWS Glue Data Catalog.

Step 3: Create foreign tables that point to Iceberg or Parquet data

• Zero‑schema hassle: Use CREATE FOREIGN TABLE … SERVER aurora_analytics_server OPTIONS (location 's3://my-bucket/path/file.parquet', format 'parquet');. Aurora reads the schema from the file metadata automatically.
• Bulk onboarding: Run IMPORT FOREIGN SCHEMA to materialize every table in a Glue database with a single command.

With those three actions complete, you can write a single SQL query that joins fresh operational rows with years of historical data stored in your lake. Aurora automatically applies predicate push‑down, column pruning, and caching, so only the data you need is read from S3—keeping queries fast even as the lake grows.

Strategic payoff

• Eliminate reverse‑ETL pipelines: No more costly data duplication or sync engineering.
• Unified analytics surface: Real‑time dashboards, AI agents, and ad‑hoc reports all use the same familiar PostgreSQL syntax.
• Cost‑effective scaling: Pay only for the incremental Aurora compute and S3 request fees, with hot data optionally materialized for sub‑millisecond latency.

Start today in the Amazon RDS console, follow the Aurora PostgreSQL documentation, and watch your data lake become an instant extension of your operational database.

Hybrid queries that span live Aurora PostgreSQL tables and data‑lake files can feel like juggling fire—slow response times, duplicated pipelines, and endless tuning. The good news is that Aurora now embeds DuckDB, delivering built‑in performance tricks that let you treat operational and analytical data as one seamless source.

Start with the three core optimizations that keep every query lean:

• Predicate pushdown: Filters are sent straight to the Parquet or Iceberg files, so only rows that matter are read from S3.
• Column pruning: Aurora reads just the columns you request, avoiding the hefty I/O of full‑file scans.
• Smart caching: Frequently accessed lake data is cached in the Aurora instance; subsequent runs see dramatically lower latency and you can monitor cache hits with aurora_analytics_stat_statements().

These techniques translate directly into business value: faster dashboards, real‑time enrichment of transactions with historical context, and lower storage‑and‑compute costs because you no longer need reverse‑ETL pipelines that duplicate data.

When a query pattern demands single‑digit‑millisecond response, materialize the hot slice of lake data into a native Aurora table. Use familiar commands such as CREATE TABLE AS SELECT or MERGE INTO to copy just the needed rows. The materialized table lives on any Aurora instance—writer or read replica—so analytical scans stay off your primary workload while delivering the low‑latency path your users expect.

Putting it all together looks like this:

• Define foreign tables that point to Iceberg or Parquet files via the aurora_analytics extension.
• Run a single SQL statement that joins operational rows with lake data, letting Aurora handle predicate pushdown, column pruning, and caching automatically.
• Materialize hot data when latency thresholds tighten, using standard PostgreSQL DDL/DML without building a separate ingestion pipeline.

By leveraging these best practices, you eliminate data silos, cut engineering overhead, and unlock a unified view of live and historic data—all from the familiar PostgreSQL interface.

Explore the Amazon Aurora documentation to start querying your data lake directly today.