English Vocabulary for DuckDB Users

DuckDB has quickly become a favourite tool among data engineers and analysts for its ability to run fast analytical queries directly on local files without a server. As its adoption grows, so does the need to discuss it fluently in English — whether in team meetings, technical write-ups, or job interviews. This post covers the vocabulary you need to talk about DuckDB confidently.

Key Vocabulary

In-process analytics DuckDB runs as an in-process engine, meaning it operates within the same process as your application rather than as a separate server. This eliminates network overhead and makes it ideal for local data exploration. Example: “We switched to DuckDB because in-process analytics let us query parquet files directly from our Python script without spinning up a data warehouse.”

Columnar storage Unlike traditional row-based databases, DuckDB uses a columnar storage format internally, which means data is stored and processed column by column. This makes aggregations and scans over large datasets significantly faster. Example: “DuckDB’s columnar storage is why it can scan billions of rows so quickly — it only reads the columns your query needs.”

Parquet Parquet is a columnar file format widely used in the data engineering ecosystem. DuckDB can query Parquet files directly, without importing them into a database first. Example: “We’re storing our event data as Parquet files in S3, and DuckDB can query them directly using its HTTPFS extension.”

OLAP (Online Analytical Processing) OLAP refers to query workloads that are analytical in nature — aggregations, joins across large datasets, and multi-dimensional analysis. DuckDB is optimised for OLAP, in contrast to transactional systems optimised for OLTP (Online Transaction Processing). Example: “DuckDB is an OLAP engine, so it’s excellent for reporting but not designed for high-frequency transactional writes.”

Extension DuckDB’s functionality can be expanded through extensions — modules that add support for additional file formats, data sources, or SQL functions. Common extensions include HTTPFS for remote file access and JSON for parsing JSON data. Example: “Install the HTTPFS extension if you want to query files stored on S3 or Azure Blob Storage.”

Common Scenarios Where This Language Is Used

In a data team standup: You might mention: “I’ve set up a DuckDB pipeline to process our raw event logs from S3. It reads the Parquet files directly, so we don’t need to load anything into Redshift for exploratory queries.”

In a technical comparison discussion: Teams often debate which tool is right for a task. “DuckDB is a good fit here because this is a batch analytics use case. If we needed real-time ingestion and concurrent writes, we’d need something like PostgreSQL.”

In a job interview: You may be asked to explain your data stack. “In my last role, I introduced DuckDB for local data validation tasks. It let analysts run SQL against CSV and Parquet files on their laptops without needing access to the production data warehouse.”

Useful Phrases for DuckDB Discussions

• “DuckDB runs in-process, so there’s no server to manage.”
• “You can query Parquet and CSV files directly using standard SQL syntax.”
• “DuckDB’s vectorised execution engine makes it very fast for analytical queries.”
• “We use DuckDB for local data exploration and Snowflake for production reporting.”
• “The HTTPFS extension lets you query remote files over HTTP or from cloud storage.”
• “DuckDB handles out-of-core processing, so it can work with datasets larger than your available RAM.”
• “The SQL dialect is very close to standard SQL, with some useful extensions like LIST aggregations.”
• “DuckDB can ingest data from PostgreSQL, SQLite, and other databases directly.”
• “This is a good use case for DuckDB because the query pattern is read-heavy with complex aggregations.”
• “We embedded DuckDB in our CLI tool to let users query their local data files without installing anything extra.”

Comparing DuckDB to Other Tools in English

A common conversation in data teams is comparing tools. Here is how to frame these comparisons clearly:

“DuckDB is similar to SQLite in that it runs in-process and stores everything in a single file, but it’s designed for analytical workloads rather than transactional ones. For large aggregations, DuckDB is typically much faster.”

“Compared to Spark, DuckDB is much simpler to set up and is ideal for single-node analytics. Spark shines when you need distributed processing across a cluster.”

“DuckDB and Polars serve similar use cases, but DuckDB uses SQL while Polars uses a DataFrame API. Both are excellent for local analytics.”

Using comparison phrases like “similar to,” “in contrast to,” “whereas,” and “unlike” will help you discuss trade-offs clearly.

Practice Suggestion

Write a short (200-word) technical summary in English explaining why you would or would not use DuckDB for a specific data task at your current or previous job. Focus on being precise about the workload type (analytical vs. transactional, batch vs. streaming, local vs. distributed) and explain your reasoning using at least three of the vocabulary terms from this post. Share it with a colleague for feedback.