DuckDB is a high-performance, easy-to-integrate solution that fills the gap between lightweight embedded databases and full-scale data warehouses. It is easy to install and set up, flexible, and works well with data analysis pipelines that use Python.
DuckDB is a new solution that runs a high-performance SQL engine optimised for analytical workloads directly in Python. It can quickly query large datasets without needing any outside infrastructure because it stores data in columns and executes it in vectors. DuckDB works perfectly with Python and fills the gap between lightweight in-memory tools and heavy-duty data warehouses. This lets analysts and developers do scalable, fast, and efficient in-process analytics, and makes DuckDB an essential tool for modern data workflows that want things to be quick, easy, and adaptable.
What is DuckDB?
DuckDB is an open source, built-in SQL database that is made just for analytical workloads. The main idea is to offer fast, simple data analysis tools in a small, self-contained engine that can run directly in programs like Python. DuckDB is different from other databases because it is optimised for online analytical processing (OLAP). This makes it easy to query and combine large datasets, which is great for data exploration, reporting, and complex analytics.
DuckDB is better than SQLite at handling large, read-heavy analytical queries. SQLite is mostly used for online transaction processing (OLTP) tasks like small, fast read/write operations on individual records. SQLite is best for transactional workloads, while DuckDB is best for analytics tasks that involve complex aggregations, joins, and large dataset scans because of its columnar storage and vectorized execution engine.
Key advantages of DuckDB
DuckDB has several important benefits that make it a great tool for modern data analysis workflows. One of its most important features is in-process execution, which means it runs right inside your application or environment without needing to set up a server or use outside infrastructure. DuckDB is built on a high-performance columnar engine that is made for analytical workloads. Columnar storage is better than row-based storage for read-heavy analytical queries because it makes compression easier and speeds up data retrieval. This architecture speeds up operations like aggregations, joins, and filtering, making analytical contexts much faster than traditional row-based databases.
DuckDB works perfectly with Python and pandas, which is the most common way to work with data in Python. Users can use tools and workflows they already know, run SQL queries directly on DataFrames, and easily import and export data. This tight integration makes analytical workflows easier by combining the power of SQL with the flexibility of Python. It also lets data professionals do scalable, fast, and efficient in-process analytics without having to set up complicated systems or rely on outside tools.
How to install and set up DuckDB
It’s easy to install and set up DuckDB using pip, which is Python’s package manager. Just open your command line or terminal and type:
pip install duckdb
This command gets the most recent version of DuckDB and installs it so that you can use it in your Python environment. You can use DuckDB in your Python scripts or notebooks as soon as it is installed. The main interface is meant to be easy to use and similar to working with SQL. You can bring DuckDB into your program and use it to run SQL queries directly on data, whether it’s stored in memory or in a database file that doesn’t change.
You can connect like this to make an in-memory database, which is great for testing or doing temporary analysis:
import duckdb conn = duckdb.connect(‘memory’)
This makes a temporary database that only exists in RAM and is deleted when the connection closes. You can run SQL commands, and read and write data without having to worry about managing files. To create a file-based database that will last, give it a name:
conn = duckdb.connect(‘my_database.duckdb’)
This makes or opens a file on disk called ‘my_database.duckdb’. This file keeps track of all the changes you make to the data and schema, so you can keep your database up to date between sessions. The connection object lets you run SQL commands, which is the most basic use — for instance, making a table, putting data in it, and asking questions:
conn.execute(“CREATE TABLE users (id INTEGER, name VARCHAR)”) conn.execute(“INSERT INTO users VALUES (1, ‘Alice’), (2, ‘Bob’)”) result = conn.execute(“SELECT * FROM users”).fetchall() print(result)
You can also work directly with pandas DataFrames, which makes it easy to analyse data:
import pandas as pd
df = pd.DataFrame({‘id’: [1, 2], ‘name’: [‘Alice’, ‘Bob’]})
conn.register(‘my_table’, df)
result_df = conn.execute(“SELECT * FROM my_table”).fetchdf()
DuckDB makes it very easy to load and query data. It works with many different data formats and works well with pandas. To load data, DuckDB has simple SQL commands and functions that make it easy to work with CSV, Parquet, JSON, and DataFrames.
Reading data files
You can use the `READ_CSV`, `READ_PARQUET`, and `READ_JSON` functions to read common data formats directly into DuckDB. For instance, to load a CSV file:
CREATE TABLE my_csv AS SELECT * FROM read_csv_auto(‘data.csv’);
The `read_csv_auto` function automatically finds the schema, which makes things easier. The same goes for Parquet files:
CREATE TABLE my_parquet AS SELECT * FROM read_parquet(‘data.parquet’);
And for files in JSON format:
CREATE TABLE my_json AS SELECT * FROM read_json(‘data.json’);
These commands make tables right from external files, which makes it easy to explore data quickly without having to import it into a separate database engine.
Querying pandas DataFrames
DuckDB lets you query pandas DataFrames directly, which makes it easy to use Python data in SQL workflows:
import pandas as pd import duckdb df = pd.read_csv(‘data.csv’) con = duckdb.connect() con.register(‘df_view’, df) result = con.execute(‘SELECT * FROM df_view WHERE column_value > 100’).fetchdf()
This integration lets you run SQL queries on pandas DataFrames that are stored in memory. It combines the power of SQL with the flexibility of Python.
More advanced SQL operations
DuckDB has filtering, aggregation, joins, and window functions that make it easier to analyse complex data. For example:
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Filtering:
SELECT * FROM my_table WHERE sales > 1000;
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Groupings:
SELECT category, SUM(sales) FROM my_table GROUP BY category;
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Joins:
SELECT a.*, b.* FROM table_a a JOIN table_b b WHERE a.id = b.id;
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Functions for windows:
SELECT id, sales, RANK() OVER (ORDER BY sales DESC) AS rank FROM my_table;
These features let you carry out complex data manipulations right in DuckDB, using SQL’s powerful syntax. DuckDB is a flexible tool for loading, querying, and changing data from different formats. It works well with pandas to give a data analysis experience.
How to use DuckDB with big data
DuckDB is very efficient for working with large datasets because it can read data from the disk and stream it, which means it can query data that is too big to fit in memory. This is different from traditional in-memory databases, which read all the data from the disk.
Data that is bigger than memory
DuckDB can work with datasets that are bigger than your system’s RAM by breaking them up into smaller pieces. When you run a SQL query on a large dataset stored in a file (like Parquet), DuckDB only reads the parts of the file that are needed for the query. This means that you don’t have to load the whole dataset into memory, which makes it possible to do analytics on terabyte-sized data with modest hardware.
Streaming and out-of-core features
DuckDB is built to handle streaming data. It reads and processes data in a streaming way, so you can do complex things like joins, aggregations, or filtering on large datasets without using up too much memory. This makes it perfect for workflows that involve continuous data ingestion or real-time analysis, since DuckDB processes data in a streaming way.
Scanning Parquet files quickly
DuckDB is very good at quickly reading Parquet files. Because Parquet stores data in columns, DuckDB can only scan the columns it needs for a query, which cuts down a lot on I/O. For example:
SELECT column1, column2 FROM large_parquet_file WHERE filter_condition;
This selective reading cuts down on disk access and speeds up query execution. DuckDB also supports predicate pushdown, which filters data even more at the file level, cutting down on unnecessary data reads.
Using DuckDB to speed up pandas workflows
DuckDB can speed up pandas workflows a lot, especially when working with big datasets. It does this by acting as a fast query engine for pandas DataFrames, letting users use SQL’s expressive power to replace slow pandas operations.
Instead of using Python loops or complicated functions to go through DataFrames one row at a time, you can register pandas DataFrames in DuckDB and run SQL queries directly. This method takes advantage of DuckDB’s optimised query execution, vectorised operations, and smart use of CPU resources.
For instance, DuckDB can filter and combine large DataFrames in milliseconds, while pandas can take seconds or minutes to do the same thing:
import duckdb import pandas as pd df = pd.read_csv(‘large_dataset.csv’) con = duckdb.connect() con.register(‘df_view’, df) # Pandas work slowly # result = df[df[‘sales’] > 1000].groupby(‘category’).sum() # Quick DuckDB query result = con.execute(‘SELECT category, SUM(sales) FROM df_view WHERE sales > 1000 GROUP BY category’).fetchdf()
Using DuckDB’s optimised query engine, avoiding Python-level loops, and allowing parallel execution, this SQL-based method often leads to big performance improvements—sometimes 10 times or more.
How to work with Parquet files
DuckDB and Parquet files work well together for fast data analysis. Parquet’s columnar storage format is great for big data workloads because it has fast read speeds, compression, and efficient storage. DuckDB adds to this by having an embedded SQL engine that can query Parquet files directly without having to load or change the data.
One of the best things about using Parquet with DuckDB is that you can read more than one file at a time. DuckDB lets you treat a directory of Parquet files as a single table, making it easy to query large datasets that are spread out over many files. For example:
SELECT * FROM ‘path/to/parquet_directory/*.parquet’;
This method makes it easier to manage data and scales well, which makes it perfect for big data workflows. DuckDB also uses predicate pushdown and column pruning to speed things up. Predicate pushdown means that the filters in your SQL query are applied at the storage level, so only the data you need is read into memory. For example:
SELECT * FROM ‘data.parquet’ WHERE date >= ‘2023-01-01’;
This filter is sent down to the Parquet file, which cuts down on I/O by skipping over data blocks that aren’t needed.
Putting DuckDB into apps
DuckDB is great for embedded apps, Jupyter notebooks, and data science workflows where you need to quickly query and change local datasets. It can easily save databases to disk, which lets you store and share processed data for a long time. This makes it useful for ETL pipelines, ML preprocessing, and interactive dashboards.
DuckDB speeds up data transformations, feature engineering, and preprocessing in ETL and ML workflows by running complicated SQL operations quickly, often better than pandas and SQLite. For dashboards, it gives quick query responses, allowing for real-time data exploration with little delay.
DuckDB is often faster than pandas for large-scale data operations, sometimes by a factor of ten, because it uses vectorized execution and an optimised query engine. It is also better for analytical workloads, like window functions and complex joins, than SQLite, making it a better choice for analytical applications.
DuckDB is great for big data analytics, interactive data exploration, and embedded data pipelines in the real world.
Things to think about for production
When deploying DuckDB in production, you need to think carefully about the amount of data, how many people will be using it at once, and what the operational needs are. DuckDB is good for analysing large datasets, but it might not work well with data that is very large—usually more than hundreds of gigabytes—where distributed systems like Spark or distributed SQL engines may be better. For workloads that are bigger than these sizes, you might want to split the data up or connect it to external storage solutions.
Concurrency and multi-user access are also important. DuckDB is best for environments with only one user or low concurrency; it doesn’t support high levels of concurrent writes or multiple users at the same time, which is common in traditional client-server databases.
In conclusion, DuckDB has a lot of advantages: it’s lightweight, easy to embed, fast for analytical workloads, and supports SQL directly on local files. It’s best for embedded analytics, data science notebooks, ETL preprocessing, and prototyping. For large-scale, high-concurrency, or transactional systems, other solutions are better. Look at the specific workload needs, benchmark DuckDB in your environment, and add it to data pipelines to make the most of its strengths.
