“Data scientists don’t use databases until they have to.”
– CTO of DuckDB.
DuckDB is a desk-oriented database management system (DBMS) that supports the Structured Query Language (SQL). It is an effective and lightweight DBMS that transforms data analysis and analytics of massive datasets. While there are many DBMS available, most of them are tailored to unique use cases with specific trade-offs. No single database may be the appropriate fit for all applications. In this article, we see how DuckDB can be used for a variety of diverse use cases and how it compares with other databases like SQLite.
Before we get into the meat of the topic, let’s first understand why existing databases fall short for data scientists.
Learn More: Understanding the need for DBMS
Amidst all of these drawbacks, DuckDB appeared as a promising solution for analytical databases. Here are the features that make DuckDB the best choice for data analysts.
SQLite is an extensively used, serverless, self-contained SQL database engine. It is known for its simplicity, reliability, and small footprint. Moreover, it’s a lightweight database, which makes it the perfect choice for embedded structures, cellular packages, and small to medium-sized applications.
However, SQLite does have some limitations.
DuckDB can be easily installed on different platforms using pip on python:
Windows: pip install duckdbmacOS: brew install duckdbLinux: DuckDB is not available directly via apt or yum repositories. Installation may require compiling from source or using alternative installation methods.
You can also use this link to try the commands.
Example
CREATE TABLE my_table AS SELECT * FROM read_csv('path_to_csv_file.csv');
DuckDB seamlessly integrates with Python’s Pandas library, facilitating the transfer of data between Pandas DataFrames and DuckDB tables. This integration streamlines the workflow for data scientists accustomed to working with Pandas.
Example
import pandas as pd
import duckdb
# Create a Pandas DataFrame
df = pd.read_csv('data.csv')
# Connect to DuckDB and insert Pandas DataFrame into DuckDB table
con = duckdb.connect(database=':memory:')
con.register('df', df)
con.execute('CREATE TABLE duckdb_table AS SELECT * FROM df;')DuckDB’s SQL-compatible interface allows data scientists to perform a wide range of queries directly within the DuckDB shell or through integrated development environments (IDEs) like Jupyter Notebooks.
Example (in DuckDB shell)
-- Basic SELECT query
SELECT * FROM duckdb_table WHERE Age > 50;
-- Aggregation query
SELECT SUM(Fare) AS total_sum FROM my_table;
-- Join query
SELECT t1.column1, t2.column2
FROM table1 t1
JOIN table2 t2 ON t1.id = t2.id;DuckDB helps us study data from numerous sources including CSV documents, Parquet files, and Pandas DataFrames. This flexibility allows facts scientists to seamlessly integrate data points from special codecs into their analytical workflows.
Example
-- Import data from Parquet file
CREATE TABLE parquet_table AS SELECT * FROM read_parquet('path_to_parquet_file.parquet');Now let’s explore the diverse use cases of DuckDB.
DuckDB may be embedded in net applications to offer real-time analytics. For instance, an e-trade website can use DuckDB to analyze consumer behavior, tune income tendencies, and generate dynamic reports without delay inside the utility.
Example:
from flask import Flask, jsonify
import duckdb
app = Flask(__name__)
con = duckdb.connect(database=':memory:')
@app.route('/analytics')
def analytics():
result = con.execute("SELECT * FROM user_activity WHERE action = 'purchase'").fetchall()
return jsonify(result)
if __name__ == '__main__':
app.run()DuckDB can be integrated into desktop applications written in Java or Python for enhanced data processing capabilities. It allows these applications to perform complex queries and data analysis without the need for an external database server.
Example (Python)
import duckdb
def perform_analysis(data):
con = duckdb.connect(database=':memory:')
con.execute("CREATE TABLE analysis_data AS SELECT * FROM read_csv(data)")
result = con.execute("SELECT AVG(column1) FROM analysis_data").fetchone()
return result
DuckDB can be used in Extract, Transform, and Load (ETL) pipelines to optimize techniques and analyze information because it efficiently handles data movement among various structures. Its in-memory skills and fast question execution make it best for remodeling and aggregating data earlier than loading it into a facts warehouse or every other device.
Example
import duckdb
def etl_process(source_csv, destination_db):
con = duckdb.connect(database=':memory:')
con.execute(f"CREATE TABLE temp_table AS SELECT * FROM read_csv('{source_csv}')")
con.execute(f"INSERT INTO destination_table SELECT * FROM temp_table")DuckDB excels at reading and processing Parquet and CSV documents, which might be commonplace formats in data engineering and facts technological know-how. This makes it a treasured device for fast loading and analyzing huge datasets saved in these codecs.
Learn More: How to Read and Write With CSV Files in Python?
Example:
import duckdb
# Reading a CSV file
con = duckdb.connect(database=':memory:')
con.execute("CREATE TABLE csv_data AS SELECT * FROM read_csv('data.csv')")
# Reading a Parquet file
con.execute("CREATE TABLE parquet_data AS SELECT * FROM read_parquet('data.parquet')")DuckDB is tremendously effective for interactive records evaluation and exploratory data analysis (EDA). Data scientists can use DuckDB inside Jupyter Notebooks or other interactive environments to fast query and visualize data, allowing quicker insights and choice-making.
Example:
import duckdb
import pandas as pd
# Connect to DuckDB and load data
on = duckdb.connect(database=':memory:')
df = pd.read_csv('data.csv')
con.register('df', df)
# Perform interactive queries
result = con.execute("SELECT * FROM df WHERE column1 > 100").fetchdf()
print(result)These use instances demonstrate DuckDB’s versatility and effective talents in numerous situations, from web and computer packages to data pipelines and interactive data analysis, making it an invaluable tool for data scientists and developers alike.
DuckDB is a game-changer for data scientists, combining the simplicity of SQLite with the electricity needed for complex analytical responsibilities. It addresses not unusual demanding situations like integration difficulties, constant preservation, complex setup, and inefficient facts coping with, offering a streamlined solution tailored for present-day records workflows.
With seamless integration into popular records technological know-how tools, in-memory processing, and complete SQL support, DuckDB excels in performance and ease of use. Its versatility in applications, ETL pipelines, and interactive data analysis make it an invaluable asset for an extensive range of eventualities.
By adopting DuckDB, data scientists can simplify workflows, and reduce database management overhead, and consciousness of deriving insights from facts. As data volumes and complexity grow, DuckDB’s combination of strength, simplicity, and versatility could be more and more vital in the data science toolkit.
A. Here are some of the disadvantages of using DuckDB:
In-Memory Processing: Limited scalability for very large datasets.
Limited Ecosystem: Fewer tools and libraries compared to established databases.
Community and Support: Smaller community and fewer resources.
Parallel Processing: Less advanced parallel execution compared to some databases.
A. Yes, DuckDB supports multiple connections. It allows multiple queries to run concurrently from different connections, which is useful for handling multiple users or tasks simultaneously.
A. DuckDB is generally faster than SQLite for analytical queries and complex data processing tasks. This is because DuckDB is designed specifically for analytical workloads and leverages modern hardware more effectively.
A. DuckDB is better than Pandas in three areas:
1. Performance: DuckDB can be faster than Pandas for certain operations, especially when dealing with large datasets and complex queries. This is due to its efficient query engine and optimization techniques that are typically more advanced than those in Pandas.
2. Scalability: DuckDB can handle larger datasets more efficiently than Pandas, which is limited by the available memory in a single machine. DuckDB’s query execution engine is optimized for handling large-scale data processing.
3. Functionality: DuckDB is powerful for SQL-based analytics. Pandas is more flexible for data manipulation and integrated with Python.
A. Here are some alternatives to DuckDB:
SQLite: SQLite lightweight, disk-based database.
SQLite: Lightweight, embedded relational database.
PostgreSQL: Robust, open-source object-relational database.
Apache Druid: Real-time analytics database.
Amazon Redshift: Cloud-based data warehouse.
Google BigQuery: Serverless data warehouse.
A. Yes, DuckDB can read SQLite database files directly.
Hello, I'm Abhishek, a Data Engineer Trainee at Analytics Vidhya. I'm passionate about data engineering and video games I have experience in Apache Hadoop, AWS, and SQL,and I keep on exploring their intricacies and optimizing data workflows :)
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