Can Duckdb Handle Tb Level Data?

Generated with sparks and insights from 42 sources

Introduction

Capability: DuckDB can handle terabyte-level data, but with some caveats.
Performance: DuckDB is known for its high performance on large datasets, often outperforming other tools like SQLite and Pandas.
Limitations: DuckDB is not designed for transactional applications or parallel write access and may struggle with datasets that exceed the main memory of your computer.
Use Cases: It is particularly effective for analytical queries and data processing tasks on large datasets.
Real-World Examples: Users have reported successfully using DuckDB for datasets ranging from 100GB to several terabytes, often in combination with other tools like Parquet.

Speed: DuckDB is significantly faster than SQLite for analytical queries.
Efficiency: It can handle complex SQL queries efficiently, often completing tasks in minutes that would take other tools much longer.
Parallelization: DuckDB parallelizes workloads based on row groups, enhancing performance on multi-core systems.
Vectorization: The database uses vectorized execution to speed up query processing.
Memory Usage: DuckDB is designed to work efficiently with the available memory, but performance can degrade if the dataset exceeds the main memory.

Transactional Applications: DuckDB is not designed for transactional applications.
Parallel Write Access: It does not support parallel write access.
Memory Constraints: Handling datasets that exceed the main memory can be challenging.
Real-Time Processing: DuckDB does not support real-time streaming data processing.
Stability: Users have reported occasional crashes and bugs, particularly with very large datasets.

Data Analysis: Ideal for data scientists and analysts for running complex queries on large datasets.
ETL Processes: Effective for Extract, Transform, Load (ETL) processes, especially when combined with tools like Parquet.
Machine Learning: Used for preparing machine learning datasets from large input files.
Data Integration: Can integrate with other data processing tools like Pandas and Polars.
Ad-Hoc Queries: Suitable for running ad-hoc analytical queries on large datasets.

API Analytics: Used to store close to 1TB of API analytics data.
ML Dataset Preparation: Successfully used for preparing machine learning datasets from about 100GB of input.
Parquet Files: Frequently used to query Parquet formatted files stored in cloud storage.
Data Processing: Employed for processing billions of rows of data in various analytical tasks.
Performance: Users have reported significant performance improvements over other tools like Spark and Pandas.

SQLite: DuckDB is faster for analytical queries but slower for inserts.
Pandas: DuckDB is generally faster and more efficient for large datasets.
Spark: DuckDB is more ergonomic and faster for single-machine setups but lacks Spark's scalability.
Clickhouse: Clickhouse is more stable and mature but DuckDB offers better integration with Python and other tools.
MongoDB: DuckDB uses SQL and is more suited for analytical queries, whereas MongoDB is a NoSQL database better suited for flexible schema requirements.

Parquet: DuckDB can efficiently query Parquet files, often used in combination with cloud storage.
Pandas: DuckDB can refer to any Pandas data frame in the namespace as a SQL object, allowing seamless integration.
Polars: DuckDB can be used alongside Polars for enhanced data processing capabilities.
DataFrames.jl: DuckDB can work with Julia's DataFrames.jl for efficient data manipulation.
Cloud Storage: DuckDB can query data stored in cloud storage services like AWS S3 using plugins.