The Data Source #23 | Exploring Data Analytics Tools in the Python Data Ecosystem: Ft. Ibis, Polars, DataFusion, and More 👩🔬
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The TL;DR on Pandas 🐼
Pandas is at the forefront of the Python data ecosystem: It is an open-source library in Python designed for manipulating and analyzing data. The primary data structure in Pandas is the DataFrame, which resembles a table with rows and columns, similar to a spreadsheet or SQL table. Pandas excels at handling labeled and relational data, making tasks like data cleaning, exploration, and transformation easier. It integrates with other libraries in the Python data ecosystem, such as NumPy for numerical operations and Matplotlib/Seaborn for data visualization, forming a comprehensive toolkit for data analysis and modeling. Its flexibility and extensive functionality have made it a staple for data scientists and analysts working with tabular data in Python.
Yet, as datasets expand, Pandas struggles with performance and memory usage when handling large datasets that exceed available RAM. This often leads to slower processing times and potential out-of-memory errors. The library also lacks built-in support for distributed computing, making it less suitable for scaling data processing across multiple clusters without additional tools or libraries.
To tackle these challenges, a new wave of scalable and distributed data processing tools has emerged, changing the way in which organizations handle big data. These tools leverage parallel computing and distributed architectures to manage and analyze large datasets across clusters of machines.
⚖️ Scaling Data Processing with PySpark, Dask and DataFusion
PySpark is the Python API for Apache Spark. It combines Python's simplicity and Spark's distributed computing capabilities to enable data science and engineering teams to process massive datasets across computing clusters. PySpark supports Spark SQL, DataFrames, Structured Streaming, and Machine Learning (MLlib) which make it ideal for data analytics tasks and for applications requiring high performance and scalability in processing large volumes of data.
Similarly, Dask is an open-source Python library which addresses the complexities of working with large datasets that surpass the memory limits of a single machine. It integrates with widely used Python libraries such as Pandas, NumPy, and scikit-learn and leverages parallel computing to enable machine learning teams to utilize available computational resources. This empowers teams to train complex models effectively through distributed processing and on large datasets.
Like PySpark and Dask, DataFusion is designed for large-scale data processing. It is is a high-performance query engine, powered by Rust and Apache Arrow. It integrates with distributed frameworks like Dask SQL and offers SQL and DataFrame APIs for data manipulation across formats such as Parquet, CSV, JSON, and Avro. Its Python bindings simplify analytics, streaming, and database projects by providing optimized tools for task execution. DataFusion's flexibility and efficiency in handling SQL queries across diverse data sources using Apache Arrow's data format makes it ideal for processing large and complex datasets required in modern data applications.
🖼️ Next-Gen DataFrame Libraries: Polars, Daft, and Ibis
Polars is a leap forward in local and distributed data processing. It is an open-source OLAP query engine built in Rust for high performance on modern hardware. It offers a composable API for constructing data pipelines and a rapid vectorized query engine optimized for memory usage. Polars processes datasets larger than available memory using out-of-core techniques. It excels in tasks like ETL processes and real-time analytics, leveraging efficient memory management and parallelized operations for rapid manipulation of large datasets. As a result, it surpasses traditional DataFrame libraries like Pandas for large-scale data analysis and machine learning.
Daft, on the other hand, is a distributed dataframe library designed for processing large-scale multimodal data. Leveraging Rust and the Arrow format, Daft optimizes performance and resource utilization with a Pythonic interface similar to Pandas and Polars. It supports complex data types like images, manages memory efficiently, and handles large datasets across computing environments. Daft scales from individual laptops to cloud clusters using the Ray framework, which makes it easy to process on resource-constrained systems. By enabling users to work with diverse data types within a unified framework and delivering exceptional distributed processing performance, Daft enhances data analytics workflows and unlocks the full potential of multimodal data in Python.
Meanwhile, Ibis serves as a versatile bridge between Python's data manipulation capabilities and various backend systems. It provides a common DataFrame interface that interacts with databases and analytics platforms like BigQuery, Snowflake, and Spark. Users can write analysis code once and execute it on different backends without extensive rewriting. By bridging Python's abilities for handling and transforming data with diverse query engines, Ibis optimizes workflows for data engineers, analysts, and scientists. Data engineers benefit by replacing complex ETL/ELT jobs and SQL pipelines with a robust Python API, analysts use Ibis for interactive exploration and rapid data analysis, and data scientists prototype and preprocess data for machine learning workflows.
🦆 Simplified Data Management with DuckDB
Data management systems like Postgres or Spark have historically posed challenges for data scientists due to their difficulty in setting up, transferring data and integrating with Python workflows. In response, data scientists have developed user-friendly data wrangling tools such as Pandas, though it has its own limitations as discussed above.
Recently, DuckDB has emerged as a new database management system designed to provide efficient data processing while offering a user-friendly interface similar to dataframes, which is especially convenient for Python users. It supports popular dataframe libraries like Pandas, Polars, and Apache Arrow and enhances query performance compared to Pandas, particularly when handling large datasets. In addition to dataframe operations, DuckDB facilitates data exchange with databases such as Postgres, MySQL, and SQLite. It also extends functionality by supporting custom data types, functions, and SQL syntax, making it ideal for applications needing lightweight, embedded SQL capabilities for data analysis and exploration. DuckDB is particularly effective for analytical query tasks due to its columnar-vectorized query execution engine, which minimizes CPU usage compared to traditional row-based systems.
Where Are We Headed Next? 🕵️♂️
Looking ahead, the future of Python data tools will focus on deeper integrations with distributed computing paradigms. Next-gen DataFrame libraries will continue to redefine memory efficiency and processing speed while improving interoperability with various backend systems. With the rise of DuckDB, the ecosystem is evolving towards more streamlined SQL-driven data management solutions within Python applications. I anticipate further advancements in this direction, leveraging DuckDB's capabilities to improve analytical workflows and facilitate seamless integration with other data processing technologies.
If you are a data practitioner focusing on building next-gen tooling in the Python Data ecosystem, I’d love to chat!
My Twitter (@psomrah) & 📩 (priyanka@work-bench.com) are always open.
Priyanka 🌊
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I’m a Principal at Work-Bench, an early stage enterprise-focused VC firm based in NYC. Our sweet spot for investment is at the Pre-Seed & Seed stage. This correlates with building out a startup’s early go-to-market motions. In the data, dev tools & infra world, we’ve invested in companies like Cockroach Labs, Arthur, Alkymi, Streamdal and others.