🔬 Tidyverse Series – Post 12: Handling Big Data Efficiently with Arrow & Parquet Link to heading

🛠 Why Arrow & Parquet? Link to heading

Traditional file formats like CSV can be slow and inefficient when working with large datasets. The combination of Apache Arrow and the Parquet format provides a modern, high-performance solution for big data processing in R.

🔹 Why Use Arrow & Parquet? Link to heading

✔️ Faster than CSV for reading and writing large datasets
✔️ Columnar storage improves query performance
✔️ Multi-language interoperability (R, Python, SQL, etc.)
✔️ Works seamlessly with dplyr and database backends
✔️ Supports efficient in-memory operations with Apache Arrow

These technologies enable scalable data workflows in R without memory bottlenecks.

📚 Key Arrow & Parquet Functions Link to heading

These functions allow for faster data storage and retrieval while minimizing disk space usage.

📊 Example: Converting a Large CSV to Parquet Link to heading

Imagine we have a 10GB CSV file that takes too long to load into R. Using Parquet, we can reduce read time significantly.

➡️ Save a CSV as a Parquet file Link to heading

library(arrow)
library(readr)

df <- read_csv("large_data.csv")
write_parquet(df, "large_data.parquet")

✅ Parquet is compressed, reducing storage size and improving I/O speed.

➡️ Read the Parquet file back into R Link to heading

df <- read_parquet("large_data.parquet")

✅ Loads in seconds instead of minutes and consumes less memory.

🚀 Querying Large Datasets with arrow::open_dataset() Link to heading

Instead of loading the entire dataset into memory, we can query only the relevant rows from a large dataset using open_dataset().

➡️ Query large Parquet datasets efficiently Link to heading

library(dplyr)

# Open the dataset
big_data <- open_dataset("data_directory/")

# Query specific rows without loading the full dataset
filtered_data <- big_data %>%
  filter(category == "Cancer") %>%
  select(sample_id, gene_expression) %>%
  collect()

✅ This avoids memory overload by fetching only the necessary data.

📉 Comparison: CSV vs. Parquet Performance Link to heading

Parquet reduces load time by ~24x and file size by ~5x compared to CSV. 🚀

📌 Key Takeaways Link to heading

✅ Arrow and Parquet provide a modern, scalable alternative to CSV for handling large datasets.
✅ Columnar storage significantly boosts performance, especially for analytics workflows.
✅ Interoperability allows seamless data exchange between R, Python, SQL, and big data platforms.
✅ open_dataset() enables querying large datasets without loading them into memory.

📌 Next up: Tidyverse for Bioinformatics – Case Studies! Stay tuned! 🚀

👇 Have you used Arrow or Parquet in your workflow? Let’s discuss!

#Tidyverse #Arrow #Parquet #BigData #RStats #DataScience #Bioinformatics #OpenScience #ComputationalBiology