Big Data formats such as Parquet, ORC, and Feather are designed for efficient storage and fast access when working with large datasets. They are optimized for performance, compression, and scalability, making them ideal for data science and big data applications.
Parquet
Parquet is a columnar storage format widely used in big data ecosystems such as Apache Spark and Hadoop. It allows efficient reading of selected columns and supports strong compression.
import pandas as pd
# Read Parquet file into a DataFrame
df = pd.read_parquet("data.parquet")
ORC (Optimized Row Columnar)
ORC is a columnar format optimized for high-performance analytics and commonly used in Hadoop-based systems.
import pandas as pd
# Read ORC file into a DataFrame
df = pd.read_orc("data.orc")
Feather
Feather is a lightweight binary format designed for fast data exchange between Python and other languages like R.
import pandas as pd
# Read Feather file into a DataFrame
df = pd.read_feather("data.feather")
✅ This concludes our Data Importing Series.
👉Join @datascience_bds for more
Part of the @bigdataspecialist family ❤️