=================================================================================
In the Apache ecosystem, there are several libraries and tools that can read data from CSV files into DataFrame structures. Here are the most common ones: - Apache Spark:
- Library: PySpark
- Function: spark.read.csv()
- Description: Apache Spark is a unified analytics engine for large-scale data processing. PySpark is the Python API for Spark. You can use the spark.read.csv() method to read CSV files and load them into a Spark DataFrame. This is suitable for big data scenarios and distributed computing.
- Apache Arrow:
- Library: PyArrow
- Function: pyarrow.csv.read_csv()
- Description: Apache Arrow is a cross-language development platform for in-memory data. PyArrow is the Python library for Apache Arrow. The pyarrow.csv.read_csv() function allows you to read CSV files into Arrow tables, which can be easily converted to Pandas DataFrames. This is particularly useful for efficient data interchange and processing.
- Apache Flink:
- Library: PyFlink
- Function: TableEnvironment.from_csv()
- Description: Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. PyFlink is the Python API for Apache Flink, and it includes capabilities for working with tables and SQL. The TableEnvironment.from_csv() method can be used to create a table from a CSV file, which acts similarly to a DataFrame.
Each of these tools has its specific use cases and strengths, depending on the scale of the data and the requirements of the processing tasks.
Such process of reading data from a source like a CSV file, optionally transforming it, and then loading it into a DataFrame or similar data structure can be considered a part of the "Extract, Transform, Load" (ETL) process.
===========================================
User-defined schema (UDS) for a CSV file in Apache Spark using PySpark. Code:
Input:
Output:
===========================================
|
