Hadoop is a powerful open-source framework for storing and processing large datasets in a distributed computing environment. At the core of Hadoop is the Hadoop Distributed File System (HDFS), which is designed to handle a wide range of data formats, including structured, semi-structured, and unstructured data.
Hadoop supports various data formats, each with its own characteristics and use cases. Some of the most common data formats in Hadoop include:
- Text Files: This is the simplest and most widely used data format in Hadoop. Text files can be in plain text, CSV, or other delimited formats, and are easy to read and process.
## Example of a CSV file
name,age,gender
John,25,male
Jane,30,female
- Sequence Files: Sequence files are binary files that store key-value pairs, making them efficient for storing and processing large volumes of data.
SequenceFile.Writer writer = new SequenceFile.Writer(
fs, conf, new Path("output/sequence.txt"),
Text.class, IntWritable.class);
writer.append(new Text("John"), new IntWritable(25));
writer.append(new Text("Jane"), new IntWritable(30));
writer.close();
- Avro Files: Avro is a data serialization system that provides a compact, efficient, and self-describing format for storing and processing data in Hadoop.
{
"name": "John",
"age": 25,
"gender": "male"
}
- Parquet Files: Parquet is a columnar storage format that is optimized for efficient storage and processing of large datasets in Hadoop.
name:John,age:25,gender:male
name:Jane,age:30,gender:female
Understanding the various data formats supported by Hadoop is crucial for effectively managing and processing data in a Hadoop ecosystem.