In Hadoop, “HDFS Data” refers to the data stored in the Hadoop Distributed File System (HDFS). HDFS is a distributed and highly fault-tolerant file system designed to store and manage vast amounts of data across a cluster of machines. It is a fundamental component of the Hadoop ecosystem and is used for storing both input and output data for various data processing tasks.

Here are some key characteristics and concepts related to HDFS data:

1. Data Blocks: HDFS breaks down large files into smaller, fixed-size data blocks (typically 128 MB or 256 MB in size). These data blocks are distributed across multiple DataNodes in the Hadoop cluster.

2. Replication: HDFS uses data replication for fault tolerance. Each data block is replicated multiple times (usually three) across different DataNodes. If one copy becomes unavailable due to node failure, another copy can be used.

3. Rack Awareness: HDFS is rack-aware, meaning it takes into account the physical network topology of the cluster. It aims to store replicas of data blocks on different racks to reduce the risk of data loss in case of rack or network failures.

4. High Throughput: HDFS is optimized for high-throughput data access rather than low-latency access. It is well-suited for batch processing workloads that involve reading and writing large datasets.

5. Write-Once, Read-Many: HDFS follows a write-once, read-many model. Once data is written to HDFS, it is typically not modified; instead, new data is written as a new version. This simplifies data consistency.

6. Data Locality: Hadoop’s data processing frameworks, such as MapReduce and Apache Spark, are designed to take advantage of data locality. They preferentially schedule tasks to run on nodes where the data they need is stored, reducing network overhead.

7. HDFS Commands: Hadoop provides a set of command-line utilities (e.g., hadoop fs) that allow users to interact with HDFS. These commands enable users to copy data to and from HDFS, list files, create directories, and more.

8. Data Compression: HDFS supports data compression to reduce storage space and improve data transfer efficiency. Common compression formats like Gzip and Snappy can be used with HDFS.

9. Data Security: Hadoop provides mechanisms for securing HDFS data, including access control lists (ACLs) and Kerberos authentication.

10. Ecosystem Integration: HDFS is used as the primary storage layer for various Hadoop ecosystem components, including Hive, Pig, HBase, and more.