HDFS EMR

Amazon EMR (Elastic MapReduce) is a cloud-native big data platform offered by Amazon Web Services (AWS). EMR provides a managed environment for running various big data processing frameworks, including Hadoop, Apache Spark, Hive, and others. While EMR doesn’t use Hadoop’s HDFS (Hadoop Distributed File System) as its primary storage, it can work with HDFS when needed for certain data processing tasks.

Here’s how HDFS and EMR can be related:

1. Hadoop Compatibility: EMR is designed to be compatible with Hadoop, which means you can use Hadoop-based applications and processing patterns on an EMR cluster. EMR provides pre-configured Hadoop components such as HDFS, MapReduce, and YARN (Yet Another Resource Negotiator).

2. HDFS on EMR:

• When you launch an EMR cluster, you have the option to configure it with an HDFS-like distributed file system that is local to the cluster. This is called “EMRFS” (Amazon EMR File System), and it provides a similar interface to HDFS for storing and accessing data within the EMR cluster.
• EMRFS can be used to store input data and intermediate results for processing jobs within the cluster. It is often used when the data processing workflow involves Hadoop-based tools.

3. S3 Integration: While EMR provides EMRFS, it also encourages the use of Amazon S3 as the primary data storage solution. S3 is an object storage service provided by AWS that is highly scalable, durable, and cost-effective. EMR clusters can read data directly from and write data to S3, and it’s often used as a data lake for storing and sharing data across multiple EMR clusters and other AWS services.

4. Data Ingestion: Data can be ingested into EMR from various sources, including S3, HDFS, and other data storage solutions. EMR provides connectors and tools for importing data into the cluster for processing.

5. Data Processing: EMR clusters are used for processing and analyzing large datasets using distributed computing frameworks like Hadoop, Spark, and Hive. The data can be read from either EMRFS, S3, or other data sources, depending on your data architecture.

6. Data Export: After processing, results can be written back to EMRFS, S3, or other data storage systems, depending on your data workflow and requirements.