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Zookeeper – a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.
HCatalog has a REST interface and command line client that allows you to create tables or do other operations
HBase – run on top of HDFS to provide non-relational database capabilities.
Hive – use an SQL-like language called Hive QL (query language) that abstracts programming models and supports typical data warehouse interactions.
Tez – create a complex directed acyclic graph (DAG) of tasks for processing data.
Aws redshift emr msk code#
Pig – use SQL-like (Pig Latin) commands that runs on top of Hadoop to transform large data sets without having to write complex code and converts those commands into Tez jobs based on directed acyclic graphs (DAGs) or MapReduce programs.
Presto – a fast SQL query engine designed for interactive analytic queries over large datasets from multiple sources.
Phoenix – use standard SQL queries and JDBC APIs to work with an Apache HBase backing store for OLTP and operational analytics.
Flink – a streaming dataflow engine that you can use to run real-time stream processing on high-throughput data sources.
HUE – graphic user interface acts as front end application on EMR cluster to interact with other applications on EMR.
Aws redshift emr msk software#
Task node: A node with software components that only runs tasks and does not store data in HDFS.
Multi-node clusters have at least one core node.
Core node: A node with software components that run tasks and store data in the Hadoop Distributed File System (HDFS) on your cluster.
Every cluster has a master node, and it’s possible to create a single-node cluster with only the master node. The master node tracks the status of tasks and monitors the health of the cluster.
Master node: A node that manages the cluster by running software components to coordinate the distribution of data and tasks among other nodes for processing.
Each instance in the cluster is called a node. Instance store volumes are ideal for storing temporary data that is continually changing, such as buffers, caches, scratch data, and other temporary content.Ĭluster: a collection of EC2 instances. Data on instance store volumes persists only during the life of its EC2 instance. When a Hadoop cluster is created, each node is created from an EC2 instance that comes with a preconfigured block of preattached disk storage called an instance store.
Local file system: The local file system refers to a locally connected disk.
EMRFS provides the convenience of storing persistent data in S3 for use with Hadoop while also providing features like S3 server-side encryption, read-after-write consistency, and list consistency.
EMRFS: prefix with s3:// EMRFS is an implementation of the Hadoop file system used for reading and writing regular files from Amazon EMR directly to Amazon S3.
It’s best used for caching the results produced by intermediate job-flow steps. One advantage is that it’s fast a disadvantage is that its ephemeral storage which is reclaimed when the cluster ends. HDFS is used by the master and core nodes. An advantage of HDFS is data awareness between the Hadoop cluster nodes managing the clusters and the Hadoop cluster nodes managing the individual steps.
Aws redshift emr msk portable#
HDFS is a distributed, scalable, and portable file system for Hadoop.
HDFS: prefix with hdfs://(or no prefix).
This is the cheat sheet on AWS EMR and AWS Redshift. AWS Big Data Notes: AWS EMR and AWS Redshift