How can I learn Hadoop for free
Apache ™ Hadoop® is an open source software project for the efficient processing of large data sets. Instead of using a single computer to process and store the data, you can use Hadoop to combine standard hardware into clusters in order to analyze large data sets in parallel.
The Hadoop ecosystem has a ton of applications and execution engines that provide the tools to help you do your analytics jobs effectively. With Amazon EMR, you can easily create fully configured, elastic clusters on Amazon EC2 instances that you can run Hadoop or other applications in the Hadoop ecosystem.
Hadoop is typically the actual Apache Hadoop project, which includes MapReduce (framework for execution), YARN (resource manager), and HDFS (distribution storage). Amazon EMR also includes EMRFS, an interface that allows Hadoop to use Amazon S3 as a tier of storage.
The Hadoop ecosystem also includes other applications and frameworks, including tools for activating low-latency queries, GUIs for interactive queries, a variety of interfaces such as SQL and distributed NoSQL databases. The Hadoop ecosystem contains many open source tools that can be used to develop additional functions for the Hadoop core components. With Amazon EMR, you can easily install and configure tools like Hive, Pig, Hue, Ganglia, Oozie, and HBase on your cluster. In addition to Hadoop on Amazon EMR, you can also run other frameworks, such as Apache Spark for in-memory processing or Presto for interactive SQL.
Amazon EMR programmatically installs and configures applications in the Hadoop project on the nodes of your cluster, including Hadoop MapReduce, YARN, and HDFS. You can also have other applications such as Hive and Pig installed.
Hadoop MapReduce, an execution engine in Hadoop, handles workloads through the MapReduce framework. This divides jobs into smaller work packages that can be split across the nodes in your Amazon EMR cluster. The Hadoop MapReduce engine was specially developed to not be susceptible to computer failures on a cluster. If a server that is currently running a task goes down, Hadoop will redirect that task to another machine until it is completed.
You can program MapReduce programs in Java, Hive and Pig (if you have these applications installed on your Amazon EMR cluster) or run your own scripts in parallel with Hadoop Streaming to perform higher-level abstractions via MapReduce. Or you can use other tools that interact with Hadoop to do this.
As of Hadoop 2, resource management is taken over by YARN (Yet Another Resource Negotiator). YARN keeps track of all the resources in your cluster and ensures that these resources are dynamically distributed to get the work done in your processing job. YARN can also manage Hadoop MapReduce workloads as well as other distributed frameworks such as Apache Spark, Apache Tez, and more.
Use EMR File System (EMRFS) on your Amazon EMR cluster to use Amazon S3 as the data plane for Hadoop. Amazon S3 is highly scalable, inexpensive, and built to last. It is therefore ideally suited as a data storage device for big data processing. If you store your data on Amazon S3, you can separate the computer tier from the storage tier and adjust the CPU and memory of your Amazon EMR cluster according to the workloads, thus eliminating the need for additional nodes in the cluster to maximize the storage capabilities on the cluster. You can also turn off the Amazon EMR cluster when it is idle to save costs while the data is safe on Amazon S3.
EMRFS is optimized so that Hadoop can read and write data directly and with high performance to Amazon S3 and process objects that have been protected with the server and client-side encryption of Amazon S3. With EMRFS, you can use Amazon S3 as a data lake, while Hadoop in Amazon EMR can act as an elastic query plane.
Hadoop also includes a distributed storage system - Hadoop Distributed File System (HDFS) - in which data is stored in large chunks on multiple hard drives on the cluster. HDFS has a configurable replication factor (3x by default) for increased availability and longevity. HDFS monitors replication and distributes data evenly across all nodes, even if nodes fail or new ones are added.
HDFS is automatically installed with Hadoop on your Amazon EMR cluster. You can use HDFS in conjunction with Amazon S3 to store your input and output data. Amazon EMR also configures Hadoop to use HDFS and local disks to store intermediate data created during the Hadoop MapReduce jobs, even if your input data is on Amazon S3.
You can quickly and dynamically initialize a new Hadoop cluster or add servers to your existing Amazon EMR cluster. This significantly reduces the time required to make resources available to users and data scientists. Use Hadoop on the AWS platform to dramatically increase operational agility and reduce both the cost and time it takes to allocate resources for experimentation and development.
Hadoop configuration, networks, server installations, ongoing administrative maintenance - all of these can be complicated and demanding. As a managed service, Amazon EMR takes care of your Hadoop infrastructure needs so you can focus on your core business.
Many Hadoop jobs do not have to be processed all the time. For example, an ETL job might run hourly, daily, or monthly, while model jobs for financial firms or genetic sequencing might run a few times a year. With Hadoop on Amazon EMR, you can start up clusters for these workloads at any time, save the results, and then shut down the Hadoop clusters when you no longer need them. In this way you avoid unnecessary infrastructure costs.
Capacity planning can often lead to expensive wasted or limited resources if you are not using a Hadoop environment. With Amazon EMR, you can access as much or as little capacity as you need and adjust your needs as you wish in a matter of minutes.
Hadoop is widely used to handle big data workloads because of its excellent scalability. Simply add more servers with the appropriate CPU and memory values to increase the processing power of your Hadoop cluster and meet your operational needs.
Hadoop offers a high degree of longevity and availability while still being able to process analytical workloads in parallel. This combination of availability, longevity, and scalability makes Hadoop the perfect solution for big data workloads. With Amazon EMR, you can create and configure a cluster of Amazon EC2 instances with Hadoop in a matter of minutes and make use of your data.
Apache and Hadoop are trademarks of the Apache Software Foundation.
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