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Based on the incoming application traffic, Elastic Load Balancing automatically distributes traffic across EC2 instances. Elastic Load Balancing detects unhealthy instances and moves traffic to healthy instances till the unhealthy instances are restored back. Elastic Load Balancing is used for 1. Better fault tolerance 2. DNS failover 3. Auto Scaling 4. Easy creation of entry point for VPC (Virtual Private Cloud) Elastic Load Balancing Features: 1. Incoming traffic can be distributed across amazon Ec2 instances in a single availability zone or multiple zones. 2. Helps creation of security groups when used in a Virtual Private Cloud (VPC) 3. Detects health of Amazon Ec2 instances. For example, when a load balanced instance is detected, Elastic Load Balancing makes sure not to route traffic to that instance. 4. Elastic load balancing supports sticking user sessions to specific EC2 instances. 5. Supports both Internet Protocol v4 and v6 (IPv4 and IPv6) 6. Supports SSL termination 7. Elastic Load balancing metrics, request count and latency, are reported by Amazon CloudWatch Cost: The cost depends on the usage of a Elastic Load Balancer. The charge is on an hourly basis and for each GB transferred through the Elastic Load Balancer.
Auto Scaling allows scaling of Amazon EC2. This feature of Amazon EC2 allows automatic allocation or removal of resources based on the actual needs at any given time. The number of instances increases during demand hikes and reduces when the demand falls. Auto Scaling comes as part of the Amazon CloudWatch service. It’s applicability is significant in applications that has usage variability on an hourly, daily or weekly basis. Features: 1. When demand increases, scales out amazon EC2 instances. 2. When demand subsides, shuts down unused Ec2 instances. 3. Dynamic Scaling based on CloudWatch metrics 4. Replace unhealthy or not in reach instances to maintain a higher availability of applications 5.
Amazon Elastic MapReduce is a web service that helps with Big Data challenges. EMR is a framework that splits the large amount of data into pieces, processes the pieces and gathers the result as a single output.
”BigData” is a term that has been buzzing around a lot for the last few years. And when you hear this buzz, you’ll hear ”Hadoop” as well. In last 2-3 years, many big players in the industry have come up with their own distribution of Apache Hadoop, be it Intel, Microsoft, IBM, or EMC, etc. Also, some startups, focusing only on Hadoop, have become big players now – Cloudera, Hortonworks – in this area. Each Hadoop distributor claims how its distribution is the best one out there. Each distribution has some unique features which really may be useful for a set of users and may not be useful for another. It may become non-trivial to choose from so many distributors matching your requirements, especially when the user is spending money on purchasing a distribution and support. Update: The free white paper comparing the Hadoop Distributions is ready for download! Click here or check the resources section on the sidebar to download the whitepaper for free. User Bases: There are multiple user bases that may need to deploy Hadoop. Some of them are listed below: 1. Higher management in some company, willing to move to BigData solutions using Hadoop. 2. A developer building some tool in Hadoop Ecosystem. 3. A newbie learning Hadoop and looking for a temporary/non-serious Hadoop deployment. Keeping these things in mind, we have completed a thorough study of following distribution sources, which will be covered in a 6-part series. 1. Intel Distribution for Apache Hadoop 2. Cloudera Distribution Including Apache Hadoop 3. Hortonworks Data Platform 4. MapR Through this series, we’ll share our experience with each of these distributors and provide subjective as well as objective results of the feature/performance comparisons we did. This will help you shortlist the distributors, based on your requirements.