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Practical Real-time Data Processing and Analytics

You're reading from  Practical Real-time Data Processing and Analytics

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Pages 360 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Introducing Real-Time Analytics 2. Real Time Applications – The Basic Ingredients 3. Understanding and Tailing Data Streams 4. Setting up the Infrastructure for Storm 5. Configuring Apache Spark and Flink 6. Integrating Storm with a Data Source 7. From Storm to Sink 8. Storm Trident 9. Working with Spark 10. Working with Spark Operations 11. Spark Streaming 12. Working with Apache Flink 13. Case Study

Cloud – considerations for NRT and IOT


Cloud is nothing but a term used to identify a capability where computational capability is available over internet. We have all been acquainted with physical machines, servers, and data centres. The advent of the cloud has taken us to a world of virtualization where we are moving out to virtual nodes, virtualized clusters, and even virtual data centers. Now, I can have virtualization of hardware to play with and have my clusters built using VMs spawned over a few actual machines. So, it's like having software at play over physical hardware. The next step was the cloud, where we have all virtual compute capability hosted and available over the net.

The services that are part of the cloud bouquet are:

  • Infrastructure as a Service (IaaS): It's basically a cloud variant of fundamental physical computers. It actually replaces the actual machines, the servers and hardware storage and the networking by a virtualization layer operating over the net. The IaaS lets you build this entire virtual infrastructure, where it's actually software that's imitating actual hardware.
  • Platform as a Service (PaaS): Once we have sorted out the hardware virtualization part, the next obvious step is to think about the next layer that operates over the raw computer hardware. This is the one that gels the programs with components like databases, servers, file storage, and so on. Here, for instance, if a database is exposed as PaaS then the programmer can use that as a service without worrying about the lower details like storage capacity, data protection, encryption, replication, and so on. Renowned examples of PaaS are Google App Engine and Heroku.
  • Software as a Service (SaaS): This one is the topmost layer in the stack of cloud computation; it's actually the layer that provides the solution as a service. These services are charged on a per user or per month basis and this model ensures that the end users have flexibility to enroll for and use the service without any license fee or locking period. Some of the most widely known, and typical, examples are Salesforce.com, and Google Apps.

Now that we have been introduced to and acquainted with cloud, the next obvious point to understand is what this buzz is all about and why is it that the advent of the cloud is closing curtains on the era of traditional data centers. Let's understand some of the key benefits of cloud computing that have actually made this platform a hot selling cake for NRT and IOT applications

  • It's on demand: The users can provision the computing components/resources as per the need and load. There is no need to make huge investments for the next X years on infrastructure in the name of future scaling and headroom. One can provision a cluster that's adequate to meet current requirements and that can then be scaled when needed by requesting more on–demand instances. So, the guarantee I get here as a user, is that I will get an instance when I demand for the same.
  • It lets us build truly elastic applications, which means that depending upon the load and the need, my deployments can scale up and scale down. This is a huge advantage and the way the cloud does it is very cost effective too. If I have an application which sees an occasional surge in traffic on the first of every month, then, on the cloud I don't have to provision the hardware required to meet the demand of surge on the first of the month for the entire 30 days. Instead, I can provision what I need on an average day and build a mechanism to scale up my cluster to meet the surge on the first and then automatically scale back to average size on the second of the month.
  • It's pay as you go: well, this is the most interesting feature of the cloud that beats the traditional hardware provisioning system, where to set up a data centers one has to plan up front for a huge investment. Cloud data centre, don't invite any such cost and one can pay only for the instances that are running, and that too, is generally handled on an hourly basis.
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Practical Real-time Data Processing and Analytics
Published in: Sep 2017 Publisher: Packt ISBN-13: 9781787281202
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