Difference between Cloud Computing and Big Data Analytics
Ever since the New York Times published an article on how Walmart utilizes big data analytics to maximize its sales, people are in a frenzy about Big Data. The retailer figured out that sales of Pop-Tarts, a popular brand of Sweets surges during Hurricanes and used this knowledge to increase their profits.
Be it, individuals who save their data for an on-the-go access or businesses who cut upfront costs whilst maintaining disaster-proof IT operations, everyone is looking towards the sky these days. Enter cloud-computing, a modern approach to computing because of which everything and everyone is on cloud nine.
Post the dot-com bubble burst, the information technology field is gaining incredible momentum. Emerging from this momentum are Cloud Computing and Big Data Analytics, the two hottest trends which have an unprecedented impact on all levels of human life. In this write-up, we will look at these trends of today’s technological ecosystem and attempt to make a comparison between Cloud Computing and Big Data Analytics.
Head-to-Head Comparison between Cloud Computing vs Big Data Analytics
Below is the Top 11 Comparison between Cloud Computing vs Big Data Analytics
Key Differences between Cloud Computing vs Big Data Analytics
Key Differences between Cloud Computing vs Big Data Analytics- Cloud computing is about providing computer resources and/or services over the network while Big Data is about tackling problems faced when the huge amount of data is involved, and traditional methods become infeasible.
- Big Data works by breaking huge data sets into manageable ‘chunks’ and distributing these chunks across the different computer systems. In Cloud computing, information is stored on physical servers which are maintained and controlled by Service Providers. The user can access these resources through the internet.
- It is possible to deploy a Big Data Solutions on the cloud through the PaaS or SaaS service. In PaaS, Hadoop platform is provided to the consumer while in SaaS various components or applications running on Hadoop are accessible. In fact, the marriage of Big Data and Cloud Computing is becoming so popular that we have a new buzz word in IT: BDaaS (Big Data as a Service).
- Big Data taps the previously ignored data of an organization and provides valuable insights which can drive its business while Cloud Computing provides flexibility and speed with respect to IT deployments which can streamline an organization’s operations.
Cloud Computing vs Big Data Analytics Comparison Table
The differences between cloud computing vs Big data analytics are explained in the points presented below
| Basis for comparison | Cloud Computing | Big Data |
| What is it? | Computing Paradigm | Extremely large data sets |
| Focus | Providing universal access to services | Solve technological problem dealing with humongous data sets |
| Best described by | Cloud computing is about providing services over a network, mostly the internet. The services can be a software, a platform or IT Infrastructure. | 3 V’s – Velocity, Volume, and Variety
To qualify your data as “Big Data”, the data set of interest should be illustrated by either or all the above V’s. |
| When to move to? | You might consider migrating to the cloud when you need rapid deployment or scaling of IT Applications or Infrastructure whilst maintaining centralized access. Maintaining IT Operations on-premise requires diverging from your business, with cloud computing your focus remains on your business. | Big data engineering comes into play when traditional methods and frameworks are ineffective when dealing with the voluminous amount of data. When we are analyzing data of petabytes, a distributed framework along with parallelized computing is required. |
| When to not move? | Conversely, in certain cases, you might not want to migrate to the cloud. If your application deals with highly sensitive data and requires strict compliance or your application does not adhere to cloud architecture, you should keep things off the cloud. Moreover, moving to the cloud is equivalent to losing control of your hardware. | Big Data solutions solve a very specific problem statement relating to huge datasets and most Big Data Solutions are not meant to deal with small data. Big Data is not a replacement for relational database systems. |
| Benefits | Low maintenance costs, Disaster-safe implementation, centralized platform, zero-upfront costs | High-scalability (Scales out forever), Cost-effective, Parallelism, Robust eco-system |
| Popularized by | The term “Cloud Computing” became prevalent when Amazon released EC2 (Elastic Compute Cloud) Product in 2006. | When Mike Cafarella and Doug Cutting released the ‘Hadoop’ project in 2005 at Yahoo, “Big Data” began becoming mainstream. |
| Common Roles | 1.Cloud Resource Administrator: The person or an organization that administers the cloud 2.Cloud Service Provider: 3.Cloud Consumer: 4.Cloud Service Broker: 5.Cloud Auditor: |
1.Big Data Developers:
They write programs to ingest, process or cleanse data. They also set up scheduling and delta capture mechanisms. 2.Big Data Administrators: 3.Big Data Analysts: 4.Data Scientist: 5.Big Data Architect: |
| Buzz Words | IaaS: Infrastructure as a Service happens when Service Providers provide the Consumer with physical resources like memory, disk, servers, and networking. The customer can utilize these services however she wishes and installs applications on top of them.
PaaS: A platform can be an Operating System, RDBMS System, Server or a Programming Environment. All these platforms are provided in the form of Platform as a Service. SaaS: In Software as a Service paradigm, the Consumer directly utilizes the application or software and doesn’t have to worry about underlying platform or infrastructure. |
Hadoop: Hadoop itself is a buzz word. It is an ecosystem of various components which carry out specific tasks and are integrated together to implement a big data solution. Doug Cutting named his project as “Hadoop” after his son’s toy elephant.
HDFS (Hadoop Distributed File System): A filesystem which provides high throughput access. It is a Java-based file system which is distributed across multiple machines. MapReduce: Framework for writing massively parallel applications that process large amounts of data stored in HDFS. On a rudimentary level, MapReduce performs two operations, Map where data is converted into Key-Value pairs and Reduce where data is aggregated. |
| Vendors/Solutions Providers | Google, Amazon, Microsoft, IBM, Dell, Apple | Cloudera, MapR, HortonWorks, Apache |
| Popular Solutions/Examples | IaaS: Google Compute Engine, Amazon Web Services, Microsoft Azure
PaaS: Windows Azure, AWS Elastic Beanstalk, Google App Engine, Apache Stratos SaaS: Google Docs, Microsoft Office 365 |
Hadoop is the most popular Big Data Solution and has been inspired by Google File System (GFS) and MapReduce papers. A Hadoop ecosystem typically as a multitude of components such as Ambari for cluster management, Sqoop for data extraction, Hive for data warehousing and Oozie for scheduling. |
Conclusion – Cloud Computing vs Big Data Analytics
Cloud Computing and Big Data Analytics have truly impacted the way organizations function and humans operate. Cloud Computing provides benefits which are applicable to all sizes of businesses and all kinds of individuals. Data is perceived as a resource and organizations are scrambling to implement Hadoop to exploit this resource. It is interesting to know that although these technologies have become mainstream, companies are still investing huge amounts in R&D. We can expect more growth of Cloud Computing and Big Data Analytics in coming years.
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