Big Data Architecture on AWS Cloud
Some of the business cases that exists for the need of Big Data are as follow:
Service Industries: Hospitality/service offerings
Entertainment: Connected TV platforms and content distribution companies
Retail: Ecommerce, Online retail stores
There is a need to create timely recommendations for each user, products and services. Companies need to aggregate data regarding the user interaction and the services offered by the company.
This data includes the following
· Historic behavior of the user
· Interaction among user & the products/services
· Stated Preferences
· Behavioral patterns of the user
· Provided patterns
This information is captured in the data warehouses and the information is pushed to other downstream applications for predictive analysis. Based on the underlying analytics algorithms/process, the offers, recommendations are provided to users.
The recommendations are basically identified & produced using machine learning, predictive analytics and or with collaborative filtering algorithm. When the number of such users, products, services and interactions are huge, processing these predictive analytics and recommendations are processed using distributive framework like Hadoop.
Hadoop ecosystems are highly scalable, fast, flexible and Resilient to failure
Data warehousing solutions along with big data analytics infrastructure built for the Hadoop offers powerful capabilities for enhancing user interactions, recommendations and offers for customers in real-time. However, the operating costs and business uses cases are always out of reach. It incurs operating costs for hiring big data experts.
Building a brand-new data warehouse is always expensive which requires special skill sets and longer commissioning times to get the warehouse scalable & running.
Setting up new big data analytics can be similarly very expensive in terms of procuring licenses of special software and recruiting analytics experts.
Usage of Hadoop do involve a lot of operating cost coupled with a team of data scientists who are familiar with various frameworks of Hadoop and use them effectively.
Below diagram shows the challenges for using environment
Decreasing the cost of big data deployments, a cost-effective solution syndicates, the rich library of data integration products provided by Prime vendor like Oracle and Integration Cloud with on-demand, priced data warehouse and big data services from Amazon Web Services (AWS). AWS’ infrastructure can be deployed in hours whenever it is needed to perform a data load or workload. When required set of tasks are complete, the set up can be released, which “stops the clock” on billing/payment for that infrastructure. You pay only for what you use and bring your own license on the cloud (any data transformation/integration tool). This means the price is only for your actual consumption. Whereas in traditional systems you pay for everything which you use and you don’t.
Getting into cloud substantiate all your operating costs. AWS provides a wide range of capabilities that make this approach feasible
Amazon Simple Storage Service (S3) Amazon S3 is object storage built to store and retrieve any amount of data from anywhere Data in S3 can persevere self-sufficiently from Redshift or Elastic MapReduce resources
Amazon Redshift is a cloud-based data warehouse environment that can be incorporated on demand.
Redshift Copy from S3 — Redshift delivers for extremely high performant parallel loads of data from S3 by allocating data loading work across all nodes in the Redshift
Transient Elastic MapReduce (EMR) Clusters — In a traditional Hadoop, the cluster assists in couple of roles:
1. To process work,
2. To distribute Input /Output by using local storage within local HDFS
Amazon Elastic Map reduce has its own drivers which efficiently does read & write operation from S3, removing the need of data storage in HDFS.
Amazon drivers that enable every node in the EMR cluster and does read and write operations directly from S3, removing the need to stage data in HDFS. When HDFS is not required, cluster nodes only exist during the time for the specific work is being processed. This is called a Transient Cluster: one that which is active only for the operation which you perform. i.e. You pay for the cluster only while it is running.
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