How to design a Big Data warehouse on cloud ?

 

 

 

Guess what, cloud computing is one of the hottest topics around with affordable computing infrastructure designed to reduce both the lead time needed to deploy resources for new projects and up-font investment.Madrid Software Trainings provides complete practical Big Data Hadoop Training in Delhi in association with industry experts.

With cloud computing gaining momentum since inception, the Big Data analytics has developed as a powerful tool, which companies can use to manage, mine as well as monetize their big data structure for competitive advantages. This has led the adoption of Big Data Hadoop by companies.

In a hurry to take advantages of big data, there resides a myth that Haddop means replacing the data warehouse, while in reality, it was designed to supplement RBMS (Relational Database Management System).

You can design a Big Data warehouse on cloud. However the foremost question that arises is how? Keep reading to know how to design big data warehouse on cloud-

Using a DWS-as-a-Service like Amazon Redshift is one of the many options available around. Another option is to establish your own relational database based on virtual cloud instance services like IBM Softlayer, Amazon EC2, or Rackspace. Having a DWH on the cloud has certain benefits.

Scalability and Flexibility: Scaling a data warehouse at a physical location simply means buying & installing new hardware that may consume your valuable time. Whereas having it on cloud can scale up instantly, so it’s easier to upgrade the dedicated DWH services like Redshift.

Sadly, more traditional solutions like Oracle database on the cloud, do not get benefited. While it is completely simple to establish a new instance, transmitting the data there could be difficult, possibly at many premises.

Cost: Owing to virtualization along with less hardware maintenance, the cost of cloud data warehouse is much cheaper as compared to their bare-bone brothers. With a pricing model “pay-as-you-go” and greater flexibility, you have to pay for only actual instance usage rather than having the machines keep on working round-the-clock and consuming more electricity.

Operational Data Store, ETL, & Data Integration: These can enjoy the same advantages of high flexibility, scalability, and budget-friendliness on the cloud. When using Hadoop, the can also function on top of Hadoop as a Service solution like Amazon EMR.

The sole issue with Hadoop is that, it calls for specialized as well as complex know-how. Not all businesses are familiar with these skills at their disposal, or just a small number of specialists under them do.

Storage of Data: Rather than using local or virtual Hadoop instances for the storage of data, Amazon S3- one of the file storage services are available as well. Such services have better scalability, persistence and durability as compared to HDFS. Not to forget they are cost-effective too. If the data of your company is being kept on premise, tools like CloudBeam of Attunity could help you to get the data on the cloud. Regardless of that, nowadays more and more data acquired from various sources, for example, web logs or social data, is also being stored on these efficient cloud solutions.

Reporting: At present, the reporting department isn’t as efficient as its other cloud counterparts. You will probably require setting up servers on the cloud with relevant tools & connect to them through a remote desktop solution. You can however, get hosted versions of products from companies like Chariot and Tableau. BIME, GoodData and Reporting Services on Azure platform are also worth mentioning.

This way designing your own data warehouse on cloud can help you cut back costs while increasing flexibility and scalability. There is available a wide array of solutions right from establishing your own virtual instances to making most of hosted services. Data storage is highly useful on platform like this, and even reporting tools are kicking off to make their way up there on the cloud.

While you make your way through big data warehouse on cloud, below given are some important parameters that you must keep in mind-

  • Total volume of data
  • Volume of data to be loaded day-to-day
  • Scope of Analytics (e.g. mart or full-scale EDW)
  • Sensitivity of data/Regulatory as well as compliance requirements
  • Primary environment use (e.g. dev/test/production)

If you keep these factors in mind, hence there is no doubt to say you are going to have your own big data warehouse on cloud!.

 

 

 

 

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