- By Prachi Bhatnagar
Is your Company generating large amounts of data which is leading to increased costs, high capex and complex infrastructures? May be, the concept itself carries lofty business hopes for unearthing nuggets of hidden data and patterns.
Harnessing the power of Big Data means you have to add new technologies to the infrastructure. To manage growing volumes of big data, it is crucial to create a fast, efficient and simple data integration environment. Especially when 1,144 business and IT professionals involved in some stage of a big data program, are hitting a wall on one aspect of implementation-belief that present information infrastructure is sufficient.
Or in its expectations of disruptive tech trends for 2013, Gartner Research writes that the maturity of “strategic big data” will move enterprises toward multiple systems – content management, data marts and specialized file systems. Some other hassles of Big Data might just include finding profits in social media posts and public sources.
Now if the problem is clear that effectively combining and managing Big Data is not that simple maybe we could consider some of these points.
Did You Know?
These points can be included to scrutinize a cost optimizing solution for managing Big Data:
- 48 hours of video are uploaded to YouTube every minute, resulting in nearly 8 years of content every day
- There are nearly as many pieces of digital information as there are stars in the universe.
- Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
- Big Data theory is moving faster than the reality of what an enterprise is capable of from both a technology and manpower standpoint.
- 7 billion shares are traded every day.
- 10,000 credit cards transactions are made every second.
- If 48 hours of video is uploaded to YouTube every minute, imagine the amount of data that will be generated and all in different formats, such as comments, shares, downloads.
- Having Digital Information compared to stars in the universe and if we generate 2.5 quintillion bytes of data everyday, I think the reason is self explanatory for the need to manage enormous amount of data which results into Big Data. Also the same logic applies if 7 billion shares are traded or 10,000 credit card transactions are made.
- Next would be if Big Data theory is moving faster than technology and manpower, it is evident that we need to mend our ways and technology according to the data requirements.
How to Overcome these?
With AWS Cloud 3Vs of Big Data i.e., Volume, Variety & Velocity might look like a piece of cake and cherry on that cake is Amazon Kinesis with which you can collect and process hundreds of terabytes of data per hour from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time which is termed as Sharding, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
It also includes Transferring Data services such as AWS Import/Export which helps in transferring of the data which can be useful for credit cards and shares transaction as mentioned as an above example. It also constitutes services such as AWS Direct Connect for establishing a dedicated network connection from your premises to AWS and AWS Storage Gateway to secure integration between an On-Premises IT and AWS’s Storage Infrastructure.Amazon Simple Storage Service (S3), AWS DynamoDB is also contained in AWS Services for Collecting and Storing the data and Amazon Glacier which will help you in archiving storage service that provides secure and durable storage for data archiving and online backup.
Use Cases of Amazon EMR
Another Segment of Big Data includes Hadoop Implementation. Amazon Web Services provides Amazon EMR which is a managed Hadoop distribution. The Use Cases for Amazon EMR consists of:
- Data Mining
- Log file analysis
- Web indexing
- Machine learning
- Financial analysis
- Scientific simulations
- Data warehousing
- Bioinformatics research
So, if you are one of those reading the blog and are facing Big Data challenges such as Infrastructure Problems, Software Complications, Man Power Concerns, Time Consumption, Maintenance Issues and Inelastic Capacities maybe, you are on the definite direction to overcome all of it.
Also Check out our research on Big Data Chapter.
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