Big Data in the Cloud: Predictive Analytics Driving Efficiency
We now have the ability to work on large numbers of records and very large numbers of attributes, which could increase the reliability of very different systems. The cloud is emerging as a popular option for the processing of the big data that is generated and aids in the task of storing and organizing all that much needed information.
Cloud Computing Promises
Big data and the cloud have made an important impact on business operations in almost all verticals by allowing companies to serve customers more effectively. However, storing, classifying and moving terabytes of data has been a bump in the road for some businesses. New tools offered by the larger cloud providers are easing big data migration and integration pain points.
Cloud computing allows on demand, scalable storage capacity. It eliminates large upfront IT investments, permits businesses to scale out infrastructure, while paying only for the capacity they need to use. Cloud adoption is accelerating and with it the amount of data stored in the cloud is growing exponentially.
Cloud computing is a generational shift from on premise, dedicated ‘on-metal’ hardware to cloud based utility storage, compute and communications infrastructure. Now small businesses can take advantage of this infrastructure to scale their applications instantaneously.
Cheaper Storage = More Data & Cheaper Compute/Memory = More Analytics
Ever heard of the expression ‘Storage is so cheap just store all of your data, you can always analyze it later.’?
Big data has been traditionally associated to big data infrastructure technologies, such as Hadoop, MapReduce, among others. However traditional relational and NoSQL databases are being used by SMB’s to store vast amounts of data (AWS Redshift and AWS DynamoDB are examples). Additional tools are available to retrieve input variables and their attributes, allowing companies of all sizes to analyze their data sets, train machine learning models and predict future events.
The cloud is indeed playing a big role in big data analytics. The combination of having increased amounts of data and the capacity to store and analyze it without capacity constraints allows for more data driven decisions that can end up being game changers for a company.
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