Could someone please let me know what does it mean by 'Big Data implementation over Cloud'
I have been using Amazon S3 to store data and query using hive, which I read is one of the cloud implementation. I would like to know what exactly does this mean and all possible ways to implement it.
Thanks
You’ve likely heard the terms “Big Data” and “Cloud Computing” before. If you’re involved with cloud application development, you may even have experience with them. The two go hand-in-hand, with many public cloud services performing big data analytics.
With Software as a Service (SaaS) becoming increasingly popular, keeping up-to-date with cloud infrastructure best practices and the types of data that can be stored in large quantities is crucial. We’ll take a look at the differences between cloud computing and big data, the relationship between them, and why the two are a perfect match, bringing us lots of new, innovative technologies, such as artificial intelligence.
The Difference Between Big Data & Cloud Computing
Before discussing how the two go together, it’s important to form a clear distinction between “Big Data” and “Cloud Computing”. Although they are technically different terms, they’re often seen together in literature because they interact synergistically with one another.
Big Data: This simply refers to the very large sets of data that are output by a variety of programs. It can refer to any of a large variety of types of data, and the data sets are usually far too large to peruse or query on a regular computer.
Cloud Computing: This refers to the processing of anything, including Big Data Analytics, on the “cloud”. The “cloud” is just a set of high-powered servers from one of many providers. They can often view and query large data sets much more quickly than a standard computer could.
Essentially, “Big Data” refers to the large sets of data collected, while “Cloud Computing” refers to the mechanism that remotely takes this data in and performs any operations specified on that data.
The Roles & Relationship Between Big Data & Cloud Computing
Cloud Computing providers often utilize a “software as a service” model to allow customers to easily process data. Typically, a console that can take in specialized commands and parameters is available, but everything can also be done from the site’s user interface. Some products that are usually part of this package include database management systems, cloud-based virtual machines and containers, identity management systems, machine learning capabilities, and more.
In turn, Big Data is often generated by large, network-based systems. It can be in either a standard or non-standard format. If the data is in a non-standard format, artificial intelligence from the Cloud Computing provider may be used in addition to machine learning to standardize the data.
From there, the data can be harnessed through the Cloud Computing platform and utilized in a variety of ways. For example, it can be searched, edited, and used for future insights.
This cloud infrastructure allows for real-time processing of Big Data. It can take huge “blasts” of data from intensive systems and interpret it in real-time. Another common relationship between Big Data and Cloud Computing is that the power of the cloud allows Big Data analytics to occur in a fraction of the time it used to.
Big Data & Cloud Computing: A Perfect Match
As you can see, there are infinite possibilities when we combine Big Data and Cloud Computing! If we simply had Big Data alone, we would have huge data sets that have a huge amount of potential value just sitting there. Using our computers to analyze them would be either impossible or impractical due to the amount of time it would take.
However, Cloud Computing allows us to use state-of-the-art infrastructure and only pay for the time and power that we use! Cloud application development is also fueled by Big Data. Without Big Data, there would be far fewer cloud-based applications, since there wouldn’t be any real necessity for them. Remember, Big Data is often collected by cloud-based applications, as well!
In short, Cloud Computing services largely exist because of Big Data. Likewise, the only reason that we collect Big Data is because we have services that are capable of taking it in and deciphering it, often in a matter of seconds. The two are a perfect match, since neither would exist without the other!
Conclusion
Finally, it’s important to note that both Big Data and Cloud Computing play a huge role in our digital society. The two linked together allow people with great ideas but limited resources a chance at business success. They also allow established businesses to utilize data that they collect but previously had no way of analyzing.
More modern components of cloud infrastructure’s typical “Software as a Service” model such as artificial intelligence also enable businesses to get insights based on the Big Data they’ve collected. With a well-planned system, businesses can take advantage of all of this for a nominal fee, leaving competitors who refuse to use these new technologies in the dust.
Big Data: This simply refers to the very large sets of data that are output by a variety of programs. It can refer to any of a large variety of types of data, and the data sets are usually far too large to peruse or query on a regular computer.
Cloud Computing: This refers to the processing of anything, including Big Data Analytics, on the “cloud”. The “cloud” is just a set of high-powered servers from one of many providers. They can often view and query large data sets much more quickly than a standard computer could.
Essentially, “Big Data” refers to the large sets of data collected, while “Cloud Computing” refers to the mechanism that remotely takes this data in and performs any operations specified on that data.
The Roles & Relationship Between Big Data & Cloud Computing
Cloud Computing providers often utilize a “software as a service” model to allow customers to easily process data. Typically, a console that can take in specialized commands and parameters is available, but everything can also be done from the site’s user interface. Some products that are usually part of this package include database management systems, cloud-based virtual machines and containers, identity management systems, machine learning capabilities, and more.
In turn, Big Data is often generated by large, network-based systems. It can be in either a standard or non-standard format. If the data is in a non-standard format, artificial intelligence from the Cloud Computing provider may be used in addition to machine learning to standardize the data.
From there, the data can be harnessed through the Cloud Computing platform and utilized in a variety of ways. For example, it can be searched, edited, and used for future insights.
This cloud infrastructure allows for real-time processing of Big Data. It can take huge “blasts” of data from intensive systems and interpret it in real-time. Another common relationship between Big Data and Cloud Computing is that the power of the cloud allows Big Data analytics to occur in a fraction of the time it used to.
Big Data & Cloud Computing: A Perfect Match
As you can see, there are infinite possibilities when we combine Big Data and Cloud Computing! If we simply had Big Data alone, we would have huge data sets that have a huge amount of potential value just sitting there. Using our computers to analyze them would be either impossible or impractical due to the amount of time it would take.
However, Cloud Computing allows us to use state-of-the-art infrastructure and only pay for the time and power that we use! Cloud application development is also fueled by Big Data. Without Big Data, there would be far fewer cloud-based applications, since there wouldn’t be any real necessity for them. Remember, Big Data is often collected by cloud-based applications, as well!
In short, Cloud Computing services largely exist because of Big Data. Likewise, the only reason that we collect Big Data is because we have services that are capable of taking it in and deciphering it, often in a matter of seconds. The two are a perfect match, since neither would exist without the other!
Conclusion
Finally, it’s important to note that both Big Data and Cloud Computing play a huge role in our digital society. The two linked together allow people with great ideas but limited resources a chance at business success. They also allow established businesses to utilize data that they collect but previously had no way of analyzing.
More modern components of cloud infrastructure’s typical “Software as a Service” model such as artificial intelligence also enable businesses to get insights based on the Big Data they’ve collected. With a well-planned system, businesses can take advantage of all of this for a nominal fee, leaving competitors who refuse to use these new technologies in the dust.
In an increasingly data-driven business atmosphere, Enterprises are strategizing more towards deriving meaningful insights from their vast amounts of data. As per Gartner, till 2017, 75% Enterprises have already invested in technology that facilitates Data Analysis. Amongst the many cloud vendors available, Microsoft Azure and Amazon AWS are the top Cloud Platforms that Enterprises are utilizing to build their robust Big Data & Analytics solutions.
Here is a snapshot that helps better understand the salient features of Azure and AWS platforms available to build Big Data and Analytics solutions.
Big Data & Analytics relies heavily on computing power because of the vast amounts of data that needs to be analyzed. AWS provides EC2 instances for computing along with ancillary services like Elastic Beanstalk and EC2 container services. Whereas, Azure’s compute mostly comes from its Virtual Machines. Both offer scale-on-demand computing capacity, providing the infrastructure needed to run robust Big Data & Analytics solutions.
Network and Storage act as the backbone for Big Data solutions, pertaining to which, both the platforms support relational databases and are excellent with networking attributes. AWS & Azure offer scalable storage features like S3 and Azure Blob Storage respectively to handle unstructured data and are at par with each other.
The pricing factor is a key to choose a capable Cloud Analytics platform and AWS & Azure both offer competitive pricing. AWS charges an upfront fee depending on the use or offers a committed instance for up to three years. Further, clients can bid for any extra bandwidth, if available. Azure has a pay-as-you-go model with Microsoft charging its customers by the minute. For a shorter commitment, Microsoft may allow a mix of pre-paid and monthly charges.
Considering the vulnerability of data theft and leakage, AWS & Azure both provide greatest security features to safeguard hacking instances and sensitive data. With 90% of Fortune 500 companies entrusting Azure for their Big Data & Analytics security aspect, Azure is emerging victorious for Enterprise security.
Most organizations demand to access their business-critical data reports anytime and anywhere. AWS & Azure comply to this need by facilitating excellent reporting and business intelligence tools with QuickSight and Power BI respectively. Microsoft’s Power BI is ranked higher because it can extract and combine data from more than fifty sources, along with a massive visual library and data formatting features.
