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July 27, 2023 - 7 minutes

Unlocking the Power of Big Data Analytics on the Cloud

Big data analytics is making its mark on the world.

Ironhack - Changing The Future of Tech Education

DevOps & Cloud

As soon as we wake up in the morning, we use our five senses to take in all the information we can from our surroundings to interact with the world and make the most informed decisions. In the tech world, companies rely on data to guide and point them in the right direction.

Data collection and analysis has become an essential part of a company’s arsenal and in the past few years, cloud computing has risen to power as an essential tool for not only companies, but also society as a whole. What are these tools and how do they provide companies with further benefits as they interact with one another? Let’s dive in.

What is Big Data Analytics?

There are two parts to focus on: big data and analytics. Analytics entails the evaluation and analysis of data from which companies can make insights, discover trends, and make decisions.

Big data may seem just as obvious as analytics and in a way it is: it’s just a massive amount of data. But you may ask yourself what makes it big? Big data is about the five Vs:

  • Volume: this is where the word big comes into play because volume refers to the vast amount of data with which these companies are working.

  • Velocity: the speed that data collection and analysis takes is an important part of the process although it normally is expected to be near instantaneous with some big data analytics.

  • Variety: data can be found in three different formats: structured (relational databases), semi-structured (XML, binary executables, HTML, EDI), and unstructured (images, business documents, emails, and websites).  

  • Value: when we are thinking about the value, we are answering the following question: what is the goal of collecting and analyzing all the data? The objective is to extract worthwhile insights, patterns, and trends, which provides the data with that value.

  • Veracity: since the data is coming from a variety of sources, the reliability and accuracy of the data is extremely important which is why analysts have to ensure its quality before adding it to the list of data.

When data analysts keep these five Vs in mind throughout the process, they are able to provide the best possible results from the most accurate data available. Big data analytics is leveraged in so many ways to help companies make the right choices and one of the most common ways in which we interact with it is through social networking.

Big Data Analytics Examples

If you have used social networking applications or websites, then you’ve definitely interacted with big data analytics in some shape or form. In recent years, the use of ads on these platforms has skyrocketed and because of this, companies have wanted customers to have the best possible experience when using their services. 

When a user is on a social network, there are several big data analytics functions happening at once. All of the actions you make on their pages like watching videos, clicking on or writing comments, liking videos or photos, inputting hashtags and much more can become influential data that is collected and analyzed to optimize user experience. Some of these include the following:

  • Personalization: the data that social networks harvests go into databases that show a footprint of what would improve the user experience. The more personally intune to what the user likes, the more likely the user will return and interact more with the social network.

  • Targeted advertisements: by knowing what a user likes, dislikes, and even what they may need, social networks can provide the right advertisement at the right time. If a user starts liking and consuming more content related to a hobby like boxing, wouldn’t it be the perfect time for them to buy products related to boxing? And all of a sudden, boxing gloves, wraps, and boxing gyms show up as advertisements between friends’ posts. 

  • Suggested content: one like has a lot of power and can set off a chain reaction of content that a social network can then present to you. Recently, social networks have taken the data of a user to show them videos, photos, and posts that reflect back what they have already been searching for and connecting with on the network.

To keep clients and customers on their applications and pages, social networks make a world that provides the users with hit after hit of serotonin and the best way to do that is use all of the data they collect, analyze it, and provide a personalized user experience

Benefits of Big Data Analytics

As seen with social networking, big data analytics has many advantages that make it a powerful tool for companies to make clients come back again and again to their product. Some of these advantages of big data are:

  • Personalized user experiences: as mentioned above, massive amounts of data go into data analytics to make the user experience as personalized as possible so that people feel the lure of the product; after all, humans like the familiar and comfortable. When new life events happen, data can provide fresh content that provides further opportunities for people to interact with more of the same thing.

  • Improved productivity: one of the ways in which companies leverage data is to show inefficiencies and areas of improvement within their business. 

  • More accurate decision-making: data holds the key to unlocking the insights, trends, and patterns that will help companies make the most beneficial decisions for their future.

  • More creative innovations: by providing companies with data on what does and doesn’t give them the attention they want, employees can use that feedback to create innovative and creative products and solutions to problems that they may have never thought of without it. 

Big Data Analytics Tools

To run data analytics, data analysts rely on software to evaluate datasets and generate insights and trends and it is used to manage volume, velocity, and variety. Here are some of the tools that data analysts most often use:

  • Apache Spark

  • Tableau

  • Apache Flink

  • MongoDB

  • Apache Storm

  • Atlas.ti.

  • HPCC

These big data analytic tools have their own advantages that will help in different situations and data analysts rely on other types of technologies such as the cloud to complete their analyses. 

What is the Cloud?

If you have a device that has access to the internet, it’s very likely that you have some sort of cloud service. Devices have a limited amount of physical storage space and to complement that physical storage, users can pay for more space that relies on access to the internet. There are physical servers elsewhere that house the data and the internet is the bridge that connects the device to the physical server. 

Some services that provide cloud storage are:

  • Amazon Web Services

  • Google Cloud Platform

  • Microsoft Azure

  • IBM Cloud

  • Databricks

  • Snowflake

  • Qubole

Whichever cloud or data storage service that a company pays for will be instrumental for them in improving and maximizing their big data analytics process. 

What’s the Role of the Cloud in Big Data Analytics?

The cloud plays a vital role in analyzing big data, since it provides a large amount of storage space and data analysts accumulate and store a massive amount of data for all of the insights and trends they generate and discover. There are a variety of benefits when depending on cloud storage for big data analytics like:

  • Cost efficiency: although companies must pay for cloud services, they are actually saving money in doing so since purchasing, setting up, and maintaining cloud infrastructure on-site can end up much more expensive. Housing physical servers also occupies a large amount of space that could be more efficiently used. 

  • Accessibility: using the cloud for data storage permits a user to access it as long as they have an internet connection; with the internet, you can be at home, in another country, or even on a plane and retrieve any data you need

  • Scalability: since data analysts are working with datasets that are constantly fluctuating in volume, the cloud provides a flexible amount of storage space that can always be reduced or increased easily. 

  • Real-time analytics: in addition to storage space, there are managed streaming services that complete instantaneous real-time data analysis. The services you can find include Amazon Kinesis, Azure Stream Analysis, and Google Cloud Dataflow.

With the amount of data that devices, applications, and websites are compiling, storing and analyzing, companies are able to discover many insights and trends that will assist them in making the best decisions thanks to the help of the cloud. If you’re looking to work with data and dive deeper into the world of analytics, look no further than Ironhack’s Data Analytics course.

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