Big Data Analytics Services

Cloud service integration and DevOps transformation are crucial for ensuring survival and competitiveness for any modern business. However, these tools are available to anyone and simply put your company on a level playing field with any other public cloud customer. To become competitive, you must process your data more efficiently, and Big Data business analytics is the way to go. IT Svit has 5+ years of experience in delivering Big Data analytics as a service and we can help a company of any size with the task of any scope. We have ready Big Data analytics solutions that can be quickly adapted to your project and trained to meet your goals!

Every cloud platform provides some kind of Big Data analytics solutions for business. These tools enable an in-depth analysis of your Big Data and are highly configurable. They have detailed documentation and you can use assistance from the cloud provider’s support engineers to configure them for your case. Their downside is that they use too many vendor-specific resources, while there are open-source alternatives you can configure more precisely and without vendor lock-in. IT Svit helps your company design, build and configure Big Data and business analytics that work equally well with any cloud platform and deliver actionable business insights based on your company’s data.

Some companies understand the need for Big Data analytics but just can’t select the most appropriate application for their Machine Learning algorithms and Big Data analysis results. In such a case, IT Svit can provide Big Data analytics as a service to help your business gain a competitive advantage through using Artificial Intelligence algorithms to improve your IT infrastructure management and end-user experience thanks to predictive analytics.

Big Data is a buzzword that you have surely heard a hundred times already. Big Data analytics solutions can help medics identify diseases in their early stages, help marketers better personalize their ads campaigns, are used for voice, text and visual recognition tools, and in a wide variety of other applications. However, if your product, application or service does not use Big Data innately, adding it, later on, is like bolting some device sideways to a working engine.

This is why while many companies yearn to implement Big Data in their workflows, they are challenged by the lack of skill, technology and a basic understanding of what Big Data analytics is, how it works, what it does and how can it be of use in achieving their business goals. This is why they often seek the required expertise elsewhere and get in touch with various Big Data analytics companies, who promise to implement Big Data analysis to “analyze the entirety of your data, detect previously unseen patterns and discover a goldmine of actionable business insights”.

Real-life Big Data analytics works quite differently.

The main mistake many businesses make is trying to analyze all the data at their disposal to achieve results they do not even yet know about. Actually, Big Data analysis is a very powerful tool for solving some business challenges and achieving quite a distinct result. One of the first commercial-scale examples of Big Data usage was the famous Amazon suggestion — “users who bought this also bought…”, which lead to huge increase in consecutive sales and greatly increased customer engagement, as the customers were able to identify the trends quickly and make all the purchases at once, instead of having to shop several times.

This was possible due to the Machine Learning algorithm processing huge volumes of anonymized data about the customers and finding the patterns in it. However, this algorithm was not trained to look at anything at random. It had been aimed at following the purchase routines and building the patterns. Simply put — when someone bought a smartphone and a set of earbuds, the system marked these purchases as related. If this pair of purchases repeated enough times, the Artificial Intelligence algorithms pinned it down as a pattern and began offering all new customers earbuds as the next purchase after ordering a smartphone.

The same worked for warm hats and mittens, raincoats and rubber boots, book series from the same author, etc. This is an example of a Big Data analytics used to support business goals. IT Svit has built more than 50 such systems, helping our customers solve various tasks:

• use Optical Character Recognition to validate IDs during customer registration at a dating service
• speed up diagnosing of cattle illnesses based on symptoms to speed up treatment and minimize mortality rates
• deduplicating news from various sources to build bespoke news feeds for niche websites
• using Optical Character Recognition to parse CV’s from a variety of sources and automatically create user profiles in a Human Resource Management system, etc.

Thus said, our thorough understanding of the data science and best practices in business intelligence, as well as ample firsthand experience with training Machine Learning models. We can select, train and deploy the most suitable Artificial Intelligence algorithm, which will be able to achieve the specified business goals for your company.

Big Data analysis can be used the other way around — to improve the performance of your cloud infrastructure. In this case, the input information is the system logs and other machine-generated data. The Machine Learning model tracks the selected parameters in this data, like CPU load, RAM usage, disk usage, I/O, etc — and forms the “normal operations” pattern. Once the model is trained, it can be used to provide predictive analytics.

Let’s assume you have a lazy day at your product and the number of visitors is low. Your current system CPU load is around 40% of maximum capacity. The Artificial Intelligence algorithm is dormant. However, at 6 p.m. people are back home from work and begin to use your platform actively. The number of sessions grows rapidly and in 5 minutes CPU load is 70% already.

If 70% CPU workload is configured to be a threshold, reaching it triggers a response scenario — additional platform instances are launched to deal with the load. Once the rush hour is over and visitors begin to log-off, CPU load falls to another threshold, which triggers the next response scenario and the excessive instances are shut down to save your budget.

You might say that it is possible to employ this approach using scheduled macro commands to spin up additional computing resources at 6 p.m. and shut them down at midnight, for example. However, you would have to write a huge codebase of such macro commands to manage a single platform — and it will have to be updated constantly to remain relevant.

But what if for some reason the visitor numbers won’t grow one day? The scenario will still execute and you will lose money. And if the workload will unpredictably grow at night and your system won’t be able to deal with it? Besides, macro commands can deal only with scheduled and authorized tasks — and malicious intent (like a DDoS attack) can throw your cloud infrastructure off balance easily.

Quite the opposite, a well-trained Machine Learning model can track dozens of parameters at once, do it 24/7 and respond within milliseconds if the normal pattern of operations is broken. Besides, the AI algorithm always keeps on learning, so it can detect the anomalies that were absent in the initial training set.

This way, using ML and AI algorithms to enable predictive analytics helps build a self-healing cloud infrastructure with literally no downtime, protected from force-majoure situations and secured against malicious intent. Thus said, the cost of training and running such a Machine Learning-empowered system is much lower, as compared to losses from a major breakdown.

IT Svit has ample experience with providing Big Data analytics solutions at scale for companies of all scopes. We implement Big Data analytics for any stage of your software delivery workflow and any aspect of your cloud infrastructure management operations. We will help you achieve your business goals and gain a competitive edge thanks to Big Data analytics!

It is only natural to want to utilize Big Data and Business Intelligence tools from your cloud service provider, as they integrate with other web services from AWS, GCP or MS Azure best. However, despite being developed by industry-leading data science professionals and providing extensive customization capabilities, these instruments can require quite an intense configuration before they can be applied to your project and work flawlessly there.

IT Svit has vast expertise working with Machine Learning tools from AWS and Google Cloud Platform, as well as building bespoke Big Data analysis solutions with these cloud service providers. We know how to adapt standard ML/AI configurations to meet the requirements of any project and we can replace most of the vendor-specific components with open-source tools and Python/R/JS scripts to minimize expenses on your Big Data analysis while keeping it resilient and reliable.

Big Data analytics is complicated, and running it in-house can be a very complex task, primarily due to the lack of experienced data scientists on the job market. This is why many companies opt for using Big Data analytics as a service and partnering with various third-party IT service providers. The key aspect of this partnership is that instead of working with pure Big Data analytics companies it is best to work with Managed Services Providers, like IT Svit. Why so?

Managed Service Providers are companies who possess both thorough expertise with data science and ample hands-on experience in building and running the cloud infrastructure required to make Big Data analytics solutions work. It is also important, that MSPs can cover both the Big Data and the cloud workflows parts of the system, so IT Svit provides an end-to-end solution that helps you reach the business objectives and project goals.

Should you need help Big Data analytics services from IT Svit — contact us, we are always glad to assist!