Cloud services for Big Data analytics from IT Svit

Big Data analytics can be very profitable for your business if implemented right. However, implementing it right requires a lot of effort and understanding of the reasons, workflows and technology. Thus said, a business needs a trustworthy and experienced contractor to deliver cloud services for Big Data analytics — and IT Svit is such a partner exactly.

Data analytics services for any business

All business systems generate a steady influx of data, but in order to be useful, it must be analyzed and visualized. Configuring an efficient data analytics solution is a complex task, and IT Svit can help your business with it. We have ample experience with building, configuring and running efficient cloud-based data analytics solutions at scale.

Cloud computing data analysis as a service

Many cloud vendors provide tools for data analysis that can be integrated with your systems. However, the precise configuration of these instruments with third-party systems requires a thorough understanding of cloud computing workflows and architecture, which is hard to obtain by reading a knowledgebase. IT Svit helps businesses configure and manage cloud computing data analysis tools to ensure maximum ROI and cost-efficiency of operations.

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According to the Gartner hype cycle, every idea has its point of technology trigger, the rapid growth of hype due to inflated expectations, then the rapid descend to through of disillusionment, slow climb up the slope of enlightenment, and gradual reach of the plateau of productivity. The same happened to the Big Data concept, which was introduced by O’Reilly back in 2005. It became a buzz after the cloud platforms like Amazon Web Services and Google Cloud Platform matured their technology and became able to support cloud computing Big Data analysis at scale.

However, early attempts to make use of Big Data by pouring all of your data streams into flat data lakes and applying Business Intelligence (BI) platforms to fish some secret patterns out of the mud — such attempts were based on wrong assumptions of what is the goal of Big Data analytics, and what results it can achieve. Naturally, this has lead to massive disillusionment and frustration among the early adopters — but it also forced the business to reevaluate the reasons behind Big Data, the goals they want to reach, as well as the tools they use to achieve them.

As it turned out, Artificial Intelligence algorithms and Machine Learning models are best applied to automating routine infrastructure management tasks, or to finding similarities in huge arrays of visual, audial or textual content. ML and AI algorithms can also be used for high-cost niche projects like developing self-managed Atlas robots for Boston Dynamics, or self-driven cars from Tesla or Mercedes-Benz. However, the most widespread area of application for Big Data analytics is processing the anonymous customer-submitted data to increase service personalization and performance or enabling self-healing cloud infrastructure through predictive and prescriptive analytics and monitoring of business production environments.

Big Data analytics services for your business

The most complex part of building solutions for Big Data analytics is the fact that there are no ready solutions for various business cases. This means that while the approaches and technology stack can be pretty similar for several cases, each end-to-end Big Data solution is unique. Moreover, implementing a bespoke Big Data analytics system requires an in-depth understanding of Big Data science, cloud architecture and DevOps workflows.

Gaining such expertise in-house quickly is quite expensive and effort-consuming, as you’ll have to find experienced Big Data architects and DevOps system solution engineers and form a cohesive team of them. This will demand quite large investments in recruitment and HR, in addition to paying for office space and appliances — all the while the team will not be able to begin working until all the required talents are fine.

Quite the opposite, hiring a dedicated team from a Managed Services Provider like IT Svit ensures you get a working team with established processes and a thorough understanding of the best practices of Big Data processing. Experience gained through 5+ years of working on DevOps and Big Data analysis projects helps us quickly define the most appropriate technology stack and the most fitting Machine Learning model for any particular project. In addition, lots of cloud-based data analytics solutions have similar architecture or use repetitive workflows — and we have done these operations before and have ready solutions at hand. This way, we don’t have to reinvent the wheel every time and help shorten the time-to-market for your projects.

Through the course of our 50+ successful Big Data projects, our data architects gained ample experience with the following Machine Learning models and Artificial Intelligence tools:

  • Cassandra DB
  • MongoDB
  • Redis DB
  • Python & Django
  • JuPyteR Notebook
  • R language
  • Neo4j
  • Apache Spark
  • Apache Kafka
  • Apache Hadoop
  • MapReduce
  • Naive Bayes models
  • Support Vector Machines
  • Deep Neural Networks
  • Convolutional Neural Networks
  • Optical Character Recognition
  • Decision Trees and Forests
  • etc.

We have ample experience with building cloud solutions for Big Data analysis on Amazon Web Services and Google Cloud Platform, using DevOps tools like Terraform and Kubernetes, Docker containers and Ansible playbooks, Jenkins workers and ELK stack for monitoring, Prometheus and Grafana for visualization. Thus said, we can design, deploy and configure the required infrastructure, and select, train and maintain the most fitting Machine Learning model to reach your business goals.

Cloud-based Big Data analytics as a service

All three major cloud vendors (namely Amazon Web Services, Google Cloud Platform and Microsoft Azure) offer Machine Learning and Artificial Intelligence features for building cloud-based Big Data Analytics solutions. The challenge there lies in the fact that these systems are quite complex to configure and if their setup is done according to cloud vendor’s manuals, they end up costing quite a lot due to using a multitude of platform-specific components. Thus said, a business might end up in vendor lock-in, overpaying for the components that have open-source counterparts. This is where IT Svit can lend a helping hand.

Our DevOps engineers have an in-depth understanding of AWS and GCP features, and our data science specialists know how to design and implement resilient and scalable systems for data analysis and training Machine Learning models. Due to this, we can quickly build and configure the required cloud infrastructure and workflows to ensure maximum cost-efficiency of the training and performance of your Artificial Intelligence algorithms.

The main benefit here is that with our help you get a cloud-agnostic solution that can work equally well on any cloud platform and where cloud-specific components are mostly replaced with open-source alternatives, so you pay-per-use for the mission-critical components without overspending on the rest of the infrastructure and workflows.

Additional benefits of Big Data analytics

Cost-efficiency of running a Big Data analytics is essential, but it ultimately results in great cost-efficiency of running the entire business in general, and cloud infrastructure, in particular. For example, using Machine Learning models to run prescriptive Big Data analytics helps automate the cloud system monitoring and leads to self-healing infrastructure, where all production environment components run in separate Docker containers and can be scaled up and down, rebooted or upgraded independently of each other. This guarantees increased operational resilience and predictable workflows for your IT operations, ultimately allowing your company to reach the business goals with less risk and faster.

Another huge field of application for Big Data analysis is the personalization of user-centered services and processing the information on product usage to support ongoing improvements. Your customers leave a trail of actions when they use your product, as well as a digital fingerprint of their usage patterns in the forms of support tickets, chat queries or even chatbot interactions, not to mention the heatmap of each session. By combining all of this information we are able to highlight behavioral patterns and showcase the UI/UX challenges, workflow bottlenecks and possible room for product enhancement or user experience improvements.

Why choose IT Svit for enabling Big Data analytics for your business?

All the benefits and value of Big Data analytics can come into play only on one condition — if you have sufficient expertise on hand to deliver the project flawlessly. This is why trying to form a makeshift team of specialists or ordering such a service from your cloud vendor can be not the best business decisions.

Forming an in-house Big Data & DevOps team requires lots of time an effort, bears many risks and does not guarantee success. Quite the contrary, ordering such a service from cloud providers, like Amazon Web Services or Google Cloud Platform will require much less effort but will be quite expensive and will definitely result in vendor lock-in due to using proprietary services and tools.

This is why working with a Managed Services Provider like IT Svit is the best decision in earnest. We have ample experience with the design and implementation of solutions for Big Data analysis of varying scope and scale. We have the ready system components at hand and we know how to build Big Data analytics solutions from scratch and run them efficiently. If you already have a Business Intelligence system in place and want it improved, or are already mid-process and need some technical help — our dedicated team is ready to hit the ground running and help you reach the business goals set. Contact us with the project details and we will be glad to assist you!

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