IT Svit – a Reliable Big Data Consulting Company

Every company generates Big Data over the course of its IT operations or processes it as a part of their normal interactions with their customers. Big Data experts from IT Svit can help you implement real-time data analysis and turn your flow of unstructured data into a goldmine of information for further decision-making. Such prescriptive analytics will help you maximize revenues, minimize the expenses and gain a solid competitive edge over your competition.

End-to-end solutions based on Big Data technologies

IT Svit has handled web scraping and data mining, Optical Character Recognition and Deep Neural Networks, Machine Learning and textual processing, chatbots and data encryption among many other tasks. IT Svit has gained significant expertise with various Big Data technologies, both building cloud infrastructures for them and writing the algorithms required. We are ready to help your company leverage various Big Data, ML and AI technologies to augment your products with profitable end-to-end Big Data solutions.

Big Data Outsourcing for your Business

Finding top-notch Big Data experts can be quite a task, as these specialists are rarely unemployed and freely available. IT Svit has attracted and retained a solid team of Big Data specialists with ample experience and in-depth understanding of Big Data analytics and best practices of Big Data management. We will be glad to assist you in leading your project to success using the latest and most reliable Big Data tools for business analytics.

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Big Data services are essential for any business that wants to get the most out of their investments. Big Data analytics helps organize and visualize real-time the patterns present in your machine-generated data like reports, system logs, and CRM records. This data is a goldmine of knowledge hiding multiple actionable insights that your business can use to minimize spending, pursue the profit opportunities and improve your workflows. How can this be done?

Big Data technologies utilize a wide range of Apache products like Apache Spark, Cassandra, Storm, Hadoop, and others. Big Data architects work with tools like Tableau, D3.js, and JuPyteR Notebook to visualize the results of the analysis performed by Machine Learning models and Artificial Intelligence algorithms. There are multiple methodologies used for training ML models, like Deep Neural Networks (DNNs), Naive Bayesian algorithms, Decision Forests and several dozen more pieces of ML & AI technology that can monitor and analyze your data real-time.

There are three main approaches to building data analytics solutions using Big Data technologies. A company might subscribe to Big Data analytics solutions from cloud computing providers, might try to attract Big Data architects to their in-house team or try to work by outsourcing Big Data projects to a reliable Big Data consulting company. All variants are viable and below we explain the benefits and shortcomings of each approach.

Opting for Big Data analytics services from cloud vendors 

Every cloud service provider has features and products aimed at working with Big Data and monitoring your production environments to give you in-depth business intelligence on the systems you run. AWS Kinesis, Lambda, and DynamoDB from Amazon Web Services; Google Big Query, Dataproc and Dataflow from Google Cloud; Azure Databricks, Azure Stream Analytics and Power BI embedded from Microsoft Azure — all of these, and much more services allow building complex and efficient Big Data solutions for your business.

The main benefit of such a cooperation is the guarantee that your analytical system will be delivered on time and under budget. It will also be run by professionals, so the risk of human-originated errors is minimal. 

The downside to this is exactly the same as with all the other cloud-based services — you agree to vendor lock-in and in case you ever want to move to another cloud platform, you will have to spend lots of time, money and effort to accomplish the transition process successfully.

Hiring Big Data architects in-house

The most important advantage of building a Big Data team in-house is that you accumulate the expertise within your company. Therefore, once these IT engineers solve the issues for your company, you can provide Big Data analytics-as-a-Service to other businesses. However, this approach has multiple underwater reefs, just like any other hiring process. First-class Big Data analysts and Machine Learning specialists are not easy to come by. 

Thus said, you will have to spend a lot of time, money and effort on finding, hiring and onboarding very costly specialists. All this time your Big Data analytics project is on standby, and if any of the crucial team members quit — your project is pretty much doomed.

Outsourcing Big Data projects to a Big Data analytics company

There are multiple Big Data services companies around the globe. They specialize in designing, building, and maintenance of bespoke Big Data solutions for businesses of all sizes. Their level of expertise is close to the one the cloud service providers’ IT staff possess, yet they charge much less. 

The main shortcoming of working with them is that the teams build the solutions they are comfortable with, not necessarily the best ones for your needs. Besides, such companies have huge expertise with building and running Big Data services, yet they often lack experience with building the underlying IT infrastructure to support the systems. This might result in unexpected performance drops or bottlenecks that will hamper the efficiency of your business analytics.

Working with a Managed Services Provider

Thus said, the optimal solution for building a Big Data analytics solution is by partnering with an IT outsourcing company, a Managed Services Provider or MSP. Why do we think so?

MSPs are the data services companies that provide the full range of IT consulting and software development services:

  • Web and application development
  • Cloud infrastructure design, implementation, and optimization
  • Application containerization and cloud workloads management
  • Design and implementation of data management and analysis
  • Training ML models and AI algorithms for real-time Big Data analytics
  • Running predictive analytiсs to build self-healing infrastructure

 

Below we describe each step in more details and highlight the benefits for your business.

Web and application development

First things first — you need a product or a service to generate some data (and income!) Should you want some great features added to your product — we can provide them or help build your product from scratch.

Cloud infrastructure design, implementation, and optimization

If you did not release your product to the cloud at once, you might need to redesign its infrastructure once you begin to scale. Even if you run cloud-based infrastructure from the start, it might prove inadequate with time due to updated workload patterns. IT Svit can either design and implement the cloud infrastructure from scratch or assess the existing system and redesign it to remove bottlenecks and improve resilience to high workloads.

Application containerization and cloud workloads management

There are multiple reasons for app containerization, performance and resource-efficiency first of all. However, containerized apps demand specific workflows and infrastructure, namely Terraform and Kubernetes clusters, Jenkins and Ansible CI/CD pipelines, ELK stack monitoring. IT Svit has ample experience with configuring these tools to ensure the stable functioning of your cloud infrastructure and cloud workloads.

Design and implementation of data management and analysis

Once your cloud infrastructure goes live, it begins to accumulate troves of machine-generated data, from CRM records to server logs. You need Big Data experts to properly organize the dataflow and build an efficient data analysis process. Hiring a ready team with polished processes is the best way to saving money and time, and getting the best ROI.

Training ML models and AI algorithms for real-time Big Data analytics

Big Data analysis is performed by Machine Learning models that are trained on historical data to learn to find patterns and predict the results of operations in the future. These models require supervised or unsupervised learning, as well as reinforced learning to maximize the accuracy of predictions. Choosing the most suitable model and training it correctly is what Big Data experts are needed for.

Running predictive analytiсs to build self-healing infrastructure

Once the models are trained and deployed to your production environments, they begin collecting data and issuing recommendations to improve the outcomes. For example, when an ML model recognizes the spike in CPU usage, it issues a warning to increase the number of CPU cores available to deal with the demand. 

Once the spike is gone, the model shuts down the spare CPU power. This way your AI algorithms can deal with all the routine and repetitive infrastructure management tasks, using such predictive analytics to create a self-healing IT infrastructure.

As you can see, Big Data technologies can be very useful for multiple aspects of your business! Contact IT Svit and we will help you leverage all the benefits of Big Data!

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