Big Data administration from IT Svit

For more than 5 years IT Svit provides Big Data administration services, helping our customers structurize, process and visualize their Big Data. We can build and manage any solution that will use your data sets and provide the required results — data mining and scraping, extraction and transformation, visualization of results and secure storage.

Apache Hadoop administration services

Apache Hadoop is a major part of most Big Data solutions, allowing businesses and organizations to store and process their data in distributed systems. It is a complex solution, requiring precise configuration and administration to work efficiently. IT Svit provides dedicated Apache Hadoop administration services, based on our rich experience with managing HDFS systems.

Real-time processing of your data sets

The streams and data sets involved in Big Data analytics are huge, meaning the infrastructure supporting them must be scalable and provide high availability to support your workloads. IT Svit uses DevOps best practices to deliver and configure solutions that enable real-time processing of your data at scale and help achieve your business goals.

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The main challenge of Big Data administration lies with the complexity of managing distributed systems to enable reliable processing of huge data sets in real-time. This is crucial to minimize your operational expenses or maximize the value you deliver to your customers — but it requires an in-depth understanding of the DevOps best practices of cloud infrastructure management, as well as Big Data architecture and operations.

However, every Big Data project is built differently, despite having common approaches to system architecture and using the same components and tools. Thus said, the most important part of any Big Data solution is not the technology used, but the expertise behind selecting the toolset for the project, as there are reasons that influence every decision you make. A team with great experience in Big Data administration can determine whether to use Apache Cassandra, MongoDB or Redis databases, whether to build backend on Python/Django/Flask or with Golang or R — whichever suits your project requirements most.

For example, why would you want to use Cassandra? It is a database developed specifically to run Facebook, so it works on distributed clusters of servers to ensure fault-tolerance and high-availability. It has a specific query language to speed up processing requests across huge data sets on different nodes, so it provides maximum resilience and cost-efficiency for high-load projects.

Quite the contrary, MongoDB is a robust high-speed NoSQL database with built-in scalability and high-availability features, but it does not run on distributed clusters. MongoDB enables real-time data processing of any volume, variety or velocity of extraction. In addition, it pairs well with Apache Hadoop to process the data quickly using MapReduce mechanisms to process live data alongside with historical data from various sources and control the quality of analytics real-time.

Why write scripts for your Big Data application in Python using Django or Flask frameworks? Because Python is a powerful high-level programming language with a huge set of libraries and simple syntax. Python enables real-time data processing and integrates well with a plethora of tools and solutions. Django or Flask frameworks help structure and visualize the results of Python operations in various efficient forms — from custom graphs to web apps and API interactions with third-party modules.

R language is specifically developed for statistical analysis of data and enables simple visualization of results, as a part of JuPyteR Notebook —  a common component of Big Data solutions. Working with R and JuPyteR Notebook you can change the code on the move and adjust the data processing workflows quickly, which is essential for training Machine Learning models, enabling prescriptive analytics, etc.

Apache Hadoop is a product from the Apache Foundation, enabling high-velocity processing of distributed data arrays. It uses a simplistic and powerful MapReduce mechanism allowing it to process gigabytes, terabutes or petabytes of data with equally high efficiency and scale from tens to hundreds and thousands of virtual servers with ease.

These are just a few of the tools you might need to build your Big Data solution and achieve the project goals. This is why an in-depth expertise with Big Data administration is essential to design, configure and manage such systems correctly. IT Svit has this expertise and is ready to lend a helping hand to any UK business in need of building a working and cost-efficient Big Data platform.

We use the thorough understanding of Big Data architecture and workflows, as well as DevOps best practices to enable cloud infrastructure to support these workflows. If you need such assistance — contact us, IT Svit team is glad to help you out!

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