Big Data administration services from IT Svit
One of the most complex challenges of working with distributed data management systems is keeping all of them synchronized and able to process large data sets in real-time to provide value for your customers, or optimize your OPEX. The point is, this requires both a thorough understanding of data science principles and workflows — and DevOps best practices of cloud infrastructure management.
Thus said, while there are common tools and general principles of data administration, each Big Data solution deployed in production is unique. Some Big Data systems use Apache Cassandra database, some work with Mongo DB, some are written with Python/Django, while others use R language or Golang, etc. Thus said, the choice of components depends solely on the area of application for the Big Data platform you need, the results you want to achieve and the way you want to make use of them.
- Why choose Apache Cassandra, for instance? It is a database built specifically for Facebook, to enable the stable performance of the world’s largest social media. It is distributed (meaning it runs on clusters that include multiple nodes), uses specific Cassandra Query Language to divide each query so it can be processed by multiple nodes to speed it up, and has a ton of other useful features that can make it a prime choice for building high-load data handling.
- MongoDB, on the other hand, is a highly performant NoSQL database that provides high-availability and automatic scalability by default. This ensures real-time processing of various types of data, regardless of their volume or velocity of influx. It also works excellently in pair with Hadoop clusters using MapReduce, which gathers real-time data from MongoDB and processes it together with historical and live data from other sources to deliver smarter processes.
- Python is a high-level programming language, which is quite often used for creating scripts that run Big Data solutions, as it is a powerful general-purpose language with simple syntax and powerful set of libraries. Django is a Python framework, helping visualize the results of back-end operations in an easy-to-consume form of web applications, dashboards, and graphs. It also enables the simple interaction of Python code with third-party modules and tools.
- R language is an open-source language for statistical processing of data and its visualization, which is widely used as a part of Big Data solutions.
- JuPyteR Notebook is an open-source tool for visualization of code, Python or R code, in particular. It is very convenient, as it allows editing the code on the move and tracking the results of your updates in real-time. It is one of the most popular tools when developing Big Data solutions, training Machine Learning models that enable predictive analytics, etc.
- Apache Hadoop is a framework developed by the Apache Foundation to enable distributed processing of vast data arrays using quite simplistic models of programming. It is capable to scale from dozens to thousands of servers seamlessly, using its separate computing resources and storage. It uses a simple and powerful data processing mechanism called MapReduce.
Thus said, there are many more tools that can be used for Big Data administration and the correct configuration of instruments depends on the business task you need to solve. Thus said, the most essential part of correct Big Data solution design, implementation and administration is the in-depth understanding of the best practices of data science, cloud infrastructure design and administration, CI/CD workflows, performing Big Data analytics, etc.
Therefore, to build a good Big Data solution, you would actually need quite a team: a data scientist, a system architect, a DevOps engineer, a Hadoop administrator, and some more talents, based on the technology stack your product uses. A data scientist must select the most appropriate ML/AI algorithm for your case and train it using large data sets. A system architect has to design and deploy a system capable of processing this data. A DevOps engineer has to build Continuous Integration / Continuous Delivery workflows to automate the data processing operations. A Hadoop administrator has to configure Apache stack to handle the task at hand, etc.
As a result, to successfully ensure developing a data analytics solution and ongoing Big Data administration, a business has to gain access to considerable expertise, which is not easy to come by. In addition, such expertise does not sit around waiting for you to pick it up — you need to find a team that can handle Big Data administration and delivers end-to-end Big Data analytics solutions.
IT Svit is such a team, as we have 5+ years of expertise with bespoke Big Data solution design, deployment and ongoing administration. We field skilled developers, DevOps engineers and Big Data architects, who have already developed various data analysis solutions for marketing companies, agricultural companies, logistics businesses, financial organizations, medical institutions, etc. We would be happy to assist you to accomplish your project too!
Remote Big Data administration services
Many companies try to leverage Big Data tools and services provided by cloud platforms like AWS, Google Cloud Platform or Microsoft Azure. There are two major downsides to this approach: the cloud support administrators use platform-specific tools to build Big Data systems, which results in vendor lock-in. They also have to cater to the needs of all the customers, so your requests are processed in a queue, and while you are covered by an SLA, 4 hours ticket resolution is by far not the best way to deal with Big Data administration.
This is why various businesses prefer to use IT outsourcing services from companies like IT Svit. We are a Managed Services Provider and we can deliver all the types of services required to enable end-to-end Big Data administration projects, from planning the data analytics system and training the Machine learning model all the way to delivering and configuring the cloud infrastructure required to run it. In addition, we use open-source analogs of vendor-specific tools to avoid vendor lock-in and save your money while providing resilient and robust Big Data solutions.
Real-time data processing to reduce your OPEX
Another feasible implementation of Big Data solutions is deploying the data monitoring systems to track the performance of your cloud systems. When a trained Machine Learning model begins to monitor a set of parameters, it can apply the Artificial Intelligence algorithms to identify the normal system operational parameters.
If these patterns change abruptly (due to the rapid growth of active user sessions or the beginning of a DDoS attack, etc.) — the predictive analytics system applies one of the appropriate scenarios to minimize infrastructure load and ensure an uninterrupted end-user experience. Due to this approach, your business can scale the infrastructure to meet the demand and shut down the excessive instances once the peak load is gone, thus greatly optimizing your IT operations cost-efficiency and reducing OPEX.
IT Svit can design and implement any kinds of Big Data solutions, for your customer-facing applications or for mission-critical infrastructure components alike. We provide timely and efficient Big Data administration services and if you need these — contact us right away!
