Big Data And Analytics

We Create Solutions that use Data and AI to Illuminate and simplify life

Enterprises are witness to information explosion due to businesses and individuals embracing digital communications and accumulating data. Enterprises are faced with the challenge to successfully harness the power of Big Data to realize business strategies and optimize operations. This data also has the potential to generate business opportunities, reduce costs and impact organization’s efficiency.

Big Data magnifies the unique benefits of mobile – ubiquity, immediacy and relevance.

Poor data management practices, lack of strategy, complexity of implementation and lack of required skills are the top obstacles for enterprises implementing a big data initiative. We are familiar with such challenges and are equipped to help you with your journey. 

We can align your IT and Business with Big Data for enhanced insights and process automation.

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Data Visualization

The data deluge made by Big Data can open noteworthy business esteem if took care of appropriately. As an ever increasing number of information gets gathered, inconstancy can be uncovered and help support execution. Huge Data can possibly make data straightforward and usable at a significantly higher recurrence. By concentrating on the most important information, you can achieve significant bits of knowledge and change data security.

Tedlite Specializes in Data Visualization techniques which bring a new dimension to viewing parameters which are paramount for your business.

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Predictive Analytics

At Tedlite, We believe in effectively utilizing Analytics on Big Data, one can comprehend the operations and react rapidly to developing business sector circumstances. Complex examination can help you in fundamental low-recurrence gauging to high-recurrence now throwing. By revealing shrouded designs in examination, you can characterize business methodology, tackle business issues, enhance basic leadership and increase upper hand.

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Enormous information can enable you to construct a compelling information procedure to drive development. Issue regions can be distinguished and new arrangements can be produced. The bits of knowledge given by Big Data will help you in operational enhancements and to devise imaginative courses for different procedures.

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We get results

Infrastructure Automation

With experience using major infrastructure automation frameworks, such as CloudFormation, Terraform, CDK and Ansible, our team can provide guidance on the best fit to suit business needs. We are well versed in automation of both Windows and Linux infrastructures and have broad knowledge of available AWS services.

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With deep experience on the AWS platform, we help customers understand the state of their systems and respond rapidly and automatically to changes. We have capability in CloudWatch Metrics, Logs, Log Insights and Alarms as well as X-Ray, Guard Duty, Config Rules and Cloud Trail to maintain visibility of the health of entire environments.

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Perhaps some FAQs

What is data science?

Data science is the study of data. It draws from multiple disciplines, including mathematics, statistics, computer science, human-computer interactions and the visualisation of information. Data science extracts value from data to generate insights, analyse and solve problems, develop knowledge, and make informed decisions. Data science is now a critical component of businesses operations for many organisations, helping to drive strategy and gain an analytical advantage over their competitors in the market.

What is the difference between a data scientist, data analyst and data engineer?

There’s variation in the adoption of data science terms across businesses and industries. The below definitions can be used as a guide.

Data scientist

The term data scientist is used the most broadly. A job posting for a data scientist might describe a role identical to others calling for ‘data analyst’, although there are usually more diverse coding skills needed for the role.

Data scientists are asked to participate in the entire cycle of problems and solutions. They help identify opportunities for companies to use data, while also finding, collecting and integrating relevant data sources; performing analyses of varying degrees of complexity; writing code and creating tools that teams and businesses can use over time; and visually presenting the data to tell a story to company stakeholders.

Data analyst

A data analyst creates and communicates insights from data to measure outcomes, makes predictions and guides business decisions. Often, there’s a lighter coding burden placed on a data analyst, although they may be expected to know certain languages or packages in R or Python.

Data engineer

A data engineer is a designer, builder and manager of the information or ‘big data’ infrastructure. They develop the architecture that helps analyse and process data in the way the organisation needs it – and they make sure those systems are performing smoothly.

What is Python?

Python is a widely used and rapidly growing open-source programming language, commonly used by data scientists, data analysts and software engineers. Unlike Excel, Python is scalable, and is better able to meet business needs by readily handling massive data sets and accommodating the demands of real-time analysis and collaboration. In addition to being immensely popular, Python is a straightforward and user-friendly programming language, making it very easy to learn.

What is R?

R is a highly popular programming language commonly used by data scientists, data analysts and statisticians. R is an important tool for data science and provides an environment that allows users to analyse, process, transform and visualise information to gain insights from data. It’s a popular statistical modelling language used for solving complex problems.