Data Will Reveal the Inside Story

Big Data is a data set that is huge in terms of size and is more complicated. Because of the massive volume and immense complexity of Big Data, conventional data processing software cannot manage it. Big Data means datasets containing a considerable amount of diverse data, both structured as well as unstructured.

At COGNXT we allow companies to identify issues or the pain area they are facing in their business, and to solve these obstacles efficiently using Big Data Analytics. We seek to distinguish patterns and draw acumens from this sea of data so that we can be worked upon to resolve the complications at hand.

Big data refers to massive complex structured and unstructured data sets that are rapidly generated and transmitted from a wide variety of sources. These attributes make up the three Vs of big data:

Volume: The huge amounts of data being stored or collected. The data could be structured or unstructured.

Velocity: The lightning speed at which data streams must be processed and interpreted.

Variety: The different sources and forms from which data is collected, such as numbers, text, video, images, audio, and text.

Our Capabilities

Apache Hadoop

We are experts in utilizing Apache Hadoop that is an open-source framework, utilized to efficiently collect and process huge datasets varying in size from gigabytes to petabytes of data. 

MapReduce

A MapReduce job usually breaks the input data-set into independent parts which are processed by the map tasks in a perfectly parallel manner. 

Apache Spark

We work on Apache Spark that is an open-source, classified processing system implemented for big data workloads. It uses in-memory caching, and optimized query execution for fast analytic queries against data of any size.

Talend

With the help of Talend, we do Data Integration. It renders software solutions for data development, data quality, data integration, application integration, data management, and big data. 

MongoDB

MongoDB is a document-oriented NoSQL database utilized for high-volume data storage. We use MongoDB for models that carry documents and functions, equivalent to relational database tables.  

Google Big query

BigQuery is a fully regulated enterprise data warehouse that aids you to manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence. 

Steps
BIG DATA Platforms

To address the different specifications for conducting analysis on Big Data, COGNXT follows a step-by-step methodology to plan the activities and responsibilities involved with collecting, processing, analyzing, and repurposing data. 

We begin with a well-defined business case that gives a fair knowledge of the support, motivation, and purposes of conducting the analysis. 

The next step is committed to classifying the datasets needed for the analysis project and their sources. Identifying a wider class of data sources may enhance the possibility of discovering hidden patterns and correlations.

During the Data Acquisition and Filtering stage, the data is collected from all of the data sources that were classified during the previous stage. The acquired data is then subjected to automated filtering for the elimination of contaminated data or data that has been considered to have no significance to the analysis purposes.  

The Data Extraction lifecycle stage is dedicated to extracting disparate data and converting it into a format that the underlying Big Data solution can use for the purpose of the data analysis. 

Invalid data can skew and falsify analysis results. 

sensible business decisions based on data.

The Data Validation and Cleansing stage are dedicated to establishing complex validation rules and removing any known invalid data.

Data may be spread across multiple datasets, requiring that datasets be joined together via common fields, for example, date or ID. A method of data reconciliation is required or the dataset representing the correct value needs to be determined.

The Data Visualization stage is dedicated to using data visualization techniques and tools to graphically communicate the analysis results for effective interpretation by business users. 

Consequent to analysis results being made available to business users to support business decision-making, such as via dashboards, there may be further opportunities to utilize the analysis results. The utilization of analysis results is dedicated to determining how and where processed analysis data can be further leveraged.

Industries Catered

Manufacturing

Big data analysis has been shown improvement in manufacturing process performance, manage supply chain risks, aid in customizing product designs, and improve quality assurance.

Healthcare

Hospitals, researchers, and pharmaceutical companies are adopting big data solutions to enhancing treatments, performing more effective research on diseases, developing new drugs, and gaining critical insights on patterns within population health.

Agriculture

From engineering seeds to predicting crop yields with astonishing accuracy, big data and automation are rapidly improving the farming industry

Finance

The finance and insurance industries utilize big data and predictive analytics for cybersecurity efforts and personalize financial decisions for customers, fraud detection, risk assessments, credit rankings, brokerage services, etc. 

Media & Entertainment

Media companies analyze customers reading, seeing, and listening habits to create individualized experiences. We can use data on graphics, titles, and colors to make decisions about customer preferences. 

Retail

Big data in retail is imperative to target and retain customers, streamline operations, optimize the supply chain, improve business decisions, and ultimately, save money. 

Make your business big data capable