Next-Gen Big Data Solutions

Big Data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information.

Big Data include finding cheap, abundant storage, faster processing, from affordable, distributed big data platforms, such as Hadoop as well as implementing parallel processing, clustering, MPP, virtualization, large grid environments, high connectivity, high throughputs and Cloud Computing based upon your needs and budget.

We can help your organization do more than just acquire data. We can help you make sense out of your Read More data putting it all together and making it actionable. By building connections from a myriad of data sources to visualization tools, we can help you to work with very large data sets and get answers quickly that were previously unattainable.

The emergence of a new wave of data from sources, such as the Internet of Things, Sensor Networks, Open Data on the Web, data from mobile applications, social network data, together with the natural growth of datasets inside organizations, creates a demand for new data management strategies which can cope with these new scales of data environments. Read Less

Big Data Strategies

Data Science

Apply data science approaches to identify, prioritize, and plan for the right big data analytics use case and taking decisions based on what is actually happening, and predicting the next big trend.

Data Analytics

Big Data Analytics is the process of collecting large chunks of structured/ unstructured data, segregating and analyzing it and discovering the patterns and other useful business insights from it.

Data Engineering

Dealing with big data, clouds, pipelines, and platforms and Integrate and organize large data volumes and align to business objectives with our experienced engineers.

Data Architecture

Data architecture solutions that leverage data warehousing and business intelligence capabilities to support decision making at all hierarchy levels.

Big Data Characteristics

The term “big data” implies large quantities of data. Although this is true, big data is also different from traditional data in other respects. These are characteristics that differentiate big data from traditional data.

Volume: Enormous amount of data, Value: Results of data analysis, Veracity: Completeness and accuracy of data, Visualization: Results delivery, Variety: Traditional, structured and unstructured data, Velocity: Constantly increasing speed, Viscosity: Stick or call for action, Virality: Convey a message.

Accurate analysis carried out based on big data which helps to increase and optimizes operational efficiencies, enable cost reductions, and reduce risks for the business operations.
Read More
Our Big Data solutions enables in a way that it allows them to innovate, improve and make faster decisions. In short it allows businesses to be more intelligent. All the emails, mobile devices, databases, apps and servers combine to compile this data.
When all this data is formatted, manipulated, combined, analyzed and stored can benefit a company big time. It allows the business insight about what new can be done and what can be left in order to increase revenue, improve operations and most importantly retain revenues. Read Less

Big Data Business Values

Our Big Data business values enables Identify the big data sources need to use, Map the big data types to data types, Ensure that you have the processing speed and storage access to support, Select the data store best suited to the data types, Modify the existing workflow to accommodate big data or create new big data workflow.

Cost Savings

Enables cost advantages to business when large amounts of data are to be stored and these tools also help in identifying more efficient business.

Improves pricing

Use a business intelligence Big Data to evaluate your finances, which can give you a clearer picture of where your business stands.

Time Reductions

The high speed of in-memory analytics can easily identify new sources of data which helps businesses analyzing data immediately and make quick decisions based on the learnings.

Focus on local preferences

Small businesses should focus on the local environment they cater to. Big Data allows you to zoom in on your local client’s likes/dislikes and preferences even more.

Increase sales and loyalty footprint

The digital footprints that we leave behind reveal a great deal of insight into our shopping preferences, beliefs, etc.

Control online reputation

Big data tools can do sentiment analysis. Monitor and improve the online presence of your business, then, big data tools can help in all this.

Understand the market conditions

Better understanding of current market conditions by analyzing customers’ purchasing behaviors and produce products according to this trend.

New Product Development

By knowing the trends of customer needs and satisfaction through analytics you can create products according to the wants of customers.

Big Data Workflow

A big data analytics workflow is long and complex, with many programs, tools and scripts interacting together. In general, in modern organizations there is a significant amount of big data analytics processing performed outside a database system, which creates many issues to manage and process big data analytics workflows. In general, data preprocessing is the most time-consuming task in a big data analytics workflow.

We defend the idea of preprocessing, computing models and scoring data sets inside a database system. In addition, we discuss recommendations and experiences to improve big data analytics workflows by pushing data preprocessing like data cleaning, aggregation and column transformation into a database system.

The availability of systems able to process and analyses big amount of data has boosted scientific advances in several fields. Our workflows provide an effective tool to define and manage large sets of processing tasks. In the big data analytics area to provides a cross-domain big data analytics framework for the analysis of scientific, multi-dimensional datasets.

Our workflow management system to support the execution of complex scientific data analysis, schedule tasks submission, manage operator’s dependencies and monitor jobs execution. The workflow management engine allows users to perform a coordinated execution of multiple data analytics operators in an effective manner.