Big Data analytics solutions
leverage effective big data analytics to analyze the growing volume, velocity and variety of data for the greatest insights!
big data analytics uses advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data from different sources and sizes, from terabytes to zettabytes.
What is big data exactly?
It can be defined as data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency.
Characteristics of big data include high volume, high velocity and great variety. Sources of data are becoming more complex than those for traditional data because they are being driven by artificial intelligence (AI), mobile devices, social media and the Internet of Things (IoT). For example, the different types of data originate from sensors, devices, video/audio, networks, log files, transactional applications, web and social media — much of it generated in real-time and at a very large scale.
With big data analytics technologies, you can ultimately feel better and faster decision-making, modelling and predicting future outcomes and enhanced business intelligence. As you build your big data solution, consider open-source software such as Apache Hadoop, Apache Spark and the entire Hadoop ecosystem as cost-effective, flexible data processing and storage tools designed to handle the volume of data generated today. Big data analytics is the often difficult process of analyzing large amounts of data in order to reveal information such as hidden patterns, correlations, market trends, and customer preferences that can help businesses make better decisions.
Big Data analytics architectures provide organizations with a way to evaluate data sets and obtain new information on a large scale.
Big data analytics is advanced analytics that entails complicated applications powered by analytics systems that include predictive models, statistical algorithms, and what-if analysis.
Migrate to a Cloud-based data ecosystem to enable the seamless flow, management, and consumption of fast-growing structured and unstructured data volumes.
Create road maps to upgrade the technological platform that powers data and build robust, calculable big data architectures in the Cloud.
Utilize Machine Learning and Artificial Intelligence technology to analyze data at a high rate and scale, validate its authenticity and reliability, and ensure that everyone in the organization makes data-driven business choices.
- Faster, better decision-making.
Businesses can access a large volume of data and analyze a large variety of data sources to gain new insights and take action. Get started small and scale to handle data from historical records in real-time.
- Cost reduction and operational efficiency
Flexible data processing and storage tools can help organizations save costs in storing and analyzing large amounts of data. Discover patterns and insights that help you identify and do business more efficiently.
- Improved data-driven go-to-market.
Analyzing data from sensors, devices, video, logs, transactional applications, web and social media empowers an organization to be data-driven. Gauge customer needs and potential risks and create new products and services.
Leading big data companies in India—those that distinguish, excel, and react to evolving demands faster than competitors—rely on robust, broad data systems in today’s fast-paced business climate. They can understand how using big data in a planned, systematic way, along with analytics, may lead to greater business outcomes. Companies need a simple approach to change how they gather, interpret, and use data. Turning issues into feasible opportunities necessitates a flexible modern data and analytics platform. Designing a flexible data architecture that scales with business is critical in the era of Big Data. Saven Technologies assists companies in the development of new, cloud-based data solutions.