Big Data

InetSoft's Style Intelligence can natively access big data stores such as Cassandra, Hbase, MongoDB. This is because Style Intelligence's big data deployment is natively built upon Apache Spark/Hadoop. It can not only be dropped into an existing big data environment, but it can also be deployed with its self-managed environment

big data sources

Bring Dashboards and Reporting to Your Big Data

InetSoft's Style Intelligence drops into an existing Apache Spark installation. This bring-the-software-to-the-data approach eliminates costly big data movement for analytics and reporting. Style Intelligence can also be deployed with its own built-in Spark cluster.

In this case, only minimal expertise in Spark is required. The cluster is mostly configured and administered by Style Intelligence behind the scenes to maximize data processing and mashup performance.

spark native big data platform

Big Data and Data Lake Analytics

Big data platforms have made data lakes possible where data, mostly in raw format, is stored for future analysis. Data lakes have become a viable, sometimes even preferred, alternative to data warehouses. Since they operate off raw data by design, the approach for data analytics comes from a very different direction. InetSoft's native Spark integration makes data lakes accessible like all other data sources.

big data and data lake analytics, dashboards and reporting

Productionalize Machine Learning and Mashup

InetSoft's machine learning component gives business users direct access to machine learning models. For data scientists, it provides a platform to quickly productionalize models that have succeded experimentally. As part of InetSoft's mashup engine, machine learning output can be readily mashed up with human designed analysis.

big data machine learning via spark

Spotlight: Big Data Analysis in the Travel Industry

In the travel industry, the utilization of big data has revolutionized the way companies operate and cater to their customers. One exemplary application lies in the realm of personalized recommendations. By analyzing vast amounts of data collected from various sources such as booking histories, travel preferences, and even social media interactions, travel companies can create highly tailored suggestions for their customers. For instance, airlines can recommend specific destinations based on a traveler's past trips and interests, while hotels can offer personalized package deals that align with individual preferences, ranging from room types to nearby attractions.

This not only enhances the customer experience by providing relevant and customized options but also increases customer satisfaction and loyalty, ultimately driving business growth. Moreover, big data analytics in the travel industry plays a crucial role in optimizing operational efficiency and resource management. Airlines, for example, utilize predictive analytics to forecast demand for flights, allowing them to adjust ticket prices dynamically to maximize revenue. Similarly, hotels employ data analytics to forecast occupancy rates and adjust room rates accordingly, ensuring optimal revenue management.

Furthermore, big data helps in improving logistical operations, such as route planning and scheduling, leading to smoother travel experiences for passengers. By leveraging big data insights, travel companies can streamline their operations, minimize costs, and enhance overall efficiency, ultimately benefiting both the businesses and their customers alike.