Capturing, processing, storing, analyzing and presenting data is challenging. Even more challenging is providing the operational context—adding dimensions to data to speed up decision making and adoption of automation. With years of data engineering experience, Softility helps organizations harness the power of data, that would otherwise be thrown away. We employ a structured process involving data pipelines, data lakes, and advanced analytical processing.
Our focus is on identifying, extracting and loading multi-dimensional structured, unstructured, and poly-structured data from various sources. The goal of Data Engineering is to swiftly and seamlessly bring all relevant operational data into the overall data engineering framework. With experience building large, scalable, and highly available data lakes, Softility designs and delivers vast storehouses of data from multiple operational conduits and channels. This central, unadulterated data is stored for heterogenous forms that external analytics and automation tools will depend upon. Data Lakes receive inputs from the data pipeline and engineering component in a raw, unfiltered form. Data cleansing, transformation and enrichment are then performed during this stage, bringing the data into a clear focus and provide context, so that the analytics and data science teams can apply techniques to bring out key insights.