Modak is a global provider of Data Science and Analytics services. It is focused on transforming the Healthcare and Life Sciences industry through Big Data. Founded in 2010 in Hyderabad, India, we enable enterprises make better and faster decisions.
Using Metaprogramming approach, Modak has gained a significant advantage in Data Analytics industry
Data is the core to generate business insights. Today, big data is the talk of the town. To manage such an enormous enterprise data is a tricky job. The question is not how to run queries for a single block of data, but how to run queries on billions of rows of data. The technical challenge in any business intelligence is how to query millions of events iteratively. Hence, augmented data preparation using metaprogramming is the key to process millions of rows of data quickly. All the programs designed to read, analyze, transform, or modify themselves are examples of metaprogramming.
According to Gartner, more than 70% of big data projects have failed due to the large amount of time spent on data preparation and curation. Most businesses spend more time in generating insights using machine learning and automation. By the time the data reaches the visualization phase, either the data becomes outdated or the technology becomes outdated. At Modak, our metaprogramming approach focuses mainly on the data preparation phase. The metaprogramming approach drastically accelerates the data preparation and curation processes.
Metadata is essential for data preparation in any big data platform. It contains key information about the data. Modak's nabu metaprogramming approach leverages the metadata to ingest, curate and unify the data sets. Metaprogramming basically generates code through metadata, which we capture from the source and destination, save into technical, operational and business metadata catalogues.
One of the benefits of metaprogramming is it increases the productivity of developers once they get past the convention and configuration phases. In metaprogramming, metadata is used in data ingestion, cascading templates and creating entities which is helpful for data visualization. Through meta programming approach, we follow a complete automated end-to-end process right from source to ingestion & curation, so that users can utilize optimized data for their process.