Trifacta, a data analysis services platform, recently received VC investment to advance on their efforts of making data wrangling easier for data analysts. The goal is to collect, cleanse and munge data in a fraction of the time and effort it currently takes.
Data wrangling has traditionally been the most time consuming and painful part of every Big Data project. In our era, data is flowing, heterogeneous and constantly changing attributes as data sources are evolving. NoSQL databases have long tried to answer this question in the storage side by being column based or document based but the problem still remains in getting the data collected and applying semantics to it.
Trifacta is approaching the problem from a user centric perspective, instead of a developer one. Business analysts and data scientists will be able to cleanse datasets in a visual oriented way. Based on research at Berkeley and Stanford, the platform aims to make employees and machines collaborate together in extracting insights from datasets.
Automated smart sampling from big data sets together with visualization allows for the analyst to discover interesting patterns at a fraction of the time. Trifacta can then apply machine learning algorithms to suggest ways to reorganize information and get it into shape. The analyst can group the dataset into logical parts of information, normalizing it one step at a time and viewing the outcome in a user friendly way along its course of work. Generalizing in the whole dataset is the last step which turns the semi-structured dataset into shape. The platform is designed from ground up with user experience in mind to allow data analysts to shift in depth through data, without the need to develop complex pipelines to cleanse the data and bring them into the Data Warehouse.
Trifacta’s predecessor research project, DataWrangler and the research paper are available online and can give a sneak preview of what Trifacta is getting to, since they are still in a closed beta, only scheduling demos by invitation.