Data Engineering and Analytics
Changing the Way the World Lives and Works
Data engineering to intelligence is a process but the way to achieve it is distinct in every use case that we have solved so far. The following image describes the fundamental steps that we take to ensure we achieve the best business outcome for your requirement
Data Preparation and Loading (ETL or ELT process)
Data Preparation is one of the most critical steps in building a data layer. The aim of this process is to reduce the errors or skewed information from the data and create information that’s useful and relevant to achieve the business outcome.
Every use case requires different tools to be used in achieving this, Entrans however, brings in frameworks and experience working in AI to make this process more effective and faster
Intelligent Join Detection
Use the power of AI to detect exact or fuzzy match single or multi-column join criteria and combine datasets for data feature enrichment
The name of an individual might be available in different datasets in different ways. Just imagine correlating this data across datasets? We help make it simple using AI.
Intelligent Data Ingest
Automatically detect file types at ingestion time and intelligently flatten complex structures into tabular representations for learning dataset creation.
Your dataset may be in different formats, however, during its ingestion we expect all the data to flatten out into columnar formats. This is possible using AI.
Intelligent Production Data Pipelines
Automate/schedule data preparation and loading to run at regular intervals with automatic detection and inclusion of preceding data preparation and loading steps to create an entire data flow that’s automated and repeatable.
Full Data Lineage
Record every transformation of your data automatically using the data governance and lineage tools to understand the complete audit trail.