Data Engineering and Analytics

Data Science  and Engineering
data engineering

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 science process

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

Data Preparation and  Loading
Intelligent Join Detection

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

Scenario

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

Intelligent Data Ingest

Automatically detect file types at ingestion time and intelligently flatten complex structures into tabular representations for learning dataset creation.

Scenario

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

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

Full Data Lineage

Record every transformation of your data automatically using the data governance and lineage tools to understand the complete audit trail.

Ingestion

Tools used

aws Emr
aws Emr
PySpark
PySpark
sqoop
Sqoop
Flume
Flume
Amazon Glue
Amazon Glue
PySpark
Data Standardization
Aws Lambda
Aws Lambda
 kafka
kafka
Azure Serverless
Azure Serverless

Data store

Tools used

aws dynamodb
aws dynamodb
HDFS
HDFS
aws s3
aws s3
azure blob
azure blob
Azure synapse
Azure synapse
RedShift
RedShift

Data Visualization and Intelligence

Tools used

Tableau
Tableau
PowerBI
PowerBI
Quickview
Quickview
Jasper Reports
Jasper Reports
D3.js
D3.js
Reddash
Reddash

Machine Learning and Intelligence

Tools used

Python-Scikit
Python-Scikit
TensorFlow
TensorFlow
Open CV
Open CV
Data Robot
Data Robot
Numpy
Numpy