Data Engineering
Automatically manipulate and move data.
Overview
Data engineering ensures that data is of high quality and is delivered to the right place at the right time in the proper format. This is done via automated data pipelines, APIs and Python data processing scripts.
Solutions
Data Pipelines
Data pipelines capture data from a myriad of sources and manipulate it in transit using a process called ETL or Extract Transform Load. It is then delivered to a destination database. Data pipelines can run on large batches on off-hours or smaller amounts incrementally throughout the day.
Feature Engineering
Perform transformations on your existing data using statistical analysis, mathematical calculations and aggregation methods that improve the detail of your data and give a more complete picture for reporting and analytics.
Cleaning
Ensure data quality with data cleaning to ensure consistent terminology, deal with missing values to ensure your data meets quality standards for accuracy and reliability.
Machine Learning
Apply machine learning methods to automatically make predictions, perform classifications, cluster groups and make recommendations. Integrate these into apps or deliver results with automated reporting.
Use Cases
Common use cases for data engineering
Sync Databases
Sync databases across servers with common fields using a data pipeline so multiple department have access to accurate data.
Data Enrichment
Utilize APIs or feature engineering to enrich your data with third party data and see the big picture. Then automate reporting for those that need it.
Data Integrations
Understand how your CRM data and point of sale data is related and get better reports and make better decisions.