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.