Data Engineering
Data engineering is the disciple that ensures that data is of high-quality and is delivered to the right place at the right time in the proper format. It helps everyone have access to accurate information in real-time and avoid data silos.
Focus Area
Data Storage
Develop systems that easily capture, store and retrieve data.
Integrations
Integrate systems together, move data and perform ETL.
Data Quality
Ensure that you are using quality data that gets good results.
Technology Development
Some of the things you can do with data engineering.
Database Design
Design databases to remove redundancy through relationships, using correct field types and optimize performance so it is good for today and scales for tomorrow. Useful for websites, apps and data warehouses.
Data Cleaning
Messy data gets you bad results. When data is cleaned it will have consistent terminology, deal with missing values and have a level of completeness that helps systems to return good results.
API Development
Rest API development allows you to get your software to integrate with other software products. Useful for E-commerce payment integration or providing a back-end for a mobile app.
Data Pipeline Development
Automatically capture data from a myriad of sources and use a process called ETL (Extract Transform Load) while in transit and deliver it to a final destination. Useful for syncing databases and populating data warehouses.
Data Processing
Automatically process a list of data at high volume in a similar way using Python scripts. Useful for checking that email addresses are properly formatted or splitting names into multiple fields.
Data Enrichment
Enrich your data with third-party sources or perform feature engineering to give you a higher quality data set and better insights. Useful for appending customer data or running calculations on your existing data.