Data engineering involves taking raw data from diverse sources and wrangling it into something that can be used for enterprise purposes, such as analytics.
Data engineers are responsible for building secure solutions that harness the potential of the available data. They also help upgrade and maintain existing data solutions.
Data Engineering in a Traditional Database Environment
Most organizations and enterprises have a variety of active databases, including CRM, ERP, e-commerce systems, and production systems. Some of these may run on SQL databases, while others may produce data as an export file, such as CSV or JSON.
Data scientists can perform valuable analytics, but only if the data is:
- Combined: All data must be gathered in a single location so it can be queried as a whole
- Uniform: Data must be in a standardized format (i.e., dates stored as DATE data types, rather than text or integers)
- Unique: Duplicate records must be removed
- Clean: Data cleansing must remove any corrupt or inaccurate data before analysis
- Current: All data should be recent, with any stale data cleansed
Data engineering is about building a solution that meets these criteria so that analytics experts have the information they need to generate accurate insights. Typically, this involves building a pipeline that connects enterprise systems to a data warehouse.
In most environments, engineers focus on three things:
1. Data Sources
The data engineer reviews all relevant data sources, examines the data outputs, and starts planning the most effective way to create a data pipeline. This stage involves working with all stakeholders, from those who work with each raw data source, to the analytics experts relying on cleansed data.
2. ETL (Extract, Transform, Load)
ETL is the pipeline linking the original data sources to their final destination. As the name suggests, ETL is a three-step process:
- Extract data from sources
- Transform into a standardized format
- Load into the final destination
Data engineers rely on ETL automation tools such as Xplenty to implement this stage. Xplenty integrates easily with a vast range of data sources and reduces the need for extensive configuration work.
3. Data Warehouse
The data warehouse is the final destination for post-ETL data. Data engineers are responsible for ensuring that data arrives in a suitable format for analytics and other enterprise purposes. Upgrades and maintenance also fall within the remit of engineering.
Data engineering focuses on building this pipeline as securely and reliably as possible, with the most efficient use of cloud and on-premise resources.
Data Engineering in a Big Data Environment
Data engineering is fundamentally the same when working with Big Data. It’s still a matter of taking disparate data sources, standardizing them, and transporting them into massive data structures. The main difference is the scale of the challenge and the technologies involved.
Big Data engineers use data lakes & data warehouses, facilitated by platforms such as Hadoop or Apache Spark. Big Data engineers often work with a data architect to construct large-scale data pipelines that meet business requirements.