The data engineer’s role is changing quickly. As new tools and self-service data pipelines eliminate traditional tasks such as manually writing ETL code and cleaning data, companies are asking data engineers to provide guidance on data strategy and pipeline optimization.
Also, as information grows exponentially and as the sources and types of data become more complicated, data engineers must know the latest strategies and tools to help the business leverage that data for increased profitability and growth.
If you’re a data engineer looking to make the right decisions about data strategies and tools for your organization, here are four questions to ask about your data practices:
Can your pipeline handle concurrent workloads?
To be profitable, your business must run many data analysis processes simultaneously. To keep up with the demand, implement a modern, cloud-based data pipeline. This type of pipeline features an elastic multi-cluster, shared data architecture that enables the handling of concurrent workloads. It can allocate multiple independent, isolated clusters for processing, data loading, transformation, and analytics while sharing the same data concurrently without resource contention.
Are you using data streaming as well as batch ingestion?
Data comes into your business 24 hours a day, so a periodic batch ingestion can miss recent events. This can have catastrophic consequences, such as failure to detect fraud or a data breach.
Stale data can affect profitability, as well. For example, a company running an online shopping event wants immediate insights into which products are most viewed, most purchased, and least popular, so that it can quickly take actions such as changing the website’s layout to drive more sales.
To use the most recent data and decrease pipeline latency, set up continuous streaming ingestion. Understand how the available streaming capabilities work with different architectures, and implement pipelines that can handle both batch and streaming data.
Do you use test development environments?
The back-and-forth interactions between the person who needs a pipeline and the data engineer creating it can be lengthy. Often, the process will include numerous pipeline design iterations and use a lot of time and resources. To ensure the validity of production data, build pipelines in a test environment, where you can test code and algorithms iteratively until they are ready for a production environment. If you use a cloud data platform as the foundation for running data pipelines, creating test environments can be as simple as creating a clone of an existing environment without the rigor of managing new databases and infrastructure. This will accelerate the time from development to test to production far faster than building these same pipelines on premises.
Is your pipeline development operationalized?
After creating a pipeline, you may have to modify it or scale it to accommodate more data sources. Design your pipelines so they can be easily modified or scaled. The concept is known as DataOps, or DevOps for data, and it consists of building continuous integration, delivery, and deployment into the pipeline using automation and, in some cases, artificial intelligence. Incorporating DataOps in your pipeline will make your data more reliable and more available.
For an extensive list of data engineering best practices, download our ebook, 11 Best Practices for Data Engineers.