Data engineering 101: a beginner’s guide
Data engineering bridges the gap between data sources and end-user enablement. In data engineering, data sources are found in databases, object stores and file systems, whilst end-user enablement covers dashboards, machine learning and more.
Adopting a cloud-first approach through data engineering allows you to deliver scalable data operations whilst driving down computing costs across your organization.
Some key areas of data engineering are:
- Data quality – ensuring that your data is accurate, consistent and complete
- Data governance – establishing data ownership and controls around user access
- Data security – implementing sensitive data protection protocols through authentication and authorisation
- Data scalability – designing systems that can scale as data volumes increase.
Data quality in data engineering
From profiling and cleansing to wrangling and monitoring, data quality is essential in data engineering. There are several areas in which an organization can ensure the quality of their data.
Removing duplicate values
Checking for and removing any duplicate values in a dataset to maintain the integrity of your data.
Auditing for missing data
Incomplete data sets are one of the biggest challenges to data quality, so regularly updating your data and creating missing data alerts helps to maintain full visibility of your datasets.
By developing code that is modular and reusable, it is easier to maintain version control and track changes to datasets across your organization.
Change in data capture alerts
Whenever a dataset is changed, the dataset owner is alerted, helping to keep track of changes.
Automating data pipelines
By automating the process across data testing and deployment, manual errors are reduced and workflows can be orchestrated to improve efficiency.
Data compliance in data engineering
Secure file locations
When it comes to storing files and scripts, they should be saved in a secure shared repository in the cloud, rather than a hard drive.
Limit access control
Ensuring that only the intended users have access to the data helps to limit the number of sensitive data breaches.
Explore ProCogia’s data engineering solutions
We provide agnostic advice at every stage of our clients’ data warehousing projects to help them utilize the best technologies and solutions for their needs.
From traditional SQL-based data warehouses to lakehouses for larger data sets. We can advise on the best data warehousing solution to meet your specific storage needs.
We’re experienced at designing architectures which offer elastic scaling, end-to-end security for data in motion and cost and performance scalability.
Big data engineering
We can help you to leverage modern data processing technologies such as Spark and Databricks to your advantage through cost-effective data warehousing of very large data sets.
A data engineering company you can rely on
ProCogia has a proven track record of working with businesses worldwide to create solutions to leverage the power of cloud computing in data engineering. Allow our expert team to extract, transform and load your data using game-changing solutions.
From advising on your project planning, suitable technology and architecture to roadmapping and implementing your cloud journey. We partner with all major cloud providers, including Utilizing Azure, AWS, GCP and Snowflake, to support organizations operating at scale with game-changing data engineering solutions. Allow us to help you discover a cloud provider suitable for your data engineering needs, or to migrate your existing on-premise solution over to.
If you’re ready to work with a data engineering company that will help you to determine your data requirements, build and tailor the requisite infrastructure to your organization and unlock the full value of your data, get in touch below.