Data engineers are just one member of a suite of specialised individuals that are trained to work with data.
Data is a resource as well as an object that can be gathered, analysed and transformed for a variety of uses.
The primary role of a data engineer is to set the groundwork and infrastructure required for data collection, analysis, synthesis, insight and modelling.
The data engineer’s end product should be data that is ready to be stored and used in a variety of products, predictive models and more.
The Field of Data Engineering
A data engineer, much like any engineer, works primarily with infrastructure and architecture, as well as databases, business logic and pipelines.
Data engineers are responsible for connecting various data sources together, designing data pipelines that transport data from where it is inducted to where it is needed.
Data systems need to talk to each other. For example, data collected via a mobile app may have to be used in a physical office on the other side of the world.
Data engineering focuses on building data architecture that fulfils the following criteria:
High Quality Structured Data
Data engineers need to ensure that collected data is plentiful and detailed, allowing for deep analysis and the creation of accurate models. Even small businesses might collect large datasets. Each customer might be associated with hundreds of variables, such as their address, age, gender, the products they buy, the products they have in their basket, their shop interactions, etc.
The same is true outside of commercial settings, for example, weather software may need to collect thousands or even millions of variables to make its predictions accurate.
Data often begins life as raw and unformatted. It may contain many errors that need to be corrected prior to use. Typical problems include missing values, misplaced decimal places and plain incorrect data. If unclean data is piped into models, huge problems can ensue.
Data engineers have to ensure that the data flowing through their systems is clean and readily consumable at its endpoints. This will involve various data parsing and data wrangling techniques.
Data infrastructure needs to be secure and perform optimally for the task at hand. This means constructing scalable architecture that can deal with volumes of data.
Data engineers may use traditional physical RDBMS databases with SQL or put databases in the cloud with AWS or Google Cloud. Serverless pipelines and live streaming can ensure that new data is made instantly available for query and analysis.
Data engineers can work with data architecture across the entire data science process from initial collection/induction all the way through to endpoints or APIs as embedded in products and services.
However, the emphasis of data engineering is angled towards the bottom line or foundation of data science.
In the data science ‘hierarchy of needs’, data engineering is most relevant to collection and movement/storage.
Collect: Here, data engineering plays a pivotal role in collecting data via user-generated content, customer relationship management (CRM), logging, manual data collection and sensors. Data collection techniques will be explored, including the best methods for finding existing data across systems and methods for collecting new data.
Move/Store: Data engineers will then look towards moving and storing collected data.
Structured data will have to be transformed and/or parsed or cleaned using an ETL or ELT pipeline. Structured data can be queried or analysed for later use further up the hierarchy of needs.
Whilst data engineers often build dashboards to provide insights on databases, the process of analysis and model training may be taken up by a different data practitioner.
What Skills do Data Engineers Need?
Truth be told, whilst data practitioners may specialise in data engineering when working on very advanced large-scale projects, they’ll usually work with all manner of processes and tasks within the field of data science.
The core skills required are:
- Unit Testing & good software engineering practices.
- Docker + Kubernetes
General Data Engineers
Data engineers in smaller-scale operations or teams will typically need to work at any necessary stage of the ‘data science hierarchy of needs’. These ‘generalist’ data practitioners have eclectic skill sets and may perform analysis and model training as well as data engineering.
This isn’t to say that they don’t have their own specialisms, but that they are well-versed in both data architecture and analysis, modelling, etc.
Data Pipeline Specialists
In the case of highly complex data systems, data pipeline specialists may work with advanced serverless data streaming applications. Pipeline specialists will know how to network many data systems together so they can be queried by predictive models in real-time.
Larger data projects may require many huge-volume databases and data lakes. Maintaining these databases may be the responsibility of someone who is particularly well-versed in database management languages such as SQL and NoSQL.
Top Skills for Data Engineering
Data scientists will often work with SQL, Python and R. SQL is fundamental, used for querying and managing databases – a real staple of data management. Python is much more flexible and will be used to create pipelines and write various scripts for parsing and data transformation.
Python is also integral to data processing via machine learning and constructing predictive models. R is also used by some data engineers, particularly when creating visualisations and dashboards for analysis and assessment by clients and other data practitioners.
Database and Data Lake Skills
Data engineering services include managing databases including older relational RDBMS databases based in SQL and NoSQL as well as others like MongoDB, Cassandra and Couchbase. Databases can now be placed in the cloud using services such as AWS and Google Web Services.
Data lakes differ to databases in that they can contain vast quantities of unstructured data upon which models can be trained. AI can be programmed to do much of the structuring legwork.
ETL and ELT
ETL (extract, transform, load) and ELT (extract, transform, load) are two different methods for moving and transforming data. ETL transforms data prior to loading into warehouses, data lakes or applications, whereas ELT lets the target transform the data.
REST API Development
Data engineers need to know how data systems communicate with each other. This includes knowledge of HTTP, REST protocols and how to create REStful or GraphQL APIs.
Bridging gaps between seemingly disconnected data networks and systems is one of the many challenges faced by a data engineer.
By working with machine learning models, data engineers can clean huge datasets with exceptional accuracy. Machine learning models are able to quickly and efficiently work with vast volumes of data, converting it into clean data. Data engineers are more likely to be involved in the stages before model development.
Modern data science requires an eclectic mix of personnel and data engineers are an important part of the puzzle that tend to focus on securing the bottom line of any data project. Data engineers work closely with the data itself and tend to be more distanced from the product/client-side.
Of course, data engineers, data analysts and data scientists all share a great deal of crossover and are likely adept in many different subsections of the wonderful world of data science!
What Is A Data Engineer FAQ
What Is The Difference Between a Data Engineer and Data Scientist?
A data engineer can be a data scientist and vice versa, so there is not necessarily any tangible difference. However, data engineering works very closely with the data itself, and the ways it is collected, moved, transformed and stored. It’s primarily concerned with data architecture and storage rather than insight, analysis and modelling. Data science is the whole sphere of working with data.
What is The Difference Between Data Engineering and Analysis?
Data engineering consultants solve the problem of collecting data and transforming it into something useful. For example, say a hotel business still collects vast volumes of human-input data, compiled into spreadsheets.
A data engineer will have to look at this and work out how to transform it into something clean and usable, thus eliminating all human error, missing values in the data, etc. This data will then need to be stored in such a way that it can be streamed to other areas of the business, e.g. customer service or customer relationship management (CRM).
Data analysis focuses more on analysing the data for patterns and insights. The analysis may find that certain customers (e.g. business travellers), tend to book certain hotels. This insight can then be used to build a predictive model that works out whether or not someone is a business traveller, in turn, marketing suitable matched accommodation to their profile.