Data Engineering: A Guide to the Who, What, and How
In the modern world, it is tough to think of any industry that has not been revolutionised by data science. Although many may not understand the intricacies of the data science discipline, they have enough exposure to know that data science is a growing field. People open their email to find personalised discounts, turn to Siri for immediate answers to their questions, and depend on their bank to identify and mitigate any potential fraud activity.
While we are enjoying the fruits of data science’s labour, there are other players working diligently behind the scenes. These employees are responsible for creating the data pipelines and warehouses that enable data scientists to write and optimise algorithms in order to enhance our everyday lives.
Who are these supporting actors? Data engineers.
What is data engineering?
Conclusions drawn from big datasets are only as valuable as its data integrity. Without an architecture that can structure and format growing and changing datasets, data scientists are unable to make accurate predictions. This is where data engineering comes into play.
Data Engineering is the act of collecting, translating, and validating data for analysis. In particular, data engineers build data warehouses to empower data-driven decisions. Data engineering lays the foundation for real-world data science application. Working harmoniously, data engineers and data scientists can deliver consistently valuable insights.
Required data engineering skills and responsibilities
Data engineering requires a broad set of skills ranging from programming to database design and system architecture. Here are just a few:
- Extensive experience with data processing and ETL/ELT techniques
- Knowledge of Python, SQL, and Linux
- A deep understanding of cluster management, data visualisation, batch processing, and machine learning
- Aptitude for developing a foundational understanding of company data
- Proven ability to institute appropriate architecture and establish sustainable pipeline management
- Proficiency in report and dashboard creation
Data engineers are focused on providing the right kind of data at the right time. A good data engineer will anticipate data scientists’ questions and how they might want to present data. Data engineers ensure that the most pertinent data is reliable, transformed, and ready to use. This is a difficult feat, as most organisations rarely gather clean raw data.
To work their magic, most data engineers must be proficient in Python, SQL, and Linux. Data engineers may also need skills in cluster management, data visualisation, batch processing, and machine learning. Data engineers use these processing techniques to massage data into a format that facilitates hundreds of queries.
While data engineers may not be directly involved in data analysis, they must have a baseline understanding of company data to set up appropriate architecture. Creating the best system architecture depends on a data engineer’s ability to shape and maintain data pipelines. Experienced data engineers might blend multiple big data processing technologies to meet a company’s overarching data needs.
Data engineer vs. data scientist: What’s the difference?
Although data engineers and data scientists are tied together closely when working in a company, these two roles differ greatly in skillset and job function.
Data engineers concentrate on production readiness. They prepare and manage data for data scientists’ use. Overall, data engineers care most about how company data is presented, how it scales, how secure it is, and how easy it is to change data pipelines based on new information.
As a result, data engineers typically have an extensive knowledge of data storage and transformation tools. With a solid foundation in ETL design, data modelling, relational and non-relational database design, and query execution, data engineers have the ability to choose the technique most suitable to handle each dataset.
Data scientists, on the other hand, mine prepared data for valuable insights. Using data formatted by data engineers, data scientists develop algorithms that surface underlying issues or business opportunities. As you might expect, data scientists have familiarity with analytics programming languages like SQL and Python.
Data scientists work closely with data engineers to adjust their algorithms. Data engineers can point out data limitations to help data scientists better account for variables and draw more meaningful conclusions.
Data engineering tools and solutions for your business
Clearly data engineers have a comprehensive idea of how data can be stored, processed, and delivered. But how do they begin to put this knowledge to use?
First, data engineers construct a data warehouse. The tried and true process that data engineers use is called ETL — Extract, Transform, Load. The best ETL tools often include automated alerts when there are errors in a pipeline and permit the use of open-source code.
Recently, some data engineers have switched two steps in the ETL process, forming a new, “ELT” method. When loading data occurs before data transformation, all data is accessible at any time. With the ever-increasing data pool and the availability of cloud storage, this method is becoming very popular. For this reason, data engineering tools that support ETL or ELT processes are critical. ELT tools should be cloud-based solutions and offer end-to-end support to stay abreast of new web-based data streams and afford extreme flexibility.
The cloud and the future of data engineering
The cloud has most certainly led to the need for data engineering. Agile businesses require the efficiency, organisation, and speed that comes with proper data engineering.
In the future, data engineering will only become more relevant. Companies are beginning to understand the extreme advantage of big data and are investing in data science initiatives. Data engineering will follow suit, as data science relies on sustainable, standardised data.
In fact, the data science field is starting to establish sub-disciplines, like: visualisation, machine learning, and data storytelling. Artificial intelligence and neural networks are becoming especially popular in fields like healthcare, climate change, and finance. All of these strategies necessitate the clean, transformed data that data engineers provide.
Lastly, many people are fearful of data ethics and privacy. With so much data available, companies will be placing an increased emphasis on strict security measures. Information security is a major component of data engineering. Individuals, companies, and governments will all need to rely on competent data engineers to keep their data safe.
Getting started with data engineering
Data analysis is critically important in this day and age. Companies once struggling to keep up with the vast amounts of data they collect have benefited greatly from data engineering. With innovative data engineering, data scientists have the power to offer invaluable insights that could disrupt entire industries.
Without the right software and structure, data scientists would yield different results from the same research question, end users could experience outages, or pipelines may malfunction causing data scientists to spend hours on repetitive, manual deep dives. Companies need a cloud-based ETL/ELT solution with ample data storage and self-service capabilities.
Talend Data Fabric offers a single suite of apps that store, govern, transform, and share data — making data monitoring and ETL/ELT management a breeze. Talend Data Fabric is simple for data engineers to use, and can scale with advanced functionality as companies invest in their data teams. Get ready to disrupt your industry today with Talend Data Fabric.