Data is everywhere. But it can be challenging to understand the stories the data tells.
From 2020 to 2022, the total enterprise data volume will go from approximately one petabyte (PB) to 2.02 petabytes. That's a 42% annual growth over two years—and that's just the average!
That means your company is producing a lot of data, and if you want to know what stories it tells about your business, you need the right tools and people to make sense of it.
Data engineering helps businesses understand their company's operations by turning raw numbers into meaningful information. It's a unique skill set that requires both technical knowledge and business acumen, and it's also one of the fastest-growing job titles in today's workforce!
What Is Data Engineering?
Data Engineering is the process of organizing, managing, and analyzing large amounts of data. It's a key component in the world of data science, but it can be used by anyone who has to deal with big data regularly.
Data engineering is about collecting, storing, and processing data. It involves everything from planning to keep your data for long-term use (so you don't lose it) to finding ways to ensure your servers can handle all the new information you're collecting.
Why Is Data Engineering Important?
The field of Data Engineering is constantly changing as technology advances, and we continue to learn more about how humans interact with their environment. As such, there are many different types of jobs within this field.
Data engineering is necessary because it allows you to collect, store, and manipulate data in ways that make it possible for your business to function.
Data engineering is essential for several reasons:
- Firstly, it allows for scalability. Processing large amounts of data without overloading systems.
- It also allows for robustness, preventing errors from occurring when working on large amounts of data.
- Finally, it allows for efficiency, the ability to make sure that your system is optimized so that it can handle as much volume as possible while still being cost-effective.
Companies with good data engineering practices can use their data to make better decisions and get a leg up on their competitors. Data engineering is also necessary because it helps companies organize themselves more efficiently.
What Do Data Engineers Do?
Data engineers are the people who make the data world go round. They design and build databases, develop data models and pipelines, and optimize those systems to ensure they're fast and efficient.
They are the ones who ensure that data scientists have all the tools they need to do their work, that applications can access all the relevant data, and that business leaders have access to information that helps them make decisions.
Data engineers may be involved in any number of projects:
- Upgrading existing databases to creating new ones from scratch,
- Building systems that manage massive volumes of customer data to optimize smaller, more focused systems for a particular department or project.
In addition to technical skills like SQL database design and programming languages like Python or R, they need communication skills to work across departments and understand what their business leaders want from their data.
Data engineers are the people who make sure your company's data is accessible and usable. They're the ones who create the algorithms that make sure you can get to your data quickly and easily. They work with business stakeholders to ensure everyone is on the same page regarding accessing and utilizing data.
A data engineer may also be responsible for building dashboards or reports that show how your business performs over time. These visualizations help you understand if there are any issues with your operations or products and allow you to take action before things get out of hand.
Data engineers usually work at larger organizations where multiple teams of analysts or scientists can help them understand what different datasets mean for the company's overall strategy.
In smaller companies, a single person might take on both roles—that person would then be responsible for communicating with other employees about what each dataset means for their individual departments' objectives and overall business goals.
Why Does Data Need Processing through Data Engineering?
Data engineering is the art of designing and managing complex data ecosystems. It’s not just about finding new ways to extract value from your existing data sets. It’s about finding ways to ensure that your business can continue generating value from its growing data supply for years to come.
Data engineers work hard to ensure your team has access to the information they need to make decisions based on facts, not gut instincts or assumptions. They help you collect and cleanse raw data, transform it into practical formats, and deliver it directly to those who need it most.
They do this by crafting data warehouse schemas with table structures and indexes designed to process queries quickly, and they do it well.
Businesses often store their data in data lakes, where it’s difficult to derive value from it. Data engineers must spend time structuring and formatting that data before the business can use it.
As businesses generate data constantly, it’s vital to find software that automates some
processes so your team can concentrate on delivering valuable insight to customers.
Data Engineering vs. Data Science
Data engineers and data scientists are two different types of professionals that work together to bring a company's goals to life.
The role of the data scientist is to discover insights from massive amounts of structured and unstructured data that can be used to shape or meet specific business needs and goals. The role of the data engineer is to develop, test, and maintain data pipelines and architectures.
Data scientists work with large amounts of information to find patterns, trends, and other insights to help them achieve their professional goals. Data engineers are responsible for developing ways to collect, store, transform, secure, access, analyze, visualize and interpret large amounts of data quickly and efficiently so that others can use it within an organization.
Data engineers often have computer science or engineering degrees, while many data scientists have statistics or computer science degrees.
Data Engineering vs. Data Architect
Data engineers and data architects are crucial to the success of a business. They work together to create an enterprise data management framework that will enable the company to store, manage, and access its data in the most efficient way possible.
Data architects envision an organization's enterprise data management framework and define standards and principles for data the business uses. They design the processes and systems through which an organization manages its data.
Data engineers work with the data architect to create that vision, building and maintaining the data systems specified by the data architect’s data framework.
Data Engineering Skills
Data engineers are the wizards of data. They use a variety of tools and technologies to move data around.
Data engineers work with ETL tools, SQL, Python, cloud data storage, and query engines.
- ETL is short for Extract Transform Load. It is a software tool that efficiently moves or copies data from one database to another.
- SQL stands for Structured Query Language and is the standard language for querying relational databases.
- Python is an interpreted high-level programming language for general-purpose programming. It has been widely used for rapid application and web development, primarily replacing Perl.
- Cloud Data Storage is a type of data storage used to keep your data in the cloud rather than locally on your device or computer. It means you can access your files anytime, anywhere, without worrying about losing them due to hardware failure or theft of your valuable information by hackers who want access to sensitive information such as credit card numbers, etc.
- Query Engines are software programs that process queries against relational databases such as MySQL or Oracle.
Data Engineering Trends
AI-Driven Development
Artificial intelligence is increasingly being used to automate manual labor and repetitive tasks, which is excellent news for data engineering.
With this in mind, AI can be used by data engineers to take care of repetitive tasks in the field of quality assurance. It will allow them to focus more on their core competencies, such as software development and problem-solving.
In addition to automating repetitive tasks, Data engineers can train AI in coding with behavior-driven and test-driven development techniques. It will allow data engineers to focus on other aspects of their job while ensuring that their code is up to standard.
Software Development
Data Engineers are the new rockstars of the software engineering world. They use many of the same tools software engineers use for different purposes.
They also have to deal with the same challenges as software engineers, like building and executing data pipelines.
The most significant difference between Data Engineers and software engineers is that Data Engineers specialize in working with data. That means they are often responsible for collecting and storing data from various sources, web protocols like HTTP/3, or blockchain technology.
They work with internal and external systems to collect data, which can be used to create new products or services and improve existing ones.
Data Engineering Automation
Data engineering is a booming industry, but it still needs to catch up with the pace of change in the data landscape.
Agile Data Engineering tools are emerging to address the repetitive tasks that make up the data pipeline. These tools automate much of what was done manually so that data scientists can focus on solving problems using automation and machine learning instead of spending most of their time on repetitive tasks.
DataOps tools also help with this process by automating DevOps practices like automation, continuous delivery, and agile development. The goal is to improve agility and reduce defects, ultimately increasing productivity across the whole organization.
FAQs
1. What does a data engineer do?
Data engineers are responsible for turning raw data into information that an organization can understand and use. Their work involves blending, testing, and optimizing data from numerous sources.
2. What is Data Engineering, with example?
A data engineer's job is to make data more valuable and accessible for consumers of that data by sourcing, transforming, and analyzing the data from each system. For example, data stored in a relational database is managed as tables, like a Microsoft Excel spreadsheet.
3. What are data engineering skills?
Big data engineers are responsible for building data infrastructures and must have hands-on exposure to big data frameworks and databases, such as Hadoop and Cloudera. They also need knowledge of tools such as Scala, HPCC, Storm, Rapidminer, SPSS, SAS, Excel, R, Python, and more.
4. Is Data Engineering the same as ETL?
As data engineers are experts at making data ready for consumption, they are also involved in Extract, Transform, and Load (ETL), which is part of the data engineering process.
5. Do data engineer code?
Employers often seek out candidates who have experience coding. Candidates with basic programming knowledge in languages like Python have an advantage over other applicants.
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Conclusion
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