The digital transformation journey of companies comes with its challenges of leveraging the massive amount of data at their disposal to extract insights for business decisions. These insights help to understand trends and patterns for actionable information. The end-to-end automation of analytical processes means companies must use tools and technologies within the digitized ecosystem for analytics and visualization. And it is Data Engineers who develop and maintain IT systems and databases for extraction and modeling. As the various formats of raw data cannot be used without cleaning and transforming to make it usable, it has to go through the stages of Extract, Transform, Load (ETL), undertaken by a specialized engineer, the ETL Developer.
With Analytics and Data Science applications increasing across industries and use cases, the study of Data Engineering becomes attractive. A Data Engineering Course helps understand the database pipelines that form the mainstay of a company’s IT infrastructure.
Let us begin with understanding what a Data Engineer is all about and how it differs from an ETL Engineer.
What is a Data Engineer?
As the term suggests, the Data Engineer “engineers” data by transforming raw data into a readable format for processing and analysis. Data Engineers play a critical role in the digital transformation journey of companies by collecting, transforming, managing, and storing data for retrieval and distribution in the organization. They create data pipelines that transform raw data into usable formats for analytical and machine-learning models.
Data engineers design, build and optimize systems for data collection, storage, data access, and analytics at scale. They create data pipelines and build data-centric applications. Their responsibilities include designing, building, testing, and managing the data using various tools, technologies, and software engineering techniques for cleaning and normalizing data.
Who is an ETL Developer?
ETL is the method of moving data from various sources and in multiple formats into a data warehouse. An ETL developer designs and builds data storage systems across the Extract, Transform, and Load lifecycle of data transformation.
ETL Developers EXTRACT data from different Relational Database Management Systems (RDBMS) to build a relational model, then TRANSFORM the disparate data to LOAD it into a target database system. They use designing and programming skills to create the database environment. ETL Developers also test performance and troubleshoot problems before the data goes live for analysis.
Difference between Data Engineer and ETL Developer
ETL Developer performs tasks that involve moving data from a source to a target database, a function that Data Engineers do. So as an ETL Developer, you can take the next step to become a Data Engineer with a certification.
Here are some key differences between the two:
The Data Engineer can be a Generalist who works in small companies wearing many hats, a Pipeline-centric Data Engineer working with Data Scientists in mid-sized companies, or a Database-centric Data Engineer working with data warehouses in large organizations.
ETL Developer is a developer or programmer who uses programming languages and algorithms to design data storage systems, manages the uploading of big data into data warehousing software, and tests the performance or troubleshooting before the data goes live for analysis or modeling.
Job titles can be Data Engineer, Data Architect, Data Analyst, Data Scientist, Business Analyst, Database/Warehouse Developer, BI Developer or Database Administrator. Some job titles for a wannabe ETL Developer are ETL Developer, ETL Consultant, BI Developer, Data Warehouse ETL Developer, SQL Developer, AWS Spark ETL Developer, and so on.
The Data Engineer sources data sets to filter data and data formats and processes the same in batches or streams. Other responsibilities include data storage based on the type of access or query, allowing for scalability, containerizing data for movement or deployment, caching data in memory, training and scaling models using machine learning frameworks, tracking and testing data pipelines, and so on.
A Data Engineer may also perform managerial duties, leading teams and assigning projects to ETL developers. The ETL developer works more on writing code and using tools like SQL Server and Oracle.
Data Engineers are part of larger teams, whereas ETL Developers work more independently on the programming side.
Tech-first and large data-guzzling companies look for Data Engineers, while traditional data-driven firms hire ETL Developers. The difference between the two job roles lies in the business model: Data Engineers are found in companies powered by a software product, and ETL Developers work in businesses that use the software. Data Engineers can expect a career in a large enterprise, whereas ETL Developers are part of traditional business environments or any company that works with big data and business intelligence. As an ETL developer, you may apply to a single company or work as a consultant.
Requirements: Educational Background and Experience
Data Engineers must have a background in computer science, applied mathematics, engineering, or other related disciplines. Only a few dedicated certifications are available, so if candidates cannot certify in Data Engineering, they may consider data science, data analytics, and Big Data certifications to build their skills or professional certifications from Google and AWS.
The candidate applying for an ETL Developer position must have a bachelor’s degree in Engineering or Computer Science, with a minimum of two years of experience in coding in at least one programming language and 4-6 years of experience in data warehouse development or BI, software development experience on any Microsoft BI Platform, and the experience of a Data Warehouse environment, Data Marts and Data Migration.
Requirements: Technical Skills
ETL Developers must have sound knowledge of coding languages, in particular, Java, SQL and XML. They must have expertise in warehousing architecture techniques such as MOLAP, ROLAP, EDW, ODS, and DM; and be familiar with data warehousing solutions using Microsoft SQL Server technologies. Other skills are enterprise BI technologies, dashboards, and KPI visualization on Power BI or Tableau.
They must have project management skills and the know-how to troubleshoot and solve complex technical problems.
In the years to come, we can expect an increase in Data Engineering jobs as more and more companies leverage data for insights and decision–making. With an increase in the variety of data and a growing need for analytics in real-time, traditional business models will transition from hiring ETL Developers to Data Engineers focused on software engineering skills.
At the same time, there will also be a rise in the need for ETL Developers with low-code solution experience.