While many recognize the value of their business data, few realize the importance of data engineering in making that data usable.
Data engineers do all the behind-the-scenes work to design, build, and maintain the infrastructure organizations need to collect and store their data.
An essential part of data engineering services is building and maintaining data pipelines. These conduits bring data from different sources to a central repository, such as a data warehouse, data lake, data lakehouse, or data mart.
Data engineers also do the processing that allows data to be rapidly accessed and analyzed. They integrate, consolidate, clean, and structure data so it can be used in analytics applications.
They are also responsible for an organization’s data security and integrity.
This article explores the complex role of the data engineer in order to highlight the full importance of data engineering to businesses.
Types of Data Engineers
All data engineers are not the same.
However, the differences between the types of data engineers depend on who you ask.
Daniel Beach is a senior data engineer who writes on the topic in his blog Confessions of a Data Guy. In his opinion, there are three categories of data engineers. What differentiates them is where they are in their career’s evolution.
Category 1 data engineers focus on the database/data warehouse and analytics/metrics/dashboards. Category 2 engineers do all that plus add Python and Airflow/dbt (etc.). Category 3 add Scala/Java, distributed systems, architecture, Big Data, and machine language to everything in Categories 1 and 2.
That said, Beach noted, “I’ve seen my fair share of Category 3 engineers who skipped Category 1 and lack the very foundational skills to be able to actually produce usable data.” (He later said on LinkedIn that the post made many people mad.)
In our Complete Guide to Data Engineering, we separated the three types of data engineers into generalists, pipeline-centric engineers, and database-centric engineers.
Generalists perform end-to-end data collection, intake, and processing. They set up and manage data sources, systems, and tools, making sure everything works smoothly and securely.
Generalists are proficient in software engineering, cloud computing, distributed systems, and DevOps practices. They also know different data technologies and frameworks, such as Airflow, AWS, Azure, Hadoop, Kafka, Spark, and others.
Pipeline-centric engineers know how the organization’s data is generated and what it means. They usually handle complicated data science projects across distributed systems, using the infrastructure and tools built by generalists.
Pipeline-centric data engineers excel at data modeling; ETL/ELT; SQL; Python, Scala, or Java; and performance tuning. They interact with the stakeholders, data analysts, and data scientists to understand what those roles need and expect.
Database-centric engineers specialize in designing and managing databases. They work with both relational and NoSQL databases, ensuring optimal performance, scalability, and security. Businesses that have data distributed across multiple databases often need engineers who can focus on database issues.
Breaking it down further, we have the following specialists:
- ETL developers specialize in designing, developing, and maintaining Extract, Transform, Load (ETL) processes. They are responsible for extracting data from source systems, transforming it to meet business requirements, and loading it into target systems.
- Big Data engineers work with technologies and frameworks designed for handling and processing large volumes of data. This may include distributed computing frameworks like Apache Hadoop and Apache Spark, as well as big data storage solutions.
- Data warehouse engineers concentrate on designing, building, and maintaining data warehouses. They are involved in structuring data for efficient querying and reporting, often using technologies like Amazon Redshift, Google BigQuery, or Snowflake.
- Streaming data engineers specialize in real-time data processing. They work with technologies like Apache Kafka or Apache Flink to process and analyze data as it is generated, enabling organizations to make decisions in near real-time.
- Data integration engineers work on integrating data from various sources to create a unified and coherent view for analysis. They may use tools like Apache NiFi or Talend for data integration.
- Data quality engineers are concerned with ensuring the accuracy, consistency, and reliability of data. They design and implement processes for data validation, cleansing, and governance.
- Data modelers focus on designing the structure of databases and data warehouses. They create conceptual, logical, and physical data models to represent how data is organized and related.
- Cloud data engineers specialize in building and managing data solutions on cloud platforms such as AWS, Azure, or Google Cloud. They leverage cloud-based services for storage, computation, and data processing.
- Machine learning data engineers work at the intersection of data engineering and machine learning. They build data pipelines that feed into machine learning models, ensuring the availability and quality of training data.
- DataOps engineers apply DevOps principles to data engineering processes, emphasizing collaboration, automation, and continuous delivery in data-related workflows.
The lines between these specialists are often fuzzy, and their responsibilities can overlap. Data engineers may wear multiple hats depending on the size and structure of the organization.
Data Engineer Responsibilities
Regardless of type, data engineers perform similar data engineering services.
Data Collection and Ingestion
Data engineers are responsible for collecting and ingesting data from various sources. It involves understanding the data sources, such as databases, APIs, streaming data, and more. Accurate and reliable data is essential for meaningful analysis. Data engineers ensure that the data is collected efficiently and in a format suitable for analysis.
Data Storage and Management
Data engineers design and implement storage solutions to store large volumes of data. They choose appropriate databases, data lakes, or other storage systems. Efficient data storage and management are critical for quick and easy access to data. Data engineers ensure that data is organized, secure, and easily retrievable.
Data Processing and Transformation
Data engineers develop processes for cleaning, transforming, and aggregating raw data into a format suitable for analysis. This may involve handling missing data, dealing with outliers, and ensuring data quality. Clean and well-structured data is essential for accurate analysis. Data engineers prepare the data so that data scientists can focus on modeling and analysis rather than data cleaning.
Data engineers integrate data from different sources to create a unified and comprehensive view. It can involve merging datasets, resolving inconsistencies, and ensuring data compatibility. Integrated data provides a holistic understanding of the subject and allows for more robust analysis and modeling.
Pipeline Maintenance and Monitoring
Data engineers are responsible for maintaining and monitoring data pipelines. It includes identifying and addressing issues to ensure the continuous flow of data. Reliable and well-monitored pipelines minimize downtime and ensure that data is always available for analysis.
Scalability and Performance
Data engineers design systems that can scale to handle increasing data volumes and maintain optimal performance. As data volumes grow, scalable systems are necessary to handle the load efficiently and prevent bottlenecks in the data pipeline.
Collaboration with Data Scientists
Data engineers collaborate closely with data scientists to understand their requirements and ensure that the infrastructure supports advanced analytics and machine learning models. Effective communication between data engineers and data scientists is essential for a seamless data science workflow.
The Importance of Data Engineering
According to Venture Beat, only 13 percent of data science projects make it to production.
There are several causes for these failures. A big one is that organizations lack the ability to handle data acquisition or convert large volumes of raw data into a usable format.
With the explosion of artificial intelligence, companies have a wealth of new ways to collect data—and new types of data they may not have been able to collect before. And that could easily exacerbate the problem.
The growing flood of information will only increase the importance of data engineering. Without the architecture built by data engineers, BI analysts and data scientists can’t draw meaningful insights or even access the data.
Data engineers allow organizations to leverage one of their most valuable resources. And that makes them indispensable.