Published: 31 May, 2024
Contributors: Ivan Belcic, Cole Stryker
Data engineering is the practice of designing and building systems for the aggregation, storage and analysis of data at scale. Data engineers empower organizations to get insights in real time from large datasets.
From social media and marketing metrics to employee performance statistics and trend forecasts, enterprises have all the data they need to compile a holistic view of their operations. Data engineers transform massive quantities of data into valuable strategic findings.
With proper data engineering, stakeholders across an organization—executives, developers, data scientists and business intelligence (BI) analysts—can access the datasets they need at any time in a manner that is reliable, convenient and secure.
Organizations have access to more data—and more data types—than ever before. Every bit of data can potentially inform a crucial business decision. Data engineers govern data management for downstream use including analysis, forecasting or machine learning.
As specialized computer scientists, data engineers excel at creating and deploying algorithms, data pipelines and workflows that sort raw data into ready-to-use datasets. Data engineering is an integral component of the modern data platform and makes it possible for businesses to analyze and apply the data they receive, regardless of the data source or format.
Even under a decentralized data mesh management system, a core team of data engineers is still responsible for overall infrastructure health.
In this guide, we share 10 strategies for how to build a data pipeline plan, drawn from dozens of years of our own team’s experiences.
Data Engineering Foundations Course
Data engineers have a range of day-to-day responsibilities. Here are several key use cases for data engineering:
Data engineers streamline data intake and storage across an organization for convenient access and analysis. This facilitates scalability by storing data efficiently and establishing processes to manage it in a way that is easy to maintain as a business grows. The field of DataOps automates data management and is made possible by the work of data engineers.
With the right data pipelines in place, businesses can automate the processes of collecting, cleaning and formatting data for use in data analytics. When vast quantities of usable data are accessible from one location, data analysts can easily find the information they need to help business leaders learn and make key strategic decisions.
The solutions that data engineers create set the stage for real-time learning as data flows into data models that serve as living representations of an organization's status at any given moment.
Machine learning (ML) uses vast reams of data to train artificial intelligence (AI) models and improve their accuracy. From the product recommendation services seen in many e-commerce platforms to the fast-growing field of generative AI, ML algorithms are in widespread use. Machine learning engineers rely on data pipelines to transport data from the point at which it is collected to the models that consume it for training.
Data engineers build systems that convert mass quantities of raw data into usable core data sets containing the essential data their colleagues need. Otherwise, it would be extremely difficult for end users to access and interpret the data spread across an enterprise's operational systems.
Core data sets are tailored to a specific downstream use case and designed to convey all the required data in a usable format with no superfluous information. The three pillars of a strong core data set are:
The Data-as-a-Product (DaaP) method of data management emphasizes serving end users with accessible, reliable data. Analysts, scientists, managers and other business leaders should encounter as few obstacles as possible when accessing and interpreting data.
Good data isn't just a snapshot of the present—it provides context by conveying change over time. Strong core data sets will showcase historical trends and give perspective to inform more strategic decision-making.
Data integration is the practice of aggregating data from across an enterprise into a unified dataset and is one of the primary responsibilities of the data engineering role. Data engineers make it possible for end users to combine data from disparate sources as required by their work.
Data engineering governs the design and creation of the data pipelines that convert raw, unstructured data into unified datasets that preserve data quality and reliability.
Data pipelines form the backbone of a well-functioning data infrastructure and are informed by the data architecture requirements of the business they serve. Data observability is the practice by which data engineers monitor their pipelines to ensure that end users receive reliable data.
The data integration pipeline contains three key phases:
Data ingestion is the movement of data from various sources into a single ecosystem. These sources can include databases, cloud computing platforms such as Amazon Web Services (AWS), IoT devices, data lakes and warehouses, websites and other customer touchpoints. Data engineers use APIs to connect many of these data points into their pipelines.
Each data source stores and formats data in a specific way, which may be structured or unstructured. While structured data is already formatted for efficient access, unstructured data is not. Through data ingestion, the data is unified into an organized data system ready for further refinement.
Data transformation prepares the ingested data for end users such as executives or machine learning engineers. It is a hygiene exercise that finds and corrects errors, removes duplicate entries and normalizes data for greater data reliability. Then, the data is converted into the format required by the end user.
Once the data has been collected and processed, it’s delivered to the end user. Real-time data modeling and visualization, machine learning datasets and automated reporting systems are all examples of common data serving methods.
Data engineering, data science, and data analytics are closely related fields. However, each is a focused discipline filling a unique role within a larger enterprise. These three roles work together to ensure that organizations can make the most of their data.
- Data scientists use machine learning, data exploration and other academic fields to predict future outcomes. Data science is an interdisciplinary field focused on making accurate predictions through algorithms and statistical models. Like data engineering, data science is a code-heavy role requiring an extensive programming background.
Data analysts examine large datasets to identify trends and extract insights to help organizations make data-driven decisions today. While data scientists apply advanced computational techniques to manipulate data, data analysts work with predefined datasets to uncover critical information and draw meaningful conclusions.
Data engineers are software engineers who build and maintain an enterprise’s data infrastructure—automating data integration, creating efficient data storage models and enhancing data quality via pipeline observability. Data scientists and analysts rely on data engineers to provide them with the reliable, high-quality data they need for their work.
The data engineering role is defined by its specialized skill set. Data engineers must be proficient with numerous tools and technologies to optimize the flow, storage, management and quality of data across an organization.
When building a pipeline, a data engineer automates the data integration process with scripts—lines of code that perform repetitive tasks. Depending on their organization's needs, data engineers construct pipelines in one of two formats: ETL or ELT.
ETL: extract, transform, load
ETL pipelines automate the retrieval and storage of data in a database. The raw data is extracted from the source, transformed into a standardized format by scripts and then loaded into a storage destination. ETL is the most commonly used data integration method, especially when combining data from multiple sources into a unified format.
ELT: extract, load, transform
ELT pipelines extract raw data and import it into a centralized repository before standardizing it through transformation. The collected data can later be formatted as needed on a per use basis, offering a higher degree of flexibility than ELT pipelines.
The systems that data engineers create often begin and end with data storage solutions: harvesting data from one location, processing it and then depositing it elsewhere at the end of the pipeline.
Cloud computing services
Proficiency with cloud computing platforms is essential for a successful career in data engineering. Microsoft Azure Data Lake Storage, Amazon S3 and other AWS solutions, Google Cloud and IBM Cloud® are all popular platforms.
Relational databases
A relational database organizes data according to a system of predefined relationships. The data is arranged into rows and columns that form a table conveying the relationships between the data points. This structure allows even complex queries to be performed efficiently.
Analysts and engineers maintain these databases with relational database management systems (RDBMS). Most RDBMS solutions use SQL for handling queries, with MySQL and PostgreSQL as two of the leading open source RDBMS options.
NoSQL databases
SQL isn’t the only option for database management. NoSQL databases enable data engineers to build data storage solutions without relying on traditional models. Since NoSQL databases don’t store data in predefined tables, they allow users to work more intuitively without as much advance planning. NoSQL offers more flexibility along with easier horizontal scalability when compared to SQL-based relational databases.
Data warehouses
Data warehouses collect and standardize data from across an enterprise to establish a single source of truth. Most data warehouses consist of a three-tiered structure: a bottom tier storing the data, a middle tier enabling fast queries and a user-facing top tier. While traditional data warehousing models only support structured data, modern solutions can store unstructured data.
By aggregating data and powering fast queries in real-time, data warehouses enhance data quality, provide quicker business insights and enable strategic data-driven decisions. Data analysts can access all the data they need from a single interface and benefit from real-time data modeling and visualization.
Data lakes
While a data warehouse emphasizes structure, a data lake is more of a freeform data management solution that stores large quantities of both structured and unstructured data. Lakes are more flexible in use and more affordable to build than data warehouses as they lack the requirement for predefined schema.
Data lakes house new, raw data, especially the unstructured big data ideal for training machine learning systems. But without sufficient management, data lakes can easily become data swamps: messy hoards of data too convoluted to navigate.
Many data lakes are built on the Hadoop product ecosystem, including real-time data processing solutions such as Apache Spark and Kafka.
Data lakehouses
Data lakehouses are the next stage in data management. They mitigate the weaknesses of both the warehouse and lake models. Lakehouses blend the cost optimization of lakes with the structure and superior management of the warehouse to meet the demands of machine learning, data science and BI applications.
As a computer science discipline, data engineering requires an in-depth knowledge of various programming languages. Data engineers use programming languages to construct their data pipelines.
SQL, or structured querying language, is the predominant database creation and manipulation programming language. It forms the basis for all relational databases and may be used in NoSQL databases as well.
Python offers a wide range of prebuilt modules to speed up many aspects of the data engineering process, from building complex pipelines with Luigi to managing workflows with Apache Airflow. Many user-facing software applications use Python as their foundation.
Scala is a good choice for use with big data as it meshes well with Apache Spark. Unlike Python, Scala permits developers to program multiple concurrency primitives and simultaneously execute several tasks. This parallel processing ability makes Scala a popular choice for pipeline construction.
Java is a popular choice for the backend of many data engineering pipelines. When organizations opt to build their own in-house data processing solutions, Java is often the programming language of choice. It also underpins Apache Hive, an analytics-focused warehouse tool.
IBM Databand is observability software for data pipelines and warehouses that automatically collects metadata to build historical baselines, detect anomalies and triage alerts to remediate data quality issues.
Solve inefficient data-generation and processing problems and improve poor data quality caused by errors and inconsistencies with IBM DataOps platforms.
IBM Cloud Pak for Data is a modular set of integrated software components for data analysis, organization and management. It is available for self-hosting or as a managed service on IBM Cloud.
Even if you’re on the data team, keeping track of all the different roles and their nuances can get confusing—let alone if you’re a non-technical executive who’s supporting or working with the team. One of the biggest areas of confusion is understanding the differences between data engineer, data scientist and analytics engineer roles.
Data integration stands as a critical first step in constructing any artificial intelligence (AI) application. While various methods exist for starting this process, organizations accelerate the application development and deployment process through data virtualization.
The emergence of generative AI prompted several prominent companies to restrict its use because of the mishandling of sensitive internal data. According to CNN, some companies imposed internal bans on generative AI tools while they seek to better understand the technology and many have also blocked the use of internal ChatGPT.