Summary
The Data Engineering hub is focused on bringing together information, experts, organizations, policy makers, and the public to LEARN more about a topic, DISCUSS relevant issues, and COLLABORATE on enhancing research-driven DE knowledge and addressing DE challenges …. …. where onAir members control where and how their content and conversations are shared free from paywalls, algorithmic feeds, or intrusive ads.
The onAir Knowledge Network is a human-curated, AI-assisted network of hub websites where people share and evolve knowledge on topics of their interest.
This About the Data Engineering onAir 2 minute video is a good summary of DE hub mission and user experience.
If you or your organization would like to curate a post within this hub (e.g. a profile post on your organization), contact matthew.kovacev@onair.cc.
To become an onAir member of this hub, fill in this short form. It’s free!
Source: Other
Data with Baraa – 13/05/2025 (22:46)
OnAir Post: Data Engineering on Air
News
To grow and thrive in the rapidly evolving AI landscape, organizations must strategically invest in their data engineering capabilities.
In today’s modern digital landscape, businesses are generating heavy data daily which can be processed, analyzed and interpreted for future scalability and growth. This is when AI-driven systems become integral across industries to help create real-time analytics, forecasting and initiating AI-driven automation. Beverly D’Souza, a Data Engineer at Patreon (previously worked at Meta) has played a key role in improving data workflows, processing data at pace and launching machine learning models. Having experience with ETL pipelines, cloud data systems, and AI analytics, she shared, “Building scalable AI-powered data pipelines comes with key challenges and to overcome these obstacles, organizations must implement distributed computing frameworks that can handle large-scale data processing efficiently. Incorporating AI-driven automation helps streamline data processing tasks, making the entire system faster and more efficient.”
About
Overview
What is Data Engineering?
Data engineering involves designing, building, and maintaining systems and architectures that collect, store, and analyze large-scale data. It encompasses the creation of data pipelines to ensure data flows efficiently from source systems to data storage and analytics platforms. Data engineers extract data from various sources, transform it into a usable format, and load it into data storage solutions like data warehouses or data lakes.
Importance of Data Engineering
Data engineering ensures data reliability, accessibility, and quality, which are critical for data-driven decision-making. Robust data engineering practices enable organizations to leverage data for insights, operational efficiency, and competitive advantage.
Future Scope of Data Engineering
The future of data engineering is promising due to the increasing importance of big data, AI, and machine learning. The demand for skilled data engineers will continue to rise with the growth of data volumes. Technologies like cloud computing, real-time data processing, and advanced analytics will further expand opportunities in this field.
Source: Ronit Malhotra
Videos
How I would learn Data Engineering in 2025
May 13, 2025 (22:46)
By: Data with Baraa
00:00 – Intro
02:24 – Phase 1 Roadmap
15:10 – Phase 2 Roadmap
19:03 – Phase 3 Roadmap
DE Processes
Data Collection
In data engineering, collection refers to the systematic process of gathering data from various sources, often involving the development of systems and pipelines to extract, transform, and load data into a usable format for analysis or storage. This includes both structured and unstructured data from databases, APIs, files, and other origins.
OnAir Post: Data Collection
Data Storage
Data storage is the underlying technology that stores data through the various data engineering stages. It bridges diverse and often isolated data sources—each with its own fragmented data sets, structure, and format. Storage merges the disparate sets to offer a cohesive and consistent data view. The goal is to ensure data is reliable, available, and secure.
OnAir Post: Data Storage
Data Cleaning
Data cleaning, also known as data cleansing or scrubbing, is the process of identifying, correcting, and removing errors, inconsistencies, and inaccuracies from datasets. In data engineering, this is crucial for ensuring data quality and usability for various downstream tasks like analysis, machine learning, and business intelligence
OnAir Post: Data Cleaning
Data Transformation
Data transformation in data engineering is the process of converting raw data into a more usable format for analysis and other downstream tasks. This involves cleaning, structuring, and enriching data to make it consistent, accurate, and ready for further processing or consumption by data scientists, analysts, and other end-users. It’s a crucial step in building robust data pipelines.
OnAir Post: Data Transformation
Data Engineering & AI
Data engineering and AI are deeply intertwined. AI relies heavily on data, and data engineering provides the infrastructure and pipelines necessary to make that data accessible, clean, and usable for AI models. In turn, AI is starting to automate and enhance data engineering tasks, creating a symbiotic relationship.
The relationship between data engineering and AI is increasingly symbiotic. Data engineers are the backbone of AI, while AI is becoming a powerful tool for data engineers to enhance their work, improve efficiency, and unlock new possibilities.
OnAir Post: Data Engineering & AI
Data Delivery
Data engineering delivery is a critical aspect of the data engineering process, focusing on making processed and transformed data readily available and accessible to end-users, applications, and downstream processes. It is the final stage of the data engineering lifecycle, ensuring that the valuable data refined throughout the process is served in a structured and accessible manner to support various needs, such as analysis, reporting, and decision-making.
In simpler terms, data engineering delivery is about providing the cleaned, organized, and transformed data in a format that data consumers (like analysts, data scientists, or applications) can easily use. Think of data engineering as the “refining” process for raw data. Data engineers design and build robust data pipelines that extract, transform, and load data from various sources, preparing it for use. Data delivery is the final step where this refined data is delivered to its intended users.
OnAir Post: Data Delivery
Data Infrastructure Management
Data engineering infrastructure management is the practice of designing, building, and maintaining the systems and architectures that enable the collection, storage, processing, and delivery of data within an organization. It’s the foundation upon which data-driven insights and decision-making are built.
In essence, data engineering infrastructure management is about building the engine that powers an organization’s data capabilities. It’s a crucial function for any business that wants to leverage its data assets for competitive advantage.
OnAir Post: Infrastructure Management
Data Governance
Data governance in data engineering refers to a structured system of policies, practices, and processes that ensure data is managed effectively throughout its lifecycle. It encompasses data quality, security, access, and usability, ensuring data is reliable, consistent, and compliant with regulations.
OnAir Post: Data Governance
Data Privacy
Data engineering privacy refers to the practices and technologies used to protect the privacy of individuals when handling their data within data engineering systems. It involves ensuring that data is collected, stored, processed, and shared in a way that respects individual’s rights and complies with privacy regulations.
OnAir Post: Data Privacy
Data Security
Data engineering privacy refers to the practices and technologies used to protect the privacy of individuals when handling their data within data engineering systems. It involves ensuring that data is collected, stored, processed, and shared in a way that respects individual’s rights and complies with privacy regulations.
OnAir Post: Data Security
DE Use Cases
Data Migration
Business Intelligence
Business intelligence (BI) refers to the processes and technologies used to analyze business data and extract actionable insights to improve decision-making. It involves collecting, analyzing, and presenting data in a way that helps organizations understand their performance, identify trends, and make informed strategic and operational decisions. BI tools help transform raw data into meaningful information, often through dashboards, reports, and visualizations.
OnAir Post: Business Intelligence
AI and Machine Learning
Data engineering plays a crucial role in AI and machine learning by providing the infrastructure and systems needed to manage and process the vast amounts of data that these technologies rely on. Data engineers build and maintain the pipelines, databases, and data architectures that enable AI and ML models to learn and make predictions.
In essence, data engineering provides the raw materials (data) and the tools (pipelines, infrastructure) for AI and ML to function effectively. AI and ML, in turn, are being integrated into data engineering processes to improve efficiency, accuracy, and the overall ability to extract value from data.
OnAir Post: AI and Machine Learning
Data Science
Data science use cases in data engineering focus on building the infrastructure and pipelines that enable data-driven insights. This includes tasks like data ingestion, cleaning, transformation, and storage, as well as developing real-time analytics, fraud detection systems, and machine learning models. Data engineering provides the foundation for data scientists to perform their analysis and modeling effectively.
In essence, data engineering provides the foundational infrastructure and tools that empower data scientists to extract meaningful insights from data, enabling data-driven decision-making and innovation across various industries, according to DataCamp and IBM – United States.
OnAir Post: Data Science
E-Commerce Analytics
Data engineering in e-commerce analytics refers to the processes and systems that enable the collection, processing, and management of large volumes of data from various sources to support data-driven decision-making in online retail. It involves building and maintaining the infrastructure that allows businesses to extract valuable insights from their data, ultimately leading to improved customer experiences, optimized operations, and increased revenue.
In essence, data engineering forms the foundation for effective e-commerce analytics by providing the infrastructure and tools needed to manage, process, and analyze the vast amounts of data generated in the online retail space. This, in turn, enables businesses to gain valuable insights, optimize their operations, and ultimately drive growth and success.
OnAir Post: E-Commerce Analytics
Financial Services
Data engineering in financial services involves designing, building, and maintaining the systems and processes that manage, process, and deliver financial data for various applications like risk management, investment strategies, and regulatory compliance. It’s the critical infrastructure that enables financial institutions to leverage data for informed decision-making and innovation.
In essence, data engineering is the backbone of data-driven decision-making in the financial industry, enabling institutions to manage risk, optimize performance, and innovate in a competitive landscape.
Fraud Detection
Data engineering in fraud detection involves building and maintaining the data pipelines and infrastructure that enable the identification of fraudulent activities. This includes collecting, cleaning, storing, and processing large volumes of data from various sources to feed into fraud detection models and systems. Effective data engineering ensures the reliability, scalability, and timeliness of data used for detecting anomalies and patterns indicative of fraud.
In essence, data engineering forms the backbone of fraud detection systems, ensuring that the right data is available at the right time and in the right format to support accurate and efficient fraud identification and prevention.
OnAir Post: Fraud Detection
Manufacturing
Data engineering in manufacturing involves designing, building, and managing systems that collect, process, and store data from various sources to enable insights and optimize operations. It focuses on creating the infrastructure and pipelines that make data usable for analysis, machine learning, and other applications within the manufacturing context. Essentially, data engineers ensure that the right data is available to the right people at the right time to improve efficiency, quality, and decision-making in manufacturing processes.
OnAir Post: Manufacturing
Public Health
Data engineering in the context of Public Health Management (PHM) is the process of building and managing systems and infrastructure to collect, store, process, and analyze diverse public health data. This data comes from various sources, including:
- Electronic Health Records (EHRs): Patient medical information, diagnosis, treatments, etc.
- Public health surveillance data: Information on disease outbreaks, immunizations, vital records, etc.
- Administrative claims data: Information from insurance companies related to patient care and costs.
- Wearable devices and sensors: Real-time data on patient health metrics, especially for chronic disease management.
- Survey data: Information collected through health surveys and research studies.
In summary, data engineering in public health management is crucial for ensuring that public health initiatives are effective and efficient, leading to improved population health outcomes.
Real Time Analytics
Real-time analytics refers to the immediate analysis of data as it is generated or received, providing insights and facilitating rapid decision-making. This contrasts with traditional batch processing, where data is analyzed in delayed intervals. Real-time analytics is crucial in scenarios requiring immediate action, such as fraud detection, personalized recommendations, and operational monitoring.
OnAir Post: Real Time Analytics
DE Tools
Processing & Analytics Tools
Data processing tools are software applications that collect, clean, transform, and analyze raw data to make it usable for various purposes, such as business intelligence, data analysis, and machine learning. These tools are essential for handling large volumes of data and extracting meaningful insights.
What they do:
- Collect and ingest data
They gather data from various sources, including databases, files, and APIs.
- Clean and transform data
They handle tasks like data cleansing (removing errors, duplicates, and inconsistencies), data transformation (converting data into a usable format), and data validation.
- Analyze data
They perform various analytical operations, such as filtering, aggregating, and summarizing data, to derive insights.
- Output data:
They can output processed data in various formats for further use, such as in reports, dashboards, or machine learning models.
OnAir Post: Processing & Analytics Tools
Infrastructure & Orchestration Tools
Data orchestration and infrastructure tools are software solutions that automate, manage, and monitor complex data workflows, ensuring data flows smoothly and reliably across systems.
These tools are essential for tasks like data ingestion, transformation, loading, quality checks, and more.
Examples include Apache Airflow, Prefect, and Dagster, each offering unique features for building and managing data pipelines. Infrastructure automation and orchestration tools, like those from Gartner, focus on automating infrastructure delivery and operations across hybrid IT environments.
OnAir Post: Infrastructure & Orchestration Tools
Programming Languages
Data engineers primarily use languages like Python, SQL, Java, and Scala, along with related frameworks and tools. Python is popular for data manipulation, analysis, and scripting due to its extensive libraries. SQL is crucial for interacting with databases and data warehouses. Java is often used for building data pipelines and backend systems, especially with tools like Hadoop. Scala is a common choice for working with Spark, a popular distributed computing framework.
OnAir Post: Programming Languages
Transformation and Loading
In data engineering, transformation and loading are crucial parts of the ETL (Extract, Transform, Load) process. Transformation involves cleaning, structuring, and converting data into a usable format for analysis, while loading is the process of inserting the transformed data into a target system like a data warehouse or data lake.
These tools are often used in combination to build robust and scalable data pipelines, enabling businesses to extract, transform, and load data efficiently for analytics and other downstream processes.
OnAir Post: Transformation and Loading
Visualization & Business Intelligence
Data engineering, data visualization, and business intelligence (BI) are all related but distinct concepts in the realm of data management and analysis. Data engineering focuses on building the infrastructure for data collection, storage, and processing, while data visualization uses visual elements to represent data and insights, and business intelligence leverages these tools to provide actionable insights for decision-making.
OnAir Post: Visualization & Business Intelligence
Warehousing & Storage
Data warehousing and storage are crucial components of data engineering, focusing on different aspects of managing data for analysis and reporting. Data warehousing involves designing, building, and maintaining systems for storing and managing data, making it readily available for analysis and business intelligence. Data storage, on the other hand, is the broader concept of preserving digital information, including the physical media and infrastructure used to store data.
In essence, data warehousing is a specialized form of data storage focused on providing a structured environment for business intelligence and analysis, while data storage is the broader concept of preserving digital information for various uses.
OnAir Post: Warehousing & Storage
Top Data Engineering Jobs
Data engineering roles are expected to be among the best careers in 2025, particularly due to the increasing reliance on data-driven decision-making and the growth of AI and machine learning applications. Data engineers are crucial for building and maintaining the infrastructure that supports these systems, making their skills highly sought after.
By focusing on developing the necessary skills and staying updated on the latest technologies, individuals can build successful and rewarding careers in data engineering in 2025 and beyond.
OnAir Post: Top Data Engineering Jobs
