We may earn an affiliate commission when you visit our partners.
Big Data LDN
This course is no longer available. Find something similar by browsing:
Data Engineering
Event Processing
Big Data
Data Pipelines
Data Analytics
Register for this course and see more details by visiting:
OpenCourser.com/course/niqf1l/data
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches data engineering workflows for processing, presenting, and interpreting 28 billion events daily
Covers receiving, processing, and presenting data effectively in a business intelligence context
Suitable for data engineers and other professionals seeking to enhance their understanding of big data processing workflows
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Activities
Coming soon
We're preparing activities for
Data Engineering in MagicLab: The Birth and Life of an Event.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Data Engineering in MagicLab: The Birth and Life of an Event will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure for storing, processing, and analyzing data. In this role, you would be responsible for designing and implementing data pipelines, ensuring data quality, and developing tools for data analysis. This course would help you to develop the technical skills necessary to succeed as a Data Engineer, and would provide you with an understanding of the challenges and opportunities associated with working with big data.
Data Analyst
Data Analyst
Data Analysts use data to identify trends, patterns, and insights that can help businesses make better decisions. In this role, you would be responsible for collecting, cleaning, and analyzing data, and for developing data visualizations and reports. This course would help you to develop the analytical skills necessary to succeed as a Data Analyst, and would provide you with an understanding of the different techniques and tools used for data analysis.
Data Scientist
Data Scientist
Data Scientists use data to develop predictive models and machine learning algorithms. In this role, you would be responsible for identifying business problems that can be solved using data, and for developing and implementing data-driven solutions. This course would help you to develop the technical skills necessary to succeed as a Data Scientist, and would provide you with an understanding of the different techniques and tools used for data science.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. In this role, you would be responsible for collecting, cleaning, and analyzing data, and for developing data visualizations and reports. This course would help you to develop the analytical skills necessary to succeed as a Business Intelligence Analyst, and would provide you with an understanding of the different techniques and tools used for business intelligence.
Database Administrator
Database Administrator
Database Administrators are responsible for the maintenance and performance of databases. In this role, you would be responsible for installing, configuring, and maintaining databases, and for ensuring data security and integrity. This course would help you to develop the technical skills necessary to succeed as a Database Administrator, and would provide you with an understanding of the different types of databases and the tools used to manage them.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. In this role, you would be responsible for writing code, debugging software, and testing software. This course would help you to develop the technical skills necessary to succeed as a Software Engineer, and would provide you with an understanding of the different software development methodologies and tools.
Data Architect
Data Architect
Data Architects design and build the data infrastructure for organizations. In this role, you would be responsible for developing data models, designing data warehouses, and implementing data security measures. This course would help you to develop the technical skills necessary to succeed as a Data Architect, and would provide you with an understanding of the different data architectures and the tools used to implement them.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect organizations from cyberattacks. In this role, you would be responsible for identifying and mitigating security risks, and for developing and implementing security policies and procedures. This course would help you to develop the technical skills necessary to succeed as an Information Security Analyst, and would provide you with an understanding of the different security threats and the tools used to mitigate them.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. In this role, you would be responsible for managing project scope, budget, and timeline. This course would help you to develop the project management skills necessary to succeed as a Project Manager, and would provide you with an understanding of the different project management methodologies and tools.
Business Analyst
Business Analyst
Business Analysts help organizations to improve their business processes. In this role, you would be responsible for analyzing business needs, developing solutions, and implementing change. This course would help you to develop the analytical skills necessary to succeed as a Business Analyst, and would provide you with an understanding of the different business analysis techniques and tools.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to individuals and organizations. In this role, you would be responsible for analyzing financial data, developing financial models, and making investment recommendations. This course would help you to develop the analytical skills necessary to succeed as a Financial Analyst, and would provide you with an understanding of the different financial markets and the tools used to analyze them.
Marketing Analyst
Marketing Analyst
Marketing Analysts help organizations to improve their marketing campaigns. In this role, you would be responsible for analyzing marketing data, developing marketing strategies, and measuring marketing effectiveness. This course would help you to develop the analytical skills necessary to succeed as a Marketing Analyst, and would provide you with an understanding of the different marketing channels and the tools used to analyze them.
Sales Analyst
Sales Analyst
Sales Analysts help organizations to improve their sales performance. In this role, you would be responsible for analyzing sales data, developing sales strategies, and measuring sales effectiveness. This course would help you to develop the analytical skills necessary to succeed as a Sales Analyst, and would provide you with an understanding of the different sales channels and the tools used to analyze them.
Operations Analyst
Operations Analyst
Operations Analysts help organizations to improve their operational efficiency. In this role, you would be responsible for analyzing operational data, developing operational strategies, and measuring operational effectiveness. This course would help you to develop the analytical skills necessary to succeed as an Operations Analyst, and would provide you with an understanding of the different operational processes and the tools used to analyze them.
Risk Analyst
Risk Analyst
Risk Analysts help organizations to identify and mitigate risks. In this role, you would be responsible for analyzing risk data, developing risk strategies, and measuring risk effectiveness. This course would help you to develop the analytical skills necessary to succeed as a Risk Analyst, and would provide you with an understanding of the different risk types and the tools used to analyze them.
For more career information including salaries, visit:
OpenCourser.com/course/niqf1l/data
Reading list
We haven't picked any books for this reading list yet.
Provides a practical guide to using Pandas for data analysis. It covers all aspects of Pandas, from data loading and cleaning to data manipulation and visualization.
Provides a comprehensive guide to machine learning with Python. It covers all aspects of machine learning, from data preprocessing to model building and evaluation.
Save
Provides a comprehensive overview of deep learning. It covers all aspects of deep learning, from the basics to the latest research.
Provides a practical guide to using data science for business. It covers all aspects of data science, from data collection to model building and deployment.
Provides a practical guide to using data-driven marketing to improve marketing campaigns. It covers all aspects of data-driven marketing, from data collection to customer segmentation and targeting.
Save
Provides a comprehensive overview of predictive analytics. It covers all aspects of predictive analytics, from data preparation to model building and evaluation.
Save
Provides a comprehensive overview of data visualization. It covers all aspects of data visualization, from the basics to the latest research.
Save
Provides a comprehensive overview of big data. It covers all aspects of big data, from the challenges of big data to the opportunities that big data offers.
Provides a comprehensive guide to building and managing data warehouses. It covers all aspects of data warehousing, from data modeling to data integration and optimization.
Save
Provides a comprehensive guide to using Apache Beam for building and managing data pipelines. It covers all aspects of Apache Beam, from installation and configuration to data ingestion and scheduling.
Considered a modern classic, this book delves into the fundamental trade-offs and concepts behind building robust, scalable, and maintainable data systems. While not exclusively about data engineering, its in-depth coverage of distributed systems, databases, and data processing patterns is essential for any data professional looking to deepen their understanding. It is highly valuable for undergraduate and graduate students, as well as working professionals.
Foundational text in data warehousing, a core component of data engineering. It provides timeless principles and techniques for dimensional modeling, which are still highly relevant in modern data platforms. While the latest edition was published in 2013, the concepts remain crucial for understanding data organization for analytical purposes.
This pocket reference offers practical guidance on building and managing data pipelines. It useful resource for understanding the components and considerations involved in moving and processing data for analytics. serves as a good supplementary read for those actively involved in pipeline development.
Another comprehensive guide to Apache Spark, co-authored by one of its creators. definitive resource for learning Spark for big data processing, covering its various components and APIs. It's a must-read for anyone serious about using Spark in their data engineering work, suitable for undergraduate, graduate, and professional levels.
Given the prevalence of stream processing in modern data engineering, this book on Apache Kafka is highly relevant. It covers the core concepts, architecture, and APIs of Kafka, providing the knowledge needed to build real-time data pipelines. This is particularly useful for those interested in the streaming aspects highlighted in some of the course titles. The second edition was published in 2021, making it quite current.
Python fundamental language in data engineering. focuses on using Python for various data engineering tasks, including building data pipelines and working with large datasets. It is particularly useful for those whose background is in Python and want to apply their skills to data engineering, aligning with courses mentioning Python for Data Engineering. Published in 2020, it's a relatively recent resource.
Offers a collection of insights and advice from various data engineering experts on a wide range of topics. It provides a broad perspective on different aspects of data engineering, from technical skills to best practices and industry trends. It's a valuable resource for gaining diverse viewpoints and understanding the multifaceted nature of the role.
Save
While not specific to data engineering, this book is considered a fundamental read for any software professional, including data engineers. It emphasizes the importance of writing readable, maintainable, and well-structured code, which is crucial for building robust data systems and pipelines. provides foundational principles for writing high-quality code.
Save
Introduces the concept of Data Mesh, a decentralized approach to data architecture that is gaining traction. It's a valuable read for understanding contemporary thinking in data engineering, particularly for those at the graduate or professional level looking to explore modern paradigms beyond traditional centralized data lakes and warehouses. Published in 2022, it's a very recent and relevant text.
Provides a practical guide to building and managing data science teams. It covers topics such as hiring, training, and motivating data scientists, as well as best practices for data science project management.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/niqf1l/data
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Via
Pluralsight
Instructor
Big Data LDN
This course belongs to a
collection
called
Big Data LDN 2019 . It's comprised of 101 courses:
- The Database Is Only Half Done
- Your AI Is Wrong! Lessons from Decision-making with Imperfect AI
- Multiparadigm Data Science: The Next Frontier in Decision Making
- Achieving 100% Adoption of Analytics in the Enterprise
- Data Skills: How Mondi Does Digital
- Building a Modern Data Architecture for the Data Driven Enterprise
- Harnessing the Power of Data to Improve Public Health
- Real-time Data Analytics at Scale Using Cloud Services
- Quick and Easy Access to Data through a Logical Data Warehouse Environment
- The Promise of Data Mastering at Scale
- Research vs. Reality in AI: Would You Trust Your Model with Your Life?
- Modernise Your IT Infrastructure for AI
- Scaling Machine Learning at Holiday Extras
- Why Do Some Machine Learning Models Fail?
- AI: Lessons Learned from the Front Line
- Machine Learning in Real-time: Predicting Taxi Fare in NYC
- How AI and Automated Machine Learning Will Disrupt the Organisational Alignment of Businesses
- Multi-model Powered Machine Learning
- ML in Production: Serverless and Painless
- Leveraging Data Virtualization to Maximize Advanced Analytics and Machine Learning Potential in Your Data Architecture
- Bring the Power of Watson to AWS and Azure: Watson Anywhere Powered by Cloud Pak for Data
- Modern Data Warehousing at Scale Using Azure Data Factory
- Balancing Performance, Scalability, and High Availability in Your Database Environment
- Enabling Microservices for Big Data On-demand in the Cloud
- From ETL to ELT: Building a Scalable Data Stack with SQL
- Data, Governance, and Freedom
- How to Get the Technology Stack Right for Your Business
- Data Modelling with Apache Cassandra
- How to Orchestrate Your ML Pipelines in Hybrid Cloud
- Full Stack Metadata Driven Thinking to Drive Business Agility
- Enhance Developer Experience Using OpenShift on Azure
- Fast Data for Faster Business
- Driving Faster, Smarter Actions on IoT Data with New Real-time Analytics Capabilities
- Introduction to Adept and How Big Data and Analytics Is Transforming the Connected Car Ecosystem
- Bridge to Cloud
- How to Train Your AI Models at Scale
- 99% of Your IoT Data Is Worthless, but Which 99%?
- Data Engineering in MagicLab: The Birth and Life of an Event (viewing)
- RNA - Event Stream Processing Made Simple
- When Data Leads, Transformation Follows - A Look at Qlik's Data Integration Portfolio
- Saving Energy in Homes with a Unified Approach to Data and AI
- Building Stream Processing Applications with Apache Kafka Using KSQL
- Real-world, Real-time Digital Transformation
- Machine Data in Industry 4.0: How to Handle It Better
- Why CockroachDB in the Kindred Platform?
- The Hardest Part of AI & Analytics Is Not AI, It Is Data Management
- Effective Cloud Migration Strategies for Your Modern Data Applications
- Snowflake? Why Should I Care?
- The Future of Business Intelligence Isn't a Tool
- AI-driven Retail for the 21st Century
- The Next Generation of Data Architecture
- Modern Enterprise Data Engineering
- Taking Control of User Analytics with Snowplow
- Flex Your Data(Ops) Muscles
- You Love Big Data, so Does R
- Building Predictive Maintenance Solutions
- Quickly, Codelessly Connect Any Application to Any REST API
- Rethinking Data Warehouse Modernization in the Age of Hybrid Cloud/On-premise Data Warehouses
- From Waterfall to Agile – A Data Warehouse Modernisation Story
- Freeing up Engineering and Infrastructure Resources to Scale with DataOps
- Unit Testing with pytest
- Credit Card Fraud – Why the Database Matters
- Data Science at SEGA
- British Telecom: Data-driven Digital Transformation
- Using Cloud Solution Platforms to Drive Business Value and Cost Optimization
- Data Analytics & IoT Innovations at Bosch
- Converting the CDO to a Profit Centre
- Why Self-service Data Analytics Usually Fails & Ideas for Succeeding
- Governed Data Discovery
- Journey to Create a Great Analytic Culture
- Something Amazing for Everyone: Embracing Inclusive Design to Deliver Accessible Experiences
- Democratising Data – How We Built a Self-serve Data Platform
- Discover the “Why” You Didn't Know Existed
- Selecting Your Fantasy Premier League Team with Advanced Analytics
- Decouple Storage from Compute
- Data Skills: Building a Data Team Fit for a Data-driven Enterprise
- Consent, Compliance, and the Impact on Customer Experience
- AI & Communication: Meaningful Data from New Sources
- Real-time Personalised Customer Interaction at Scale
- Data: The Musical, with LW Theatres
- Self-service and Tableau Community at Lloyds Banking Group
- The Digital Hero’s Journey: Using Big Data to Uncover Business Value and Meaning at Work
- Using Location Intelligence to Place Solar Panels in Rwanda
- Embedding Intelligence into the Heart of Customer Operations
- The Art of Customer Conversations
- How to Build a Fully Autonomous Card Fraud Detection System
- At Last! The Real-time Customer Experience
- Data Privacy Mythbusting
- Demystifying Data – What They Don’t Tell You at Data School
- Optimizing Your Data Value Chain with Agile Data Management
- Data at SEGA Europe
- Keep Your Data Lake from Getting Hadooped
- Top 10 Privacy Tips & Tricks in Big Data
- Data Governance as a Customer Service
- 7 AI Quick Win Projects
- Delivering Value with Intelligent Data Governance
- The Culture Competition – Personalisation vs. Privacy
- DataOps for AI and Digital Transformation
- Big Data LDN: A GDPR Retrospective
- GDPR: A Sword Not a Shield
- Drive Business Growth While Satisfying GDPR and CCPA
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser