Where is this course held?

Upcoming sessions run in Dubai, Cape Town, Amsterdam, London, Istanbul, Munich, Paris, Frankfurt, Kuala Lumpur, Rome, Vienna, Geneva, Madrid.

How much does the course cost?

Fees start from £2,250 per delegate, depending on the city. Fees include tuition and course materials.

Do participants receive a certificate?

Yes. Delegates who complete the course receive a London Leadership Management Innovation certificate of completion.

Can this course be delivered in-house for our team?

Yes. LLMI can tailor and deliver this course privately for your organisation at your premises or in one of our training cities.

Certificate in Advanced Big Data and Data Analytics (CABDDA) (DMBI-011) Training Course

Q: How long is the Certificate in Advanced Big Data and Data Analytics (CABDDA) course?

The course runs over 5 days of classroom training, usually Monday to Friday.

Q: When is the next Certificate in Advanced Big Data and Data Analytics (CABDDA) course?

The next public date is 14 - 18 Dec 2026 in Dubai. There are 13 upcoming dates in total.

Course overview

Why Attend

Big data is a change agent that challenges the ways in which organizational leaders have traditionally made decisions. This course provides participants with the conﬁdence to store, process, analyze and present big data use cases within their organizations. This course provides a multitude of hands-on labs with Spark, a key big data technology used to solve data intensive problems. Participants will gain the knowledge and skills they need to assemble and manage a large-scale big data analytics project. Lastly, participants will work through advanced machine learning and deep learning use cases.

This is our most advanced course in our big data series following Certified Big Data and Data Analytics Practitioner (CBDDAP) and Certificate in Big Data Fundamentals (CBDF). Participants will aim to identify areas within their organization that can be improved through big data use cases, and work on an individual chosen data project during the course. By the end of the course, participants will be able to work through multiple methods and practical approaches to leverage Spark for advanced big data analytics.

Course Methodology

This course will be highly technical with group discussions, hands-on practical exercises, and group activities being the core focus.

Course Objectives

By the end of the course, participants will be able to:

Understand key big data technologies, including a deep dive into Apache Spark
Describe the main challenges and advantages of Hadoop map-reduce
Demonstrate and discuss key technologies for big data storage and compute, such as PostgreSQL and object storage
Discuss popular machine learning algorithms, deep learning techniques and the importance of ethics in data analytics and artiﬁcial intelligence
Deliver a presentation demonstrating the analytics lifecycle and Spark

Target Audience

This is an advanced level course. It is expected that participants either have a number of years of experience utilizing big data, or have previously attended the Certified Big Data and Data Analytics Practitioner (CBDDAP) course. This course is ideal for data engineers, AI engineers and data scientists. Recommended pre-knowledge includes some python programming experience and data visualization practice.

Target Competencies

Big data utilization
Big data analytics structures and technologies
Ethics and integrity for big data and AI development
Big data storage
Apache Spark best practices

Course outline

1Day 1: Big Data Analytics Use Cases
- How can big data projects meet organizational needs
- Big data examples:
- Netﬂix
- LinkedIn
- Facebook
- Google
- Orbitz
- Dell
- Others
- Best practices in project design
- Assessing the current state of your organization
- Choosing datasets for course projects
2Day 2: Storing Big Data
- Big data architectures and paradigms
- The Hadoop Ecosystem
- Overview of Hadoop
- Hadoop Distributed File System (HDFS)
- Massively parallel processing (MPP) versus distributed in-memory applications
- RDBMSs vs NoSQL DBs
- PostgreSQL
- MongoDB
- Cassandra
- Streaming data
- Data-warehousing versus Data Mart
- Intro to Apache Spark
- Big data SQL hands-on-labs
3Day 3: Computing Big Data
- How to access big data
- Role of cloud computing
- Data movement risk
- Networking and co-location
- Apache Spark lab
- Big data extract, transform, load (ETL) big data compute technologies
- Distributed compute
- High performance clusters vs Apache Spark
- Streaming: Storm, Spark structured streaming
- Apache Spark ETL labs
- Apache Spark data engineering
4Day 4: Big Data Advanced Analytics and AI
- Analytics Lifecycle
- Apache Spark vs Pandas
- Big data machine learning & deep learning in Spark
- Importance of ethics in AI
- Automl & Hyperparameter tuning
5Day 5: Course Big Data Projects
- Identify analytical opportunities in an organization
- Deﬁne and assess the problem
- Describe the impact and use of data to address the problem
- Identify potential data sources
- Design a data analytics project
- Access, explore, analyze and visualize chosen dataset for project
- Present project insights in course