This 1-day/8 hours course is aimed to get you up to scratch on big data technologies such as Spark, Kafka, Analytical SQL, NoSQL DBMSs and Multi-Platform Analytics. What is big data? How can you make use of it? How does it integrate with a traditional analytical environment? How do you re-define your architecture to create a stronger analytical foundation for your company? What skills do you need to develop for big data analytics? All of these questions are addressed in this knowledge packed course.

Why attend

You will learn:

  • How big data creates several new types of analytical workloads
  • Big data technology platforms beyond the data warehouse
  • Big data analytical techniques and front-end tools
  • Understand when to use what where - business use cases for different big data technologies
  • How to create a stronger data and analytical architecture by integrating big data, data science, data warehouses and BI
  • How to integrate real-time data into your data warehouse
  • How to analyse un-modelled, multi-structured data using Spark
  • How to leverage predictive analytics in BI reports & dashboards

Who should attend

IT directors, CIO’s, CDO’s, IT managers, BI managers, BI and data warehousing professionals, data scientists, enterprise architects, data architects

Code: BDA2021
Price: 725 EUR

Inquire about this course

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An Introduction to Big Data

This module defines big data and looks at why business wants to use big data technology. It looks at big data use cases and the difference between big data, traditional BI and data warehousing.

  • The demand for data?
  • Types of big data
  • Why analyse big data?
  • Industry use cases – popular big data analytic applications
  • What is data science?
  • Data warehousing and BI versus big data
  • Popular patterns for big data technologies
  • Types of big data analytical workloads
  • Architecture: the extended analytical ecosystem

Big Data Platforms and How They Fit in a Modern Data Architecture

This module looks at big data platforms and storage options and how all of it fits together in an end-to-end data architecture. The topics covered include:

  • The new multi-platform analytical ecosystem
  • Analytical RDBMSs and NoSQL options
  • An introduction to the Hadoop stack
  • Apache Spark framework
  • Accessing big data via SQL on Hadoop or SQL on Cloud Storage
  • NoSQL Graph Databases
  • The cloud analytics option - Cloud storage, Spark as a Service and the increasing power of analytical relational DBMSs
    • Cloud storage and Spark as a Service Versus Cloud based Hadoop systems
    • Cloud Data Warehouse Analytic RDBMSs
    • The Lakehouse - Deeper integration between cloud storage and the data warehouse 
    • Cloud Vendor technologies
      • Amazon (AWS Lake Formation, Kenisis, Elastic MapReduce, Redshift, SageMaker)
      • Microsoft Azure (Event Hub, Stream Analytics, Data Lake, HDInsight, Data Factory and Synapse Analytics, ML Service, Power BI), 
      • Google (Pub/Sub,  DataProc, BigQuery, Dataplex, Vertex AI, Looker), 
      • IBM (Streaming Analytics, Db2 Warehouse on Cloud, Cloud Pak for Data, Watson Studio, Cognos Analytics), 
      • Oracle 
      • SAP 
    • How Big Data Systems Fit into a Modern Data Architecture

Producing Trusted Data Using DataOps Pipelines

This module will look at the challenge of ingesting, preparing and integrating big data from data lake to data marketplace and the unique issues it raises.  Topics that will be covered include:

  • Big data sources and types
  • Why are data catalog and data fabric software technologies important?
  • Defining common data entities in a catalog business glossary 
  • Using a catalog for automatic data discovery 
  • Creating DataOps pipelines to ingest data and building reusable trusted data assets in using scalable data fabric 
  • End-to-end DataOps from data lake to data marketplace

Analyzing Big Data

This module will look at analyzing big data and explores how you do analytics at scale on structured, semi-structured and unstructured data.  Topics that will be covered include:

  • Machine learning
  • Natural language processing
  • Graph analysis
  • Streaming analytics
  • Integrating Self-service BI and Big Data


09 Nov09 Nov
Virtual Live Class


The fee for this 1-day/8 hours course is EUR 725 (+VAT) per person.

We offer the following discounts.

  • 10% discount for groups of 2 or more students from the same company registering at the same time.
  • 20% discount for groups of 4 or more students from the same company registering at the same time.

Note: Groups that register at a discounted rate must retain the minimum group size or the discount will be revoked. Discounts cannot be combined.

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