Description

This 1-day 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: BDA2020
Price: 725 EUR

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Outline

 
 
 
 
 
 
 
 
 
 

An Introduction to Big Data

This session 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 Technology

This session looks at big data platforms and storage options.

  • The new multi-platform analytical ecosystem
  • Analytical RDBMSs and NoSQL options
  • An introduction to the Hadoop stack
  • Apache Spark framework
  • The big data Hadoop marketplace
  • The cloud analytics option – cloud storage versus Hadoop, Amazon (Data Lake Formation, Kenisis, Elastic MapReduce and Redshift), Google (Pub/Sub, Data Fusion, DataProc and BigQuery), Microsoft Azure (Event Hub, Stream Analytics, Data Lake, HDInsight, Data Factory and Synapse Analytics, ML Service, Power BI), IBM (Streaming Analytics, Analytics Engine, Db2 Warehouse on Cloud, Cloud Pak for Data), Qubole, Oracle Analytics Cloud, SAP 
  • Accessing big data via SQL on Hadoop or SQL on cloud storage
  • The increasing power of analytical relational DBMSs
  • Analyzing big data – What’s in the data scientist’s toolkit
    • Supervised and unsupervised machine learning
    • Cloud machine learning analytics marketplace
    • Natural language processing & sentiment analysis
    • Search, BI & big data
    • Graph analytics
    • Analyzing data in motion using streaming analytics
    • Integrating it all with self-service BI tools

Integrating Big Data Analytics into the Enterprise

This session looks at how new big data platforms can be integrated with traditional data warehouses and data marts to create a new data and analytics architecture for the data driven enterprise. It looks at stream processing, cloud storage, Hadoop, NoSQL databases and data warehouse and shows how to put them together in an end-to-end architecture to maximize business value from big data.

  • Beyond data warehouse – a new analytical architecture and ecosystem for the data driven enterprise
  • Integrated management of the analytical ecosystem
  • Integrating stream processing, cloud storage, Hadoop, Data Warehouses and MDM
  • Simplifying access to a multiplatform analytical ecosystem using data virtualisation
  • Multi-platform optimisation – the final frontier

Ingest, Prepare, Analyze and Govern Big Data

This session will look at the challenge of integrating and governing big data and the unique issues it raises. How do you deal with very large data volumes and different varieties of data? How does loading data into Hadoop differ from loading data into analytical relational databases? What about NoSQL databases? How should low-latency data be handled? It also looks at tools and techniques available to data scientists, business analysts and traditional DW/BI professionals to analyze big data. Topics that will be covered include:

  • Connecting to big data sources
  • Data ingestion into cloud storage or Hadoop
    • Data ingestion options
    • Challenges of capturing different types of big data
    • Streaming data ingest
    • Parsing unstructured data
    • Change data capture – what’s possiblity?
  • ELT data preparation, transformation and integration at scale using Spark
  • Managing data scientist and business analysts self-service data preparation – Alteryx, Azure Data Factory, Google Cloud Data Fusion, IBM Cloud Pak for Data, Datarobot Paxata Data Preperation, Trifacta, Tamr, MicroStrategy, Tableau Data Prep Builder
  • Unified data delivery – a common data integration supply chain for the entire analytical ecosystem
  • Multi-platform data and analytical pipelines from data lake to enterprise data marketplace
  • Data governance in a big data environment
    • The importance of an information catalog
    • Organizing data in a data lake
    • Governing data privacy
    • Governing data in a data science environment

Pricing

The fee for this 1-day 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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