Course: Introduction to BigData and Cloud Technologies BigData and Cloud explained with real-world examples in this intensive 1-day workshop

Introduction to BigData and Cloud Technologies

BigData and Cloud explained with real-world examples in this intensive 1-day workshop


How can we store or process large volumes of data? how to deal with a massive stream of events coming at high velocity? what really is BigData? How does a BigData-ready system look like? and how can Clouds help?

The topics of BigData and Cloud technologoies are being mentioned a lot, but it's hard to know where to start or what technologies to use.

Join our internationally renowned instructors for a full day packed of knowledge sharing. Let us give you an overview with short deep-dives into the vast landscapes of BigData, everything you need to get started with the technologies that changed the world.

Objectives

This course is aimed at giving a good overview for developers and decision-makers new to the field, as well as giving insights and valuable pointers to developers with practical experience.

  • Understanding the challenges with BigData, and approaches for solving them.
  • Storage, Compute and Stream Processing - and an overview of commonly used technologies.
  • Showcasing the characteristics and architecture of a BigData-ready system.
  • Using Cloud technologies, and overview of the notable ones.

Intended Audience

Software Developers, CTOs, Project Managers, Decision Makers and technical people looking to get started with BigData and Cloud technologies or get up to speed quickly.

Prerequisites

None

Modules

  Module 1 - Starting small with BigData
  • What is BigData? and more importantly, what is not BigData?
  • Dealing with the challenges of Volume, Velocity and Variety of data.
  • The Hadoop ecosystem and it's current state
  • Why do we need new technologies?
  Module 2 - Data at rest
  • Distributed file systems (HDFS, consistent hashing, and more)
  • File formats, and why they matter
  • NoSQL and the CAP Theorem
  • Properties of modern storage solution (Schemaless, redundancy, relaxed consistency)
  • Polyglot persistence
  • Overview of NoSQL technologies
  Module 3 - Compute
  • Batch processing and why we can't do ad-hoc querying.
  • The Map/Reduce model and locality of data
  • Hadoop's MapReduce and YARN
  • Computation frameworks like Apache Spark and Flink
  • Monitoring of batch jobs and Spark computations
  • Machine learning
  Module 4 - A BigData-ready stack
  • Distributed systems, microservices, containers and 'serverless'
  • Discovery, synchronization and configuration management
  • Queue systems and commit logs
  • Data workflows and pipelines
  • Design guidelines (explicit consistency expectations, idempotence, and more)
  • Monitoring and alerting (ELK, Graphite, Grafana, Graylog, Redash, Reimann and more)
  • Data warehousing
  Module 5 - Streams and IoT
  • Micro batching
  • Stream processing (Apache Storm, Heron and similar technologies)
  • Lambda architecture
  Module 6 - Clouds
  • Why cloud?
  • Overview of Amazon Web Services
  • Overview of Google Cloud Platform
  • Overview of Microsoft Azure
  • Comparison and highlight of notable strengths of each
  • BigData on the cloud
  Module 7 - Q&A

Q&A panel with our experts - ask us anything.

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