Google BigQuery in Practice

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Google BigQuery in Practice


This workshop shows you how Google's enterprise data warehouse BigQuery makes large scale data analysis accessible to everyone at a very low cost.

ON REQUEST
Location: On-demand (@Wherever and whenever you want)
Presented in English by Geert Van Landeghem
Price: ASK FOR PRICE QUOTE (excl. 21% VAT)

 Learning Objectives

Why you should attend this workshop on Google BigQuery:

BigQuery is a serverless, highly scalable and cost-effective enterprise data warehouse that is available on multiple cloud platforms, including the Google Cloud Platform. Google's enterprise data warehouse was designed to make large scale data analysis accessible to everyone, from data-savvy profiles like data engineers and data scientists, to less technical profiles like report / dashboard builders and business analysts.

We'll show you how BigQuery can help you get valuable insights from your data with ease, whether it is log data from thousands of retail systems or IoT data streams from millions of vehicle sensors.

If your company is on the road to become data-driven, it is probably analysing big volumes of data, sitting in a data warehouse architecture or Hadoop clusters, either on-premise or in the cloud. Very quickly, you'll run into problems with data and usage growth, scalability, maintenance, upgrades and licensing costs. If you have trouble finding the necessary skills and budgets for managing a scalable setup, then a cloud data platform like BigQuery may be the answer. Say goodbye to data silo's, huge licensing costs, infrastructure maintenance and upgrades, and start analysing all of your data.

Because BigQuery is a serverless compute architecture that decouples compute and storage, this enables diverse layers of the architecture to perform and scale independently, which also gives data developers flexibility in design and deployment.

This two-day workshop shows you how and why BigQuery has become one of the best platforms for analyzing and learning from data. Powerful features include Standard SQL, deeply nested data, user defined functions in JavaScript and SQL, geospatial data, integrated machine learning, URL addressable data sharing, federated queries, integration with other Google products, ... just to name a few. All these features allow you to implement self-service ad hoc data exploration that many users demand.

During this workshop, we will use Google's Colaboratory, or 'Colab' for short, as development environment. This allows you to write and execute SQL asnd Python in your browser without installing and configuring any software, while having free access to GPUs and really easy sharing. Think about Colab as a Jupyter notebook stored in Google Drive. Whether you're a student, a data scientist or an AI researcher, Colab can make your work easier. Watch Introduction to Colab to find out more.

For this workshop, we will set up a personal BigQuery / Colab development environment, where you can execute the prepared exercises, in the browser, and experiment with your own code and data. Many exercises will be based (with permission) on the fantastic Google BigQuery: Definitive Guide book by Valliappa "Lak" Lakshmanan.

Google BigQuery was recognised by Gartner as a leader in the Magic Quadrant for Cloud Database Management Systems (23 November 2020).

Prerequisites:

This course assumes that you are interested in data, have some knowledge of SQL and understand the basics of databases and data warehouses. There is no need to know the Google Cloud Platform, Colab or Python to attend this course.

Target Audience:

We are aiming this workshop at data engineers, data analysts, data scientists and developers of data-driven applications, but we will also illustrate BigQuery's power in machine learning and data visualisation, while mentioning the security and governance of using a cloud-based data warehouse.

 Full Programme

9.00h
Welcome

The timing of the live online workshop is from 9h00 till 17h00 at the latest. There is a coffee/tea/refreshments break in the morning and in the afternoon (timing varies slightly), and there is a lunch break from 12h45 till 13h30 approximately.

What is Google BigQuery
  • How BigQuery Came About
  • How does BigQuery work
  • Integration with the Google Cloud Platform
  • Accessing multiple data sources
  • Extracting, Transforming and Loading data: ETL vs EL vs ELT
  • Comparison with other cloud data platforms like Snowflake, Redshift or Synapse
Introducing Colab, our development environment
Query Essentials
Data Types, Functions & Operators
Loading Data into BigQuery
Developing BigQuery
Advanced Queries
Advanced Topics, briefly explained here but part of a one-day follow-up course
  • Technical Architecture of BigQuery
  • Optimising Performance and Cost
  • Machine Learning in BigQuery
  • Administering and Securing BigQuery
Summary and Conclusions
17.00h
End of this two-day workshop

 Speakers


Geert Van Landeghem (Tree53)
Tree53

Geert Van Landeghem is a Big Data consultant with 25 years of experience working for companies across industries. He worked on his first big data project in 2011, and is still consulting companies on how to adopt big data within their organisation.

He has worked as the Head of BI for a gambling company in Belgium, where he led a team of 8 people. He is an Apache Spark Certified Developer since November 2014, and has worked as an instructor for IBM and Datacrunchers, where he teaches Hadoop and Spark-related courses.

He is currently examining how Artificial Intelligence can be used for business use cases and as such followed the first IBM Watson and O'Reilly AI conferences abroad.

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