Get Free Widget

Courses

Data Engineering on Google Cloud Platform

Data Engineering on Google Cloud Platform

Category: Data & Machine Learning

Description

This Data Engineering on Google Cloud Platform course is designed to provide participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.
 

Audience

The Data Engineering on Google Cloud Platform course is intended for experienced developers who are responsible for managing big data transformations including:ul>
  • Extracting, Loading, Transforming, cleaning, and validating data/li>
  • Designing pipelines and architectures for data processing/li>
  • Creating and maintaining machine learning and statistical models/li>
  • Querying datasets, visualizing query results and creating reports/li>
  • Duration

    3 days

    Requirements

    • Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent experience.
    • Basic proficiency with common query language such as SQL.
    • Experience with data modeling, extract, transform, load activities.
    • Developing applications using a common programming language such Python.
    • Familiarity with Machine Learning and/or statistics.

    Curriculum

    Google Cloud Dataproc Overviewul>
  • Creating and managing clusters./li>
  • Leveraging custom machine types and preemptible worker nodes./li>
  • Scaling and deleting Clusters./li>
  • Lab: Creating Hadoop Clusters with Google Cloud Dataproc./li>Running Dataproc Jobsul>
  • Running Pig and Hive jobs./li>
  • Separation of storage and compute./li>
  • Lab: Running Hadoop and Spark Jobs with Dataproc./li>
  • Lab: Submit and monitor jobs./li>Integrating Dataproc with Google Cloud Platformul>
  • Customize cluster with initialization actions./li>
  • BigQuery Support./li>
  • Lab: Leveraging Google Cloud Platform Services./li>Making Sense of Unstructured Data with Google's Machine Learning APIsul>
  • Google's Machine Learning APIs./li>
  • Common ML Use Cases./li>
  • Invoking ML APIs./li>
  • Lab: Adding Machine Learning Capabilities to Big Data Analysis./li>Serverless data analysis with BigQueryul>
  • What is BigQuery./li>
  • Queries and Functions./li>
  • Lab: Writing queries in BigQuery./li>
  • Loading data into BigQuery./li>
  • Exporting data from BigQuery./li>
  • Lab: Loading and exporting data./li>
  • Nested and repeated fields./li>
  • Querying multiple tables./li>
  • Lab: Complex queries./li>
  • Performance and pricing./li>Serverless, autoscaling data pipelines with Dataflowul>
  • The Beam programming model./li>
  • Data pipelines in Beam Python./li>
  • Data pipelines in Beam Java./li>
  • Lab: Writing a Dataflow pipeline./li>
  • Scalable Big Data processing using Beam./li>
  • Lab: MapReduce in Dataflow./li>
  • Incorporating additional data./li>
  • Lab: Side inputs./li>
  • Handling stream data./li>
  • GCP Reference architecture./li>Getting started with Machine Learningul>
  • What is machine learning (ML)./li>
  • Effective ML: concepts, types./li>
  • ML datasets: generalization./li>
  • Lab: Explore and create ML datasets./li>Building ML models with Tensorflowul>
  • Getting started with TensorFlow./li>
  • Lab: Using tf.learn./li>
  • TensorFlow graphs and loops + lab./li>
  • Lab: Using low-level TensorFlow + early stopping./li>
  • Monitoring ML training./li>
  • Lab: Charts and graphs of TensorFlow training./li>Scaling ML models with CloudMLul>
  • Why Cloud ML?/li>
  • Packaging up a TensorFlow model./li>
  • End-to-end training./li>
  • Lab: Run a ML model locally and on cloud./li>Feature Engineeringul>
  • Creating good features./li>
  • Transforming inputs./li>
  • Synthetic features./li>
  • Preprocessing with Cloud ML./li>
  • Lab: Feature engineering./li>Architecture of streaming analytics pipelinesul>
  • Stream data processing: Challenges./li>
  • Handling variable data volumes./li>
  • Dealing with unordered/late data./li>
  • Lab: Designing streaming pipeline./li>Ingesting Variable Volumesul>
  • What is Cloud Pub/Sub?/li>
  • How it works: Topics and Subscriptions./li>
  • Lab: Simulator./li>Implementing streaming pipelinesul>
  • Challenges in stream processing./li>
  • Handle late data: watermarks, triggers, accumulation./li>
  • Lab: Stream data processing pipeline for live traffic data./li>Streaming analytics and dashboardsul>
  • Streaming analytics: from data to decisions./li>
  • Querying streaming data with BigQuery./li>
  • What is Google Data Studio?/li>
  • Lab: build a real-time dashboard to visualize processed data./li>High throughput and low-latency with Bigtableul>
  • What is Cloud Spanner?/li>
  • Designing Bigtable schema./li>
  • Ingesting into Bigtable./li>
  • Lab: streaming into Bigtable./li>
  • Classes

    City Date Duration Language Format Early Price Exp Date Price Early bird Price Regular GTR

    Register Here!

    Our Delivery

    on site delivery

    Online or On-site de

    We created a personalized delivery strategy by offering blended learning

    communities of practice

    Communities of practice

    Find a bunch of people who are on the same page with you.

    engaging

    Engaging

    Engaging platform with gamification for collaboration and friendly competition.

    interactive

    Interactive

    Interactive online trainings and live webinars available.

    efficient

    Efficient

    A special learning environment can boost learning efficiency.

    accesible

    Accessible

    Available anywhere and anytime, on your phone, computer or tablet.

    Q&A

    • Can you customize courses to suit our particular requirements? Yes, of course. We offer training consultancy and we establish the most appropriate solution according to the specific needs and business objectives of your company. Contact us and we’ll find the best training solution for you.
    • Can individuals use your services or they are organized for companies?Yes, we organise open courses which can be accessed by individuals. Whether you are looking for an IT or a Business training, you can find it at Brain Concert. Moreover, you’ll meet people from the same area of work as you and we can create communities of practice, where you can share professional tips and tricks and best practices.
    • What types of trainings are available for my company? We offer a variety of courses: in the IT area, there are Agile & Lean Courses, Software Development Courses, QA Courses, Software Administration Courses and Security Courses. We also offer Business Courses, focused on the development of soft skills. Define your objectives and choose the most appropriate training for you or your company.
    • What should I know before choosing a training? You should know that we offer courses for everyone, but you have to choose according to some criteria. First, define your objectives, then, the level of the participants (basic, advanced). There are courses with some requirements attached because the participants of a training session must form a compact group in terms of their previous knowledge so that they and the trainers are on the same page.
    • What is the minimum number of participants if we want in-house training? The minimum number of participants for an in-house depends on the course type. Please contact us to establish these details.
    • Can you organize virtual training sessions or only face to face trainings? Yes, you can choose an online or offline training session. We use Knolyx, an e-learning platform to make the process of online training delivery as efficient as a face to face training session.