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Google Certified Data Engineer

Learn how to design, build and operate powerful big data and machine learning solutions using Google Cloud Platform

This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Data Engineer certification.

    This professional certificate incorporates hands-on labs using Qwiklabs platform. These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

    1:1 Coaching

    24*7 Support

    CloudLabs

    High Success Rate

    Globally Renowned PSTs Trainer

    Real-time code analysis and feedback

    Course Description

    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 derive insights. The course covers structured, unstructured, and streaming data.

    A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

    Learning Objectives

    This course teaches participants the following skills:

    • Design and build data processing systems on Google Cloud Platform
    • Process batch and streaming data by implementing auto scaling data pipelines on Cloud Dataflow
    • Enable instant insights from streaming data
    • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
    • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
    • Derive business insights from extremely large datasets using Google BigQuery

    Schedule

    Enroll your course with
    $133.75
    Sept 06-10, 2021
    09:30AM – 05:30PM
    Trainer
    Ajit Kumar
    Singapore
    Weekday
    Enroll your course with
    $133.75
    Oct 25-29, 2021
    09:30AM – 05:30PM
    Trainer
    Ajit Kumar
    Singapore
    Weekday
    Enroll your course with
    $133.75
    Nov 22-26, 2021
    09:30AM – 05:30PM
    Trainer
    Ajit Kumar
    Singapore
    Weekday

    Certification Curriculum

    Module 1
    Introduction to Data Engineering
    • Explore the role of a data engineer.
    • Analyze data engineering challenges.
    • Intro to BigQuery.
    • Data Lakes and Data Warehouses.
    • Demo: Federated Queries with BigQuery.
    • Transactional Databases vs Data Warehouses.
    • Website Demo: Finding PII in your dataset with DLP API.
    • Partner effectively with other data teams.
    • Manage data access and governance.
    • Build production-ready pipelines.
    • Review GCP customer case study.
    • Lab: Analyzing Data with BigQuery.
    Module 2
    Building a Data Lake
    • Introduction to Data Lakes.
    • Data Storage and ETL options on GCP.
    • Building a Data Lake using Cloud Storage.
    • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
    • Securing Cloud Storage.
    • Storing All Sorts of Data Types.
    • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
    • Cloud SQL as a relational Data Lake.
    • Lab: Loading Taxi Data into Cloud SQL.
    Module 3
    Building a Data Warehouse
    • The modern data warehouse.
    • Intro to BigQuery.
    • Demo: Query TB+ of data in seconds.
    • Getting Started.
    • Loading Data.
    • Video Demo: Querying Cloud SQL from BigQuery.
    • Lab: Loading Data into BigQuery.
    • Exploring Schemas.
    • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
    • Schema Design.
    • Nested and Repeated Fields.
    • Demo: Nested and repeated fields in BigQuery.
    • Lab: Working with JSON and Array data in BigQuery.
    • Optimizing with Partitioning and Clustering.
    • Demo: Partitioned and Clustered Tables in BigQuery.
    • Preview: Transforming Batch and Streaming Data.
    Module 4
    Introduction to Building Batch Data Pipelines, EL, ELT, ETL
    • Quality considerations.
    • How to carry out operations in BigQuery.
    • Demo: ELT to improve data quality in BigQuery.
    • Shortcomings.
    • ETL to solve data quality issues.
    Module 5
    Executing Spark on Cloud Dataproc
    • The Hadoop ecosystem.
    • Running Hadoop on Cloud Dataproc.
    • GCS instead of HDFS.
    • Optimizing Dataproc.
    • Lab: Running Apache Spark jobs on Cloud Dataproc.
    Module 6
    Serverless Data Processing with Cloud Dataflow
    • Cloud Dataflow
    • Why customers value Dataflow.
    • Dataflow Pipelines.
    • Lab: A Simple Dataflow Pipeline (Python/Java).
    • Lab: MapReduce in Dataflow (Python/Java).
    • Lab: Side Inputs (Python/Java).
    • Dataflow Templates.
    • Dataflow SQL.
    Module 7
    Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
    • Building Batch Data Pipelines visually with Cloud Data Fusion.
    • Components.
    • UI Overview.
    • Building a Pipeline.
    • Exploring Data using Wrangler.
    • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
    • Orchestrating work between GCP services with Cloud Composer.
    • Apache Airflow Environment.
    • DAGs and Operators.
    • Workflow Scheduling.
    • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
    • Monitoring and Logging.
    • Lab: An Introduction to Cloud Composer.
    Module 8
    Introduction to Processing Streaming Data
    • Processing Streaming Data.
    Module 9
    Serverless Messaging with Cloud Pub/Sub
    • Cloud Pub/Sub.
    • Lab: Publish Streaming Data into Pub/Sub.
    Module 10
    Cloud Dataflow Streaming Features
    • Cloud Dataflow Streaming Features.
    • Lab: Streaming Data Pipelines.
    Module 11
    High-Throughput BigQuery and Bigtable Streaming Features
    • BigQuery Streaming Features.
    • Lab: Streaming Analytics and Dashboards.
    • Cloud Bigtable.
    • Lab: Streaming Data Pipelines into Bigtable.
    Module 12
    Advanced BigQuery Functionality and Performance
    • Analytic Window Functions.
    • Using With Clauses.
    • GIS Functions.
    • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
    • Performance Considerations.
    • Lab: Optimizing your BigQuery Queries for Performance.
    • Optional Lab: Creating Date-Partitioned Tables in BigQuery.
    Module 13
    Introduction to Analytics and AI
    • What is AI?.
    • From Ad-hoc Data Analysis to Data Driven Decisions.
    • Options for ML models on GCP.
    Module 14
    Prebuilt ML model APIs for Unstructured Data
    • Unstructured Data is Hard.
    • ML APIs for Enriching Data.
    • Lab: Using the Natural Language API to Classify Unstructured Text.
    Module 15
    Big Data Analytics with Cloud AI Platform Notebooks
    • What’s a Notebook?
    • BigQuery Magic and Ties to Pandas.
    • Lab: BigQuery in Jupyter Labs on AI Platform.
    Module 16
    Production ML Pipelines with Kubeflow
    • Ways to do ML on GCP.
    • Kubeflow.
    • AI Hub.
    • Lab: Running AI models on Kubeflow.
    Module 17
    Custom Model building with SQL in BigQuery ML
    • BigQuery ML for Quick Model Building.
    • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
    • Supported Models.
    • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
    • Lab Option 2: Movie Recommendations in BigQuery ML.
    Module 18
    Custom Model building with Cloud AutoML
    • Why Auto ML
    • Auto ML Vision.
    • Auto ML NLP.
    • Auto ML Tables.

    Prerequisites

    To get the most of out of this course, participants should have:

    • 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 as Python. Familiarity with Machine Learning and/or statistics.

    Download Brochure

    Join Google Certified Data Engineer Training and gain the knowledge and skills you need to advance your career and provide training to support your preparation for the industry-recognized Google Cloud Professional Data Engineer certification. Download the brochure and check the different focus areas that are covered within these three days of training.

    Download Brochure

    Certification Assessment

    Data Engineers should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

    • High Success rate
    • Join Our Dynamic Community
    • Training from Recognized Trainer
    • Post-workshop support by the Coaches

    Testimonials

    Our clients praise us for our great results, personable service, expert knowledge, and on-time delivery. Here are what just a few of them had to say:

    Training FAQ's

    Is this training conducted as an online/ virtual course?

    As a response to the COVID-19, we have moved our classes online. Our Live Virtual format delivers the same benefits as our face-to-face training: expert instruction, hands-on labs and exercises, peer-to-peer collaboration, and high-quality instructional material.

    If I cancel the enrollment, will I get a refund?

    Cancellation requests received within 24 hours of registration would be offered a full refund (minus payment gateway charges), please reach out to our support team through drop a refund request to enquiry@agilitics.sg. Visit our page for more details about Cancellation & Refund Policy.

    What Payment Options Are Available?

    The process of enrolling for this classroom training is simple. The payment can be made through different options by using a debit/credit card which includes MasterCard, Visa Card, American Express or through PayPal. Acknowledgment will be issued automatically via email to the candidates once payment is done.

    What are the modes of payment available for payment of accreditation fees?

    Payment can be made via credit card, debit card, UPI, and internet banking.

    Have more doubts?

    Please send in an email to enquiry@agilitics.sg, and we will answer any queries you may have!

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