-
This four-day, expert-led course provides you with a hands-on introduction to developing and implementing data processing systems on the Google Cloud Platform. Through a mix of presentations, live demos and interactive labs, you will learn how to design data processing systems, create end-to-end data pipelines, analyze data and apply machine learning. The focus is on structured, unstructured and streamed data to give you a comprehensive understanding of modern data processing.
-
Course Contents
-
- Introduction to Data Engineering
- Building a Data Lake
- Building a Data Warehouse
- Introduction to Building Batch Data Pipelines
- Executing Spark on Cloud Dataproc
- Serverless Data Processing with Cloud Dataflow
- Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
- Introduction to Processing Streaming Data
- Serverless Messaging with Cloud Pub/Sub
- Cloud Dataflow Streaming Features
- High-Throughput BigQuery and Bigtable Streaming Features
- Advanced BigQuery Functionality and Performance
- Introduction to Analytics and AI
- Prebuilt ML model APIs for Unstructured Data
- Big Data Analytics with Cloud AI Platform Notebooks
- Production ML Pipelines with Kubeflow
- Custom Model building with SQL in BigQuery ML
- Custom Model building with Cloud AutoML
In this course, you will learn the following skills:
- Develop data processing systems on Google Cloud Platform
- Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
- Derive business insights from extremely large datasets using Google BigQuery
- Train, evaluate, and make predictions with machine learning models using TensorFlow and Cloud ML
- Leverage unstructured data with Spark and ML APIs on Cloud Dataproc
- Enable instant insights from streaming data
Official Google Cloud documentation.
-
Target Group
-
This course is specifically designed for experienced developers who are responsible for big data transformations. This includes tasks such as:
- Extracting, loading, transforming, cleansing and validating data.
- Development of pipelines and architectures for data processing.
- Creation and maintenance of models for machine learning and statistics.
- Query data sets, visualize results and create reports.
-
Knowledge Prerequisites
-
- Basic knowledge of common query languages such as SQL
- Knowledge of data modeling, extraction, transformation and loading activities
- Knowledge of developing applications with a common programming language such as Python
- Familiarity with machine learning and/or statistics
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| Introduction to Processing Streaming Data |
| Processing Streaming Data. |
| Serverless Messaging with Cloud Pub/Sub |
| Cloud Pub/Sub. |
| Lab: Publish Streaming Data into Pub/Sub. |
| Cloud Dataflow Streaming Features |
| Cloud Dataflow Streaming Features. |
| Lab: Streaming Data Pipelines. |
| High-Throughput BigQuery and Bigtable Streaming Features |
| BigQuery Streaming Features. |
| Lab: Streaming Analytics and Dashboards. |
| Cloud Bigtable. |
| Lab: Streaming Data Pipelines into Bigtable. |
| 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. |
| Introduction to Analytics and AI |
| What is AI?. |
| From Ad-hoc Data Analysis to Data Driven Decisions. |
| Options for ML models on GCP. |
| 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. |
| Big Data Analytics with Cloud AI Platform Notebooks |
| Whats a Notebook. |
| BigQuery Magic and Ties to Pandas. |
| Lab: BigQuery in Jupyter Labs on AI Platform. |
| Production ML Pipelines with Kubeflow |
| Ways to do ML on GCP. |
| Kubeflow. |
| AI Hub. |
| Lab: Running AI models on Kubeflow. |
| 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. |
| Custom Model building with Cloud AutoML |
| Why Auto ML? |
| Auto ML Vision. |
| Auto ML NLP. |
| Auto ML Tables. |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
This four-day, expert-led course provides you with a hands-on introduction to developing and implementing data processing systems on the Google Cloud Platform. Through a mix of presentations, live demos and interactive labs, you will learn how to design data processing systems, create end-to-end data pipelines, analyze data and apply machine learning. The focus is on structured, unstructured and streamed data to give you a comprehensive understanding of modern data processing.
-
Course Contents
-
- Introduction to Data Engineering
- Building a Data Lake
- Building a Data Warehouse
- Introduction to Building Batch Data Pipelines
- Executing Spark on Cloud Dataproc
- Serverless Data Processing with Cloud Dataflow
- Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
- Introduction to Processing Streaming Data
- Serverless Messaging with Cloud Pub/Sub
- Cloud Dataflow Streaming Features
- High-Throughput BigQuery and Bigtable Streaming Features
- Advanced BigQuery Functionality and Performance
- Introduction to Analytics and AI
- Prebuilt ML model APIs for Unstructured Data
- Big Data Analytics with Cloud AI Platform Notebooks
- Production ML Pipelines with Kubeflow
- Custom Model building with SQL in BigQuery ML
- Custom Model building with Cloud AutoML
In this course, you will learn the following skills:
- Develop data processing systems on Google Cloud Platform
- Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
- Derive business insights from extremely large datasets using Google BigQuery
- Train, evaluate, and make predictions with machine learning models using TensorFlow and Cloud ML
- Leverage unstructured data with Spark and ML APIs on Cloud Dataproc
- Enable instant insights from streaming data
Official Google Cloud documentation.
-
Target Group
-
This course is specifically designed for experienced developers who are responsible for big data transformations. This includes tasks such as:
- Extracting, loading, transforming, cleansing and validating data.
- Development of pipelines and architectures for data processing.
- Creation and maintenance of models for machine learning and statistics.
- Query data sets, visualize results and create reports.
-
Knowledge Prerequisites
-
- Basic knowledge of common query languages such as SQL
- Knowledge of data modeling, extraction, transformation and loading activities
- Knowledge of developing applications with a common programming language such as Python
- Familiarity with machine learning and/or statistics
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| Introduction to Processing Streaming Data |
| Processing Streaming Data. |
| Serverless Messaging with Cloud Pub/Sub |
| Cloud Pub/Sub. |
| Lab: Publish Streaming Data into Pub/Sub. |
| Cloud Dataflow Streaming Features |
| Cloud Dataflow Streaming Features. |
| Lab: Streaming Data Pipelines. |
| High-Throughput BigQuery and Bigtable Streaming Features |
| BigQuery Streaming Features. |
| Lab: Streaming Analytics and Dashboards. |
| Cloud Bigtable. |
| Lab: Streaming Data Pipelines into Bigtable. |
| 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. |
| Introduction to Analytics and AI |
| What is AI?. |
| From Ad-hoc Data Analysis to Data Driven Decisions. |
| Options for ML models on GCP. |
| 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. |
| Big Data Analytics with Cloud AI Platform Notebooks |
| Whats a Notebook. |
| BigQuery Magic and Ties to Pandas. |
| Lab: BigQuery in Jupyter Labs on AI Platform. |
| Production ML Pipelines with Kubeflow |
| Ways to do ML on GCP. |
| Kubeflow. |
| AI Hub. |
| Lab: Running AI models on Kubeflow. |
| 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. |
| Custom Model building with Cloud AutoML |
| Why Auto ML? |
| Auto ML Vision. |
| Auto ML NLP. |
| Auto ML Tables. |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.