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.
Học phí: 18,800,000 VND
After completing the course, students will have the following knowledge:
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.
Video Demo: Querying Cloud SQL from BigQuery.
Lab: Loading Data into BigQuery.
Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
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.
How to carry out operations in BigQuery.
Demo: ELT to improve data quality in BigQuery.
ETL to solve data quality issues.
Executing Spark on Cloud Dataproc
The Hadoop ecosystem.
Running Hadoop on Cloud Dataproc.
GCS instead of HDFS.
Lab: Running Apache Spark jobs on Cloud Dataproc.
Serverless Data Processing with Cloud Dataflow
Why customers value Dataflow.
Lab: A Simple Dataflow Pipeline (Python/Java).
Lab: MapReduce in Dataflow (Python/Java).
Lab: Side Inputs (Python/Java).
Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
Building Batch Data Pipelines visually with Cloud Data Fusion.
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.
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
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.
Lab: Streaming Data Pipelines into Bigtable.
Advanced BigQuery Functionality and Performance
Analytic Window Functions.
Using With Clauses.
Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
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.
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.
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.
Google Cloud expert
Cloud Ace Training
Bringing great experiences to students
Trần Tuấn Anh
Nguyễn Ngọc Minh Thy
Trương Quốc Thắng
Phạm Văn Hùng
Dương Minh Phương
TO BECOME " GOOGLE CLOUD EXPERT"
câu hỏi thường gặp
Cloud Ace is a Google Cloud training unit, so it does not organize exams and provide Google Cloud certifications. Cloud Ace only supports providing certificates of course completion for students while waiting for the Google Cloud certification exam
In addition, if you want to take the Google Cloud certification exam, Cloud Ace will guide you to register for the Online or Offline exam at the authorized Google Cloud test centers in Vietnam.
Of course, during the learning process, you will constantly be solving quizzes, simulated mock tests that are similar to Google Cloud's actual exam questions. In addition, Cloud Ace also provides Dump questions that are constantly updated with question types, exam questions from Google Cloud to help you have the best preparation for the exam.
Of course. You will be supported by Cloud Ace during the learning process and even at the end of the course. You can interact with the Trainer via Slack, email hoặc qua Group Google Cloud Plartform User HCM để được các Trainer hỗ trợ nhé.
After completing the course, if you have any questions about the knowledge or have difficulties in implementing the project on Google Cloud, you can contact the Trainer for answers.
The Google Cloud course is not only suitable for software engineers or system development engineers, but also suitable for data processing engineers such as Data Analytics, Data Engineer, Data Scientist.
In addition, if you are a Marketer or working in the field of finance, banking, e-commerce, logistics .... constantly faced with big data to solve, then you can refer to the courses Big Data Machine Learning Fundamental or From Data to Insight on Google Cloud Platform courses to refer to simple data processing and create professional reports on Google Cloud.