Data Engineering on Google Cloud Platform

صمّم أنظمة معالجة البيانات على Google Cloud وابنِها. خلال أربعة أيام تبني بحيرات البيانات على Cloud Storage ومستودعات البيانات في BigQuery

4 أياممتوسط18 وحدة

سجّل أو استفسر ←اسأل عبر واتساب

نظرة عامة

صمّم أنظمة معالجة البيانات على Google Cloud وابنِها. خلال أربعة أيام تبني بحيرات البيانات على Cloud Storage ومستودعات البيانات في BigQuery، وتنشئ خطوط معالجة دفعية ومتدفّقة بـ Dataflow وDataproc وPub/Sub، وتدير الخطوط وتنسّقها.

ما ستتعلّمه

المحتوى التقني أدناه معروضٌ باللغة الإنجليزية كما ورد في منهاج الدورة، حفاظاً على دقّة المصطلحات.

Design and implement scalable data pipelines on Google Cloud
Analyze massive datasets using BigQuery, SQL, and machine learning
Build both batch data and streaming pipelines with Dataflow and Pub/Sub
Use Dataproc and Spark to manage big data workloads efficiently
Automate and deploy AI workflows using BigQuery ML, AutoML, and Kubeflow

المنهاج

18 وحدة

Define the role of a data engineer on GCP
Explore challenges in data processing and pipeline development
Get started with BigQuery and its capabilities
Compare data lakes and data warehouse models
Hands-on lab: Analyze data using BigQuery

Understand architecture for data lakes on Google Cloud
Store structured and unstructured data in Cloud Storage
Optimize with tiered storage and Cloud Functions
Secure and manage data access
Hands-on lab: Load taxi data into Cloud SQL

Learn modern data warehouse architecture
Perform advanced queries in BigQuery
Use schemas, arrays, and nested fields
Optimize partitioning and performance
Hands-on lab: Work with JSON and BigQuery

Compare ETL, ELT, and EL processes
Improve data quality with built-in tools
Execute batch data operations in BigQuery
Demo: Improve pipeline quality using ELT

Explore Hadoop vs. Dataproc
Migrate from HDFS to GCS
Tune Spark clusters for performance
Run big data jobs using Apache Spark
Hands-on lab: Spark processing on Cloud Dataproc

Build Dataflow pipelines for batch and streaming
Use templates, side inputs, and autoscaling
Hands-on lab: Build and run Dataflow pipelines

Create visual pipelines in Data Fusion
Use Cloud Composer and Apache Airflow
Schedule and monitor DAGs
Hands-on lab: Orchestrate a data pipeline

Understand streaming vs. batch processing
Identify tools and use cases for real-time analytics

Use Cloud Pub/Sub for streaming messaging
Understand architecture and security controls
Hands-on lab: Stream data to Pub/Sub

Expand pipelines to support streaming use cases
Monitor and troubleshoot live streams
Hands-on lab: Real-time data processing

Ingest live data into BigQuery
Analyze patterns using dashboards
Leverage Cloud Bigtable for fast I/O
Hands-on lab: Build a streaming pipeline

Use advanced SQL and GIS features
Tune complex queries for efficiency
Optional: Partition tables by date

Understand AI in analytics workflows
Compare machine learning tools in GCP
Prepare data for model development

Use Natural Language API and Vision API
Hands-on lab: Analyze unstructured text

Use Jupyter notebooks in Google Cloud
Analyze BigQuery data with Pandas
Visualize results with Python
Hands-on lab: Build reports in notebooks

Build scalable ML workflows
Use pipeline templates from AI Hub
Hands-on lab: Train and monitor models

Train models using SQL with BigQuery ML
Compare regression and classification types
Demo: Predict taxi fares using BigQuery ML

Create models using AutoML Tables, Vision, NLP
Evaluate model performance with minimal coding

لمن هذه الدورة

مهندسو البيانات المستعدّون للعمل بالمستوى الاحترافي على Google Cloud.

قبل أن تبدأ

Basic proficiency with a common query language such as SQL.
Experience with data modeling and ETL (extract, transform, load) activities.
Experience with developing applications using a common programming language such as Python.
Familiarity with machine learning and/or statistics.

يوم واحد

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