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Google Cloud Professional Data Engineer (GCP-PDE) Certification with Practice Exam
01 Introduction to data engineering
02 Introduction to BigQuery (2:47)
01 Data Engineer role (10:11)
03 Data Lakes and Warehouses (3:06)
04 Transactional Databases vs Data Warehouses (2:55)
05 Manage Data Access and Production (2:20)
02 Building a data lake
01 Data storage and ETL options on Google Cloud (2:52)
02 Building a Data Lake with Cloud Storage (10:17)
03 Secure Cloud Storage (5:56)
04 Storing different data types (5:50)
05 Cloud SQL as a relational Data Lake (5:36)
03 Building a data warehouse
01 Building a Data Warehouse (5:38)
02 BigQuery for Data Warehouses (9:29)
03 Load data into BigQuery (9:29)
04 Schema design in BigQuery (3:40)
05 Example case scenario - Nested and repeated fields (6:43)
06 Optimize with Partitioning and Clustering (5:45)
04 Managing and Securing Dataflow Operations in Google Cloud with Dataflow
01 Beam Portability (4:47)
02 Separate Compute and Storage with Dataflow (4:20)
03 Dataflow Identity Access Management (4:40)
04 Google Cloud Dataflow Quotas (4:50)
05 Enhance security while running Dataflow (2:43)
06 Dataflow security features (4:14)
05 Essentials of Data Integration Techniques - From ETL to BigQuery Optimization
01 EL, ELT and ETL (4:12)
02 Build BigQuery Operations (3:09)
03 Using E-T-L (2:37)
04 Solve data quality issues with ETL (5:29)
06 Optimize Big Data Processing with Hadoop and Spark on Dataproc
02 Run Hadoop on Dataproc (8:00)
01 From Traditional Databases to Hadoop and Spark Ecosystems (5:06)
03 Cloud Storage vs Hadoop (5:20)
04 Optimize Dataproc (3:00)
05 Optimize Dataproc Storage (7:32)
06 Optimize Dataproc templates and autoscaling (4:24)
07 Optimize Dataproc monitoring (2:27)
07 Building Robust Data Processing Pipelines with Dataflow
02 How Dataflow works (3:20)
01 Using Dataflow (6:03)
03 Creating Dataflow pipelines with Python programming (5:07)
04 Designing a Dataflow pipeline (3:09)
05 Transform data with PTransforms (9:20)
06 Side inputs and windows of data (4:12)
07 Creating and re-using pipeline templates (3:49)
LAB - 01 Designing data processing systems
01.02 Data Encryption and Key Management (9:53)
01 01 Working with Identity and Access Management (IAM) (11:56)
01.03 Encrypt Data Uing Cutomer Managed Key (7:12)
01.04 Load data from Cloud Storage into BigQuery (6:51)
01.05 Run Queries in BigQuery (7:26)
Source Files
LAB - 02 Working with Dataprep
02.01 Data Cleaning and Validation using Dataprep (5:54)
02.02 Uing Dataprep to validate files from Cloud Storage (4:32)
02.03 Creating Plans in DataPrep (5:44)
Source Files
LAB - 03 Data Discovery and Inspection using Data Loss Prevention
03.01 Create a template in Data Loss Prevention (6:54)
03.02 Create scan configuration in DLP (3:11)
03.03 Create inspection job in Data loss prevention (5:07)
03.04 Visit Dashboard in Data Loss Prevention (3:22)
Source Files
LAB - 04 Work with Data Catalog, BigQuery, and Cloud Run
04.02 Perform data transfer using BigQuery (6:19)
04.01 Tag Dataet in Data Catalog (10:10)
04.03 Create a delivery pipeline (6:23)
04.04 Create a release in delivery pipeline (4:50)
04.05 Verify the release in Cloud Run (7:06)
04.06 Create a servoce in Cloud Run (4:11)
04.07 Connect to Github repository (2:48)
Source Files
LAB - 05 Working with Dataflow
05.01 Create a pipeline in dataflow (10:53)
05.02 Create a job in dataflow (6:36)
05.03 Work with SQL Workbench in Dataflow (8:59)
Source Files
LAB - 06 Create and work with Virtual Private Cloud (VPC)
06.01 Work with Virtual Private Cloud (VPC) (8:35)
06.02 Create a Virtual Private Cloud (3:47)
06.03 Create a VPC Network Peering (6:05)
Source Files
LAB - 07 Working with Cloud Composer
07.01 Create Cloud Composer Environment (6:26)
07.02 Accessing the Web Interface from the Google Cloud Console (3:51)
07.03 Get teh details of your Composer environment (6:51)
07.04 Get the Database Connection Parameters (2:00)
07.05 Get the Database Endpoint Address (2:30)
07.06 Work with DAGs (3:43)
Source Files
LAB - 08 Working with Cloud Datastore
08.02 View the database properties using the cloud shell (3:37)
08.01 Create a database using Cloud Datastore (4:43)
08.03 Store data in the database in Cloud Datastore (8:37)
08.04 View an entity in a database (1:48)
08.05 Run a query (3:02)
Source Files
LAB 10 - 10 Creating a SQL instance
10.02 Create a MySQL database on the instance (2:03)
10.01 Create an instance in Cloud SQL (11:18)
10.03 Create a table (4:24)
10.04 Import data into the table (4:07)
10.05 Create Views on the table (3:42)
Source Files
11 Work with Spanner
11.01 Work with Cloud Spanner (5:34)
11.02 Create a database in Spanner (5:48)
11.03 Create a table in Spanner (2:40)
11.04 Add data into a table in Spanner (3:49)
11.05 Update table schema (2:12)
11.06 Create an index (2:24)
Source Files
12 Work with Memorystore
12.02 View instance information (4:33)
12.01 Working with MemoryStore (8:30)
12.03 Edit an instance (2:14)
12.04 Delete an instance (1:36)
Source Files
13 Work with Pub-sub
13.01 Create a topic in pub_sub (5:09)
13.02 Create a subscription (6:44)
Source Files
14 Work with Cloud Data Fusion and Dataproc
14.01 Work with Cloud Data Fusion (9:55)
14.02 Required Custom roles and permissions (4:06)
14.03 Work with the data pipeline (9:45)
14.04 Role of Dataproc ith Data Fusion (5:43)
Source Files
Test Your Knowledge
Practice Exam
03 Creating Dataflow pipelines with Python programming
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