Google Cloud Professional Cloud Database Engineer

Configure, import, export, and tune Cloud SQL and other managed services, including high availability and trade-offs across Cloud SQL, Spanner, Bigtable, Firestore, BigQuery, Memorystore, and Cloud Storage.

Leverage Cloud Spanner's globally distributed, strongly consistent, highly available relational database to design scalable regional or multi-regional systems, using hashed primary keys and table interleaving for related data.

Explore Cloud Bigtable, a wide-column NoSQL service for high-volume workloads with sub-10 millisecond latency. Design denormalized tables with row-key driven access, column families, and Dataflow import-export.

Explore Cloud Firestore's flexible document model, real-time reads, and native versus datastore modes, then implement built-in and composite indexes using index.yaml for efficient querying.

Explore BigQuery analytics optimization and cost management through datasets, partitioning, clustering, loading via Cloud Storage and the BigQuery Data Transfer Service, and cost-aware querying.

Master a fully managed Memorystore for sub-millisecond access with Redis and Memcached, and navigate cloud storage classes, bucket management, lifecycle rules, and the trade-offs of unmanaged databases.

This hands-on demonstration guides you through signing in to the Google Cloud Console, creating a dedicated lab project, and configuring billing, budgets, and credits while provisioning and exploring storage services.

Navigate the google cloud console, review project details and billing, then enable APIs such as sqladmin, bigquery, bigtable, bigtableadmin, firestore, and storage via console or cloud shell.

Create a MySQL Cloud SQL instance and configure the instance details, passwords, and storage settings. Evaluate edition presets, single-zone deployment, automatic storage increase, and query insights.

Demonstrates connecting to a Cloud SQL instance via Cloud Shell, creating a dedicated user, and using the MySQL prompt to run DDL, DML, and DQL.

Populate Cloud SQL data by creating a database and table, inserting records, and correcting errors. Run queries with select and group by state in Cloud SQL Studio and Cloud Shell.

Learn to safely stop then delete a Cloud SQL instance to stop compute charges, manage backups, and handle deletion protection with gcloud commands and Cloud Shell.

Create a dataset and table in BigQuery Studio, then query data with the SQL editor in its service-less data warehouse, while noting the free tier and pre-run cost estimates.

Explore Google Bigtable, a wide-column NoSQL database designed for high-velocity time-series workloads like minute-by-minute sensor readings with sub-10 ms reads.

Learn how Firestore's flexible schema and auto-indexed documents enable efficient multi-field queries, with real-time listeners and offline sync, demonstrated through sample user documents and filters.

Demonstrate automated cost optimization in Google Cloud Storage by creating a bucket and applying lifecycle rules from standard to nearline at 30 days and to coldline at 365 days.

Discover data export and import for GCP managed databases with Dataflow across Bigtable, Spanner, and Firestore, plus automation with Cloud Composer and zero-downtime migration patterns, including on-prem HBase to Bigtable.

Master change data capture with data stream to support near-zero and zero-downtime migrations. Learn migration strategies, reverse replication, and step-by-step paths like HBase to Bigtable with Dataflow and sequence files.

Explore AlloyDB architecture in GCP, focusing on HTAP with a columnar engine and vector search, primary and secondary clusters, asynchronous replication, and cross-region disaster recovery options.

Implement private IP connectivity for production databases, evaluating PSA and PSC for cross-VPC access, while enabling managed session pooling, CMEK encryption, and audit logging for compliant, high-performance deployments.

Explore BigQuery federation across Cloud Storage, Cloud Bigtable, Cloud Spanner, and Google Drive to join data without loading, and use data stream CDC for syncing under residency and CMEK constraints.

Learn to migrate databases to Google Cloud with the Database Migration Service, handling homogeneous and heterogeneous migrations, DDL/DML conversion, zero-downtime patterns, and reverse replication with Datastream.

Provision a source MySQL instance in Cloud SQL, enable backups and binary logging, configure public access, and demonstrate an end-to-end migration to a Cloud SQL destination using Database Migration Service.

Demonstrates connecting to a MySQL instance via cloud-sql-proxy and starting the proxy on port 9470 using gcloud sql connect. Explains securing the password with the get-surveypublickey flag.

Create lab-orders-db and populate lab-customers in MySQL. Then create a dms-replication-user with replication, replication-slave, replication-client, and select privileges for DMS; provision destination lab-sql-destination 01-01 with public IP and backups.

Configure DMS migration by creating source and destination connection profiles, then launch a continuous migration job with CDC replication, validate connectivity and binary log settings, and run a test job.

Master two-layer access control with IAM and database-level permissions across Cloud SQL, Cloud Spanner, LODB, and Cloud Bigtable, using predefined roles and IAM database authentication.

Learn backup and point-in-time recovery configurations for cloud sql, including automated backups, pitr retention, rpo/rto targets, and restoring by cloning to a new instance with gcloud.

Discover PITR concepts across Spanner, AlloyDB, and Bigtable, including version retention, continuous backups, automated backups, on-demand backups, and out-of-place restores.

Use Spanner and Bigtable Key Visualizers to spot hotspots and load distribution, then redesign primary keys to distribute writes and optimize indexing across Cloud SQL, Spanner, Bigtable, and Firestore.

Optimize cloud database costs using the Cloud SQL Recommender to identify idle and over-provisioned instances, choose appropriate storage, and apply CPU targets, auto-scaling, and TTL policies across services.

Learn to implement operational automation for Google Cloud databases with scheduled exports, maintenance windows, and database flags, while mastering IAM roles, backups, PITR, and cost optimization.

Demonstrates enabling APIs, creating a Cloud SQL instance, and assigning the CloudSQL Editor IAM role at the project level, explaining the two-layer IAM and database user model.

Enable Cloud SQL IAM authentication, verify the flag, and restart the instance to apply changes. Create an IAM user and grant instance-level and database permissions in the two-layer model.

Explore Cloud SQL backups and point-in-time recovery, set the PITR retention window, create on-demand backups, and perform a point-in-time clone to recover data safely.

Describe how Google Cloud encrypts data at rest using a three-layer model and envelope encryption, and protects data in transit with TLS, QIC, and ALTS.

Explore the three key management options for regulated workloads: Google-managed keys, CMEK via Cloud KMS, and customer-supplied keys. Assess operational responsibility, rotation, audit logging, and the 24-hour destruction window.

Explore sensitive data protection with infotype detectors and de-identification to meet regulatory needs in GCP databases, including HIPAA, COPPA, FedRAMP, and GDPR.

Design scalable Cloud Bigtable keys with field promotion, salting, reverse-domain patterns, and anti-patterns for hotspots; apply cell versioning, row-size guidance, replication consistency models, and app profiles for resilient access.

Explore Cloud Spanner schema design and query optimization, focusing on timestamp data types, splits, hotspots, and best practices for parameterized queries, explain plans, and read-write locking.

Design BigQuery schemas using nested and repeated fields to model one-to-many relationships with the record data type, preserving json/avro nesting and eliminating joins.