Google Cloud Platform Solutions: Data, AI, and Infrastructure Best Practices

BigQuery for Predictive Modeling & Geospatial Data

• Petabyte-scale data warehousing
• GeoJSON and geospatial processing
• Predictive modeling capabilities

IoT Data Processing with Kafka & Dataflow Alerts

• Kafka I/O as input stream
• Dataflow for stream ingestion and windowed processing
• Alerting if moving average falls below 4,000 messages

Cloud SQL for MySQL: High Availability & Zone Failure

• Utilizing failover replicas
• Deployment in a different zone within the same region
• Read replicas for high-scaling scenarios

Kafka for Centralized Data Ingestion & Delivery

• Ability to manage offsets within topics
• Publish/subscribe across multiple topics
• Retaining key ordering and messages for extended periods

Apache Hadoop Migration to Google Cloud Platform

• Cost-effective storage using Persistent Disks
• Leveraging 50% Preemptible Workers for cost savings
• Google Cloud Storage (GCS) for data storage
• Utilizing Dataproc clusters

Improving AUC Score: Strategies for Model Optimization

• Employing hyperparameter tuning

Dataproc Cluster Security: Managing Dependencies Offline

• Moving dependencies to Google Cloud Storage (GCS)
• Ensuring GCS is within a VPC Service Controls perimeter

Scalable, Transactional SQL Database for 6TB Workloads

• Solution: Cloud Spanner

On-Premise to GCP Database Migration: 20TB OLTP

• Solution: Cloud SQL

Database for Collecting CPU and Memory Statistics

• Bigtable with a narrow table design
• Row Key structure: <comp_engine, comp_id, comp_timestamp>
• Data collected every second

GCS Data Security: Implementing a "Trust No One" Policy

• Using gcloud kms for symmetric key management
• Encrypting files with KMS keys and unique Additional Authenticated Data (AAD)
• Uploading to GCS using gsutil cp
• Storing AAD outside of Google Cloud Platform

Managed Services for Performance & Failure Alerting

• Cloud Monitoring with robust alerting policies

Data Structuring for BQ ML Linear Regression: State, City

• One-hot encoding using SQL (avoiding Data Fusion)
• Representing states as rows
• Representing cities as columns
• Using 1 or 0 for encoding values

ACID-Compliant SQL Database for Bank Transactions

• Cloud Spanner, offering ACID compliance and lock read-write transactions for consistency and conflict resolution
• Important: Avoid stale reads, as they can provide delayed data for critical bank transactions

BigQuery Query Performance & Partition Restructuring

• Transitioning from ingest-date partitioning to per-item ID partitioning for improved performance