This document contains a list of various topics related to Google Cloud Platform including:
1. Methods for transferring data to GCP such as online transfer, transfer service, and transfer appliance.
2. When to use Google Cloud Shell vs kubectl for managing Kubernetes clusters.
3. High availability options for Microsoft SQL Server on GCP.
4. Using on-premises data for data cleansing or hosting data in GCP.
5. Cloud storage configuration and command options.
6. Differences in latency between BigTable and BigQuery for databases.
7. When to use Dataflow for data processing.
This document contains a list of various topics related to Google Cloud Platform including:
1. Methods for transferring data to GCP such as online transfer, transfer service, and transfer appliance.
2. When to use Google Cloud Shell vs kubectl for managing Kubernetes clusters.
3. High availability options for Microsoft SQL Server on GCP.
4. Using on-premises data for data cleansing or hosting data in GCP.
5. Cloud storage configuration and command options.
6. Differences in latency between BigTable and BigQuery for databases.
7. When to use Dataflow for data processing.
This document contains a list of various topics related to Google Cloud Platform including:
1. Methods for transferring data to GCP such as online transfer, transfer service, and transfer appliance.
2. When to use Google Cloud Shell vs kubectl for managing Kubernetes clusters.
3. High availability options for Microsoft SQL Server on GCP.
4. Using on-premises data for data cleansing or hosting data in GCP.
5. Cloud storage configuration and command options.
6. Differences in latency between BigTable and BigQuery for databases.
7. When to use Dataflow for data processing.
This document contains a list of various topics related to Google Cloud Platform including:
1. Methods for transferring data to GCP such as online transfer, transfer service, and transfer appliance.
2. When to use Google Cloud Shell vs kubectl for managing Kubernetes clusters.
3. High availability options for Microsoft SQL Server on GCP.
4. Using on-premises data for data cleansing or hosting data in GCP.
5. Cloud storage configuration and command options.
6. Differences in latency between BigTable and BigQuery for databases.
7. When to use Dataflow for data processing.
1. Online Transfer Vs Transfer Service Vs Transfer Appliance
• Online Transfer – on premise to GCP • Size of data – Huge Data/ One time transfer to be using transfer Appliance • Remember about dehydration once the data is back in GCP • public data or from other cloud – Transfer service 2. Kubarnates – • When to use Gscloud and when to use Kubctl 3. Microsoft SQl Server HA 1. Need to reply on Windows server HA 4. Data prem- for data cleansing (Can also be used for in house data) 5. Cloud Storage config/command ( Live = false means it’s not active yet) • Storage types 6. Databases – Latency is key between BigTable vs BigQuery 7. When to use Dataflow 8. Cloud CDN – edge network 9. AD Sync – Domain controller 10. Roles – (Billing) – Project Level 11. Deployment manager – How to create image from local VM and create an instance out of it 12. Creating instance group from GCP VM 13. Security – customer supported encryption -Key management system 14. Storage Bucket encryption – command/config 15. Connect to OnPrem DB from GCP 16. Primary/secondary IP range for GCP VPN 17. Managed instance group – multiregional / 18. Zone high availability 19. Global vs Local VPN / Load balancer 20. HTTPS is global load balancer 21. Cloud storage time bound exposure • You want people to load image for 24 hours – outside GCP / Unauthenticated 23. Boto Config 24. Dependencies ~/bin 25. Committed usage(1-3 year) vs Sustained Usage 26. BigQuery billing from separate project then the data 27. Team to get cloud ready • Learning through committed vs sustained • Certification to prove team effectiveness 28. Updating Data Storage Index ( Config) 29. GDPR European Data ( delete from BigTable and Storage after 36 months) 30. PCI & DS – Security testing tool 31. Big Query data Portioning 32. Data loss prevention API 33. Cloud Launcher vs Deployment manager 34. Data Prep vs Data Lab 35. Datastore – Retrieval using identifiers batch retrieval 36. Move python application with dependencies to cloud