It is best in cases where you have batch as well as streaming data. Also in some cases where you have batch data right now and in future you will get streaming data. In those cases Dataflow is very good. Also in cases where most of your infra is on GCP. It might not be good when you already are on AWS or Azure. And also you want in-depth control over security and management. Then you can directly use Apache beam over Dataflow.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
As any other archiving solution, it is very well suited for environments with a large footprint of unstructured data (CIFS / NFS shares for user data) with a large amount of unused/old files and a need to keep those unused files for long term. In our scenario, due to some legal and contractual constraints we need to keep these files for 15 years. Archiving is a good choice to move the unused files to a cheaper storage tier, both on-prem or cloud.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
More templates for Bigquery and App Engine. There is only limited options for templates so the things we use can limit.
I would like native connectors for Excel (XLSX) to reduce the need for custom wrappers in financial pipelines.
Debugging Google Cloud Dataflow using only logs in Cloud Logging can be overwhelming sometimes, and it’s not always obvious which specific element in the flow caused a failure. IT uses a lot of time.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
It really saved a lot of time and it's flexibility really can give you infra which is future-proof for most of the use cases may it be streaming or batch data. And with this you can avoid use of resource-heavy big data offerings.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
We have used Veritas Enterprise Vault in the past, and besides its being a well-known player on the data archiving market, their tool is far more complex to implement, to manage and to keep working. Komprise is very robust and also very easy to implement, as most part of the job is done on Komprise side. The management console is delivered through a public URL as a SaaS platform. You only need to deploy a few VMs for scan/archiving/user access, which they call "Observer VMs." Komprise also doesn't uses Stub files, which is a poor implementation adopted by the competitor for file access. We had a lot of issues in the past with stub files. Komprise has implemented 'bread crumbs', which are CIFS symlinks to the files on the Observer. It is a very good implementation and it works really well.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
