Context
Let’s imagine that we are working in an automotive company that collects telemetric data from their cars. A part of this data needs to be processed, stored and managed.
To be able to store data now and use it later on in time, you decided to go with Azure Data Lake, that is not limited on how much data you can store and allows you to plug any kind of processing system.
Requirements
After the architecture audit, because of legal constraints you are required to have a resiliency policy for disaster recovery. Even if in the same Azure Region there are 3 copies of data that are generated by Azure Data Lake, the legal constraints require you to have a resiliency policy.
Problem
Azure Data Lake makes 3 copies of data in the same Azure Region, but there is no support to replicated or backup content in a different Azure Region. You will need to define you own mechanism for this.
Available Solutions
We could fine many ways of doing this. There are 2-3 mechanism to do replication of Azure Data Lake cross region that is cost efficient and also not too complex from implementation point of view.
Azure Data Factory
One of the basic features of Azure Data Factory is support for copy activities. This is a generic service that allows us to move data from different types of storages. From binary content point of view, the current copy activities supported by Azure Data Factory offers us speeds up to 1 GBps for Azure Blob Storage and Azure Data Lake.
AdlCopy
This command can be used to move data from one Azure Data Lake to another. The command can be used with success as long as we integrate it in Azure Automation. This is required, because the command cannot run by its own. Azure Automation is the shell from where we run the command.
Downside of this solutions
This solution works well for small amount of data and when we don’t care about the deltas. If we can identify deltas by our self or we don’t care about it and we want to replicate everything than the previous two solutions are perfect.
If the process of delta identifications is too complex that we should go with Distcp command
Distcp
This can be used in combination with HDInsight to copy content from one storage to another. In comparison with the previous ones, it enables us to copy only the delta. Beside this, this command runs on top of a HDInsight (Hadoop) cluster, allowing it to scale inside the cluster based on how much traffic needs to be moved.
What’s suite our need?
Telemetric data collected from cars are grouped together per car and day and never updated. This means that we can identify easily what files need to be moved. Each car has a virtual folder with his own telemetric data. A new file is created each day. When a new car is sold, a new virtual folder is created.
This allows us to identify easily what files needs to be replicated and we don’t need to care about deltas, because we run the replication job only one time per day.
Taking this into account, Azure Data Factory is a good candidate.
Conclusion
As can see, there are multiple ways to do replication between different instances of Azure Data Factory. Even if Distcp is the best one, you need to take into account that you need to spin HDInsight clusters, that are more expensive that AdlCopy or Azure Data Factory.
When you handle storages where content is updated or copies rarely, then a solution based on Distcp might be too expensive and complex.
Let’s imagine that we are working in an automotive company that collects telemetric data from their cars. A part of this data needs to be processed, stored and managed.
To be able to store data now and use it later on in time, you decided to go with Azure Data Lake, that is not limited on how much data you can store and allows you to plug any kind of processing system.
Requirements
After the architecture audit, because of legal constraints you are required to have a resiliency policy for disaster recovery. Even if in the same Azure Region there are 3 copies of data that are generated by Azure Data Lake, the legal constraints require you to have a resiliency policy.
Problem
Azure Data Lake makes 3 copies of data in the same Azure Region, but there is no support to replicated or backup content in a different Azure Region. You will need to define you own mechanism for this.
Available Solutions
We could fine many ways of doing this. There are 2-3 mechanism to do replication of Azure Data Lake cross region that is cost efficient and also not too complex from implementation point of view.
Azure Data Factory
One of the basic features of Azure Data Factory is support for copy activities. This is a generic service that allows us to move data from different types of storages. From binary content point of view, the current copy activities supported by Azure Data Factory offers us speeds up to 1 GBps for Azure Blob Storage and Azure Data Lake.
AdlCopy
This command can be used to move data from one Azure Data Lake to another. The command can be used with success as long as we integrate it in Azure Automation. This is required, because the command cannot run by its own. Azure Automation is the shell from where we run the command.
Downside of this solutions
This solution works well for small amount of data and when we don’t care about the deltas. If we can identify deltas by our self or we don’t care about it and we want to replicate everything than the previous two solutions are perfect.
If the process of delta identifications is too complex that we should go with Distcp command
Distcp
This can be used in combination with HDInsight to copy content from one storage to another. In comparison with the previous ones, it enables us to copy only the delta. Beside this, this command runs on top of a HDInsight (Hadoop) cluster, allowing it to scale inside the cluster based on how much traffic needs to be moved.
What’s suite our need?
Telemetric data collected from cars are grouped together per car and day and never updated. This means that we can identify easily what files need to be moved. Each car has a virtual folder with his own telemetric data. A new file is created each day. When a new car is sold, a new virtual folder is created.
This allows us to identify easily what files needs to be replicated and we don’t need to care about deltas, because we run the replication job only one time per day.
Taking this into account, Azure Data Factory is a good candidate.
Conclusion
As can see, there are multiple ways to do replication between different instances of Azure Data Factory. Even if Distcp is the best one, you need to take into account that you need to spin HDInsight clusters, that are more expensive that AdlCopy or Azure Data Factory.
When you handle storages where content is updated or copies rarely, then a solution based on Distcp might be too expensive and complex.
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