Managed Spark - Databricks

Managed Spark

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Source Databricks

A managed Spark service lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. By using such an automation you will be able to quickly create clusters on -demand, manage them with ease and turn them off when the task is complete.

Users can also size clusters according to the workload, performance requirements or based on the existing resources. Furthermore, you will be granted access to fully managed Spark clusters that you can dynamically scale up and down in just a few seconds.  and this can be done even while jobs are processing. In addition, users will be able to turn off clusters when they no longer need them, hence saving money.

Managed Spark providers create temporal clusters instead of making provisions and retaining a cluster for all your jobs. Typically they use a cluster of machines with a master node and workers. Organizations can concentrate on extracting value out of their data instead of spending their valuable resources on operations.

Main advantages of using a Managed Spark service are:

Automated Cluster Management

Managed deployment, logging, and monitoring according to the needs of your particular job let you focus on your data, instead of focusing on the cluster. Your clusters will be stable, scalable, and fast..

Resizable Clusters

Building and configuring Spark clusters is resource-intensive, however this is no longer of your concer as clusters can be created and scaled quickly. nodes are wind down when they’re no longer needed. Everything is done on an as-needed basis

Developer Tools

Usually there are provided multiple ways to manage a cluster.

Automatic or Manual Configuration

Hardware and software on clusters is automatically configured for you while also allowing for manual control.

Simplicity of management 

You will no longer have to stress out on managing the cluster or resource allocation and make any prioritisation through tools such as YARN resource manager.

Cost effective

Users only pay for the compute resources that are consumed during the process.

 

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