Hadoop On-Premise to Google Cloud Migration

Written by Imran Abdul Rauf

Technical Content Writer

Is your business planning a data migration to the cloud while staying within budget and timeline? This is the guide you need.

About 78% of businesses believe that data modernization is the primary factor for migration to establish future business value. And 58% of IT-driven companies plan to move to hybrid cloud actively. As the cloud computing market is expected to reach $832.1 billion by 2025, migrating Apache Hadoop and Spark clusters to the cloud comes with significant benefits.

Why move from Hadoop to Cloud?

  • Lift and shift Hadoop clusters: Users can quickly migrate their systems from Apache Hadoop to the Google Cloud Platform without re-architecting. The cloud platform is a rapid, flexible compute infrastructure as a service, which allows your ideal Hadoop cluster and use existing distribution. As a result, your Hadoop administrators can work on cluster work and not on server procurement and solving tedious hardware issues.
  • Optimize for cloud efficiency: Businesses can cut costs by migrating to GCP-managed Hadoop and Spark services. In addition, you can experiment with new workarounds for data processing in an Apache Hadoop ecosystem. For example, you’re separating compute and storage through Cloud Storage and working with on-demand ephemeral clusters.
  • Modernize your data processing pipeline: The cloud management platform allows businesses to reduce their Hadoop overhead costs and diminish the complications of processing data. Use a serverless option like Cloud Dataflow to manage streaming analytics and data needs in real-time.

Planning your Migration

Planning to migrate your Apache Hadoop clusters to the cloud requires a change in approach. Hadoop is a monolithic distributed storage platform that contains different nodes and servers with their respective storage, memory, and CPU. Resource management is handled via YARN, which ensures that all the workloads obtain their share of computing and work is evenly spread across all the nodes.

Consequently, the system becomes more complicated to manage with time as it demands more administrators to handle all the workloads working in the monolithic cluster. To reduce such administrative complexity, you need to revise your approach to structure your company’s data and jobs.

The most feasible, cost-effective way to migrate from Hadoop to the cloud is to think about small, short-lived clusters primarily created to run specific jobs rather than opting for the same large, multi-purpose clusters.

Sequence for migrating to Google Cloud

  • Step 1— Move your data first: Start with moving your data into cloud storage buckets. Plan to start vigilantly by using backup or archived data to limit the impact of your current Hadoop system.
  • Step 2— Experiment: It would be best to start trying new approaches to experiment with your data. For instance, use a data subset to test and experiment, create a short proof of concept for each job, etc. Also, adjust to Google Cloud and cloud computing paradigms.
  • Step 3— Consider using specialized, ephemeral clusters: Utilize the minor clusters you’ve created and group them with single, closely related jobs. Users need to create clusters every time they require a job and can delete them once they’re done.
  • Step 4— Use Google Cloud tools: The last step is to use any of the available Google cloud tools and inspect the dynamics of your data in a new system.

Transition to an Ephemeral System

The most noticeable shift that teams experience when moving from Hadoop clusters to Google Cloud is working on specialized, ephemeral clusters. For example, users spin a cluster when running a job and delete it after its completion. And the resources needed for the job are active when they’re running, i.e., you only pay for what you use. This approach allows users to cater cluster configurations for each job. In doing so, businesses can reduce resource and cluster administration expenses.

Hadoop infrastructure to ephemeral model

Separate data from computation

Cloud Storage offers a plethora of benefits for users wanting to work on their workflows.

  • HCFS is quicker than HDFS, in most situations, and requires less maintenance

  • Helping teams to seamlessly use their data in association with other Google Cloud tools

  • It’s less expensive than storing the data in HDFS on a Dataproc cluster

  • Using a Hadoop Compatible File System is easy for existing jobs

With your data secured in Cloud Storage, you can now run your jobs on ephemeral Hadoop clusters. Although GC usually is used to store more general-purpose data, in other cases, it becomes more appropriate to move data on other GC products like BigQuery, Cloud Bigtable, etc.

Run jobs on ephemeral clusters

Users can easily create and delete clusters and move to use various ephemeral clusters. This is how this approach is beneficial:

  • Teams use different data cluster configurations for each job that reduces the administrative burden of handling tools.

  • Clusters are free from maintenance as they’re regularly configured every time you use them.

  • Infrastructures don’t require separate maintenance for development, testing, and production.

  • You can scale clusters to fit with a group or individual jobs.

Reduce the lifetime of ephemeral clusters

Ephemeral clusters are used only for each job’s lifetime.

  • Create an appropriately configured cluster

  • Use the job output as per need demands

  • Delete the cluster

  • Run the job which will send the output to Google Cloud or any other persistent location

  • View the logs through Cloud Storage or Cloud Logging

Use persistent clusters (only when required)

Users can create a persistent cluster if they can’t accomplish their work without one. Still, this option is expensive and isn’t preferred if the job can be completed on ephemeral clusters. Users can reduce the cost of a persistent cluster by creating a smaller cluster, mapping the work on that cluster to a small number of jobs, scaling the cluster to a minimum number of nodes, and incorporating more to cater to demand.

Hadoop to Spark

The Hadoop and Spark architectures can be compared through multiple use cases and contexts. These big data analytics tools constitute an entire ecosystem of open-source technologies that create, process, manage, and work through big data sets.

What is Apache Spark?

Apache Spark is an open-source analytics engine that splits large tasks across multiple nodes like Hadoop. However, Spark is comparatively faster than Hadoop as it uses RAM to process data and cache rather than using a file system. This makes the engine capable of handling use cases that Hadoop cannot.

Benefits of the Spark framework as an excellent alternate to Hadoop:

  • Spark supports SQL queries, machine learning, graph processing, and streaming data.
  • The engine is 100x faster than its Hadoop counterpart for smaller queries through disk data storage, in-memory processing, and other workarounds.
  • The framework has easy-to-use APIs for manipulating semi-structured and transforming data purposes.


As you decide to migrate your systems to a modern cloud architecture like the Google Cloud provider or lakehouse architecture, keep in mind the following pointers.

  • Always include the buy-ins of the key stakeholders of the business. This is an equally important business decision as it is a technology decision, and your business associates need to be aware throughout the journey to the end.
  • You can find plenty of skilled resources at Databricks and Royal Cyber. They have carried out such projects through self-made, repeatable best practices, saving clients and their businesses real money and time.

How can Royal Cyber help?

Royal Cyber is a digital analytics service provider helping businesses make smart, data-driven decisions through valuable customer insights.

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