Written by Imran Abdul RaufTechnical 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.
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.
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.
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.
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.
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
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.
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.
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:
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.