How does Google Cloud Dataproc simplifies Big data technology?
Big Data technology has become the most promising technology area with the ongoing interest in data-driven technologies today. Companies like Facebook have been investing huge amounts in Big Data technologies. However, there are certain challenges and gaps while working with traditional Big Data technologies too. To overcome these challenges, tech giant Google has launched its product Cloud Dataproc.
Dataproc allows to lift-and-shift the Hadoop processing jobs to the cloud. At the same time, it stores Big data separately on Cloud Storage buckets. No doubt, such a feature effectively eliminates the requirement to keep the clusters always running.
Functional Overview of Google Cloud Dataproc
Google Cloud Dataproc is built on several open-source platforms, including Apache Hadoop, Apache Pig, Apache Spark, and Apache hive. All of these platforms collectively have a different role at Dataproc.
Apache Hadoop supports aspects of distributed processing of large data sets across different clusters. Apache Spark, on the other hand, is a platform that serves as an engine for large-scale and faster data processing. Apache Pig is implemented for analyzing large data sets and Apache Hive provides a storage facility for data and helps with storage management for SQL databases.
Cloud Dataproc advantages for Hadoop and Apache Spark
Google Cloud Dataproc technology offers many advantages in the complex big data technology stack.
Faster set up of a cluster
The most significant benefit of cloud Dataproc is the speed to set up a cluster. The time taken by Dataproc to create an entire cluster is about 2 to 3 minutes. This is a dramatic reduction in normal speed. This usually takes 30 minutes to use IaaS (Infrastructure as a Service) products.
More time for data insight
Cloud Dataproc allows the user to spend much more time working on their data. In the case of self-managed deployment, you spend time more time on your clusters. Cloud Dataproc helps you to shorten the time window of you asking a question and getting insight.
Fast and automatic add and removal of clusters
Another significant benefit of Cloud Dataproc is that it allows deleting the clusters fastly and its scheduled deletion feature is impressive. It allows you to set the automatic deletion of the cluster after the time given by you. This can be given as an expiration time, maximum age, or Max Idle time. Hence you will not have to delete the clusters manually.
Runs multiple jobs related to the Big data ecosystem
Cloud Dataproc has the capability of running multiple types of jobs, including:
- Hadoop
- Spark
- SparkSql
- Pig
- SparkR
- PySpark
- Hive
You can run Spark jobs from the command line using the G-cloud command. Besides, Cloud Dataproc can run jobs written in Python and Scala.
Different Kinds of Workflow Templates in Dataproc
Dataproc includes various workflow templates that allow users to perform various tasks workably. The different kinds of workflow templates in Dataproc are:
1. Managed cluster
The managed cluster workflow template allows you to create a short-lived cluster to run on-demand or set tasks. And you can easily delete the cluster after the workflow is finished.
2. Cluster Selector
This workflow template specifies any existing clusters on which the workflow jobs can run after specifying user labels. The workflow then intends to run through the clusters that match all other specified labels. Multiple clusters match the labels in this workflow instance, then Dataproc will choose the one with the most available YARN memory to run the workflow tasks. And at the end of completing the workflow task, the cluster is not removed. To learn more about how to use cluster selectors with different workflows, check out this official documentation!
3. Inline
This type of workflow template intends to instantiate workflows using the G-cloud command. For the same, you can use YAML files or call Dataproc’s Instantiate Inline API. Embedded work to create or edit workflow template resources! If you need more ideas on using Dataproc inline workflows, then here is the official documentation to enlighten you on the necessary knowledge.
4. Parameterized
This workflow template allows you to perform different values multiple times. And in the process, you can avoid repeatedly modifying the template for multiple runs by setting the parameters in this template. And with this parameter, you can intend to pass different values to the template for each run.
Using workflow templates is of the utmost importance. Workflow templates are used to find automation for specific repetitive tasks. These templates will narrow down frequent task executions or configurations within the workflow and automate the process. In addition, Workflow templates offer support for long-lived and short-lived clusters. The managed cluster template is for a short-lived cluster, while the Cluster Selector template is for a long-lived cluster.
Conclusion
Google Cloud Dataproc offers many benefits over traditional big data technologies and cloud services. With high speed and simplified working, Dataproc will cover the gaps in complex Big data technologies. This will be an economical service for enterprises.
Also, Dataproc will take care of the challenges of Hadoop and Apache Spark. This includes massive data management, integration of data sources, and timeliness. Thus, when it is about playing around with Big data, Google Cloud Dataproc is the way to go without paying high entry costs. It allows any company to have access to the disruptive power of Big Data.
