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.

If an organization plans to move to the Google Cloud Platform (GCP) from a Cloud migration point of view, Dataproc offers the same features. However, it offers additional paradigms, which is the separation of Google compute engine and cloud storage. 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.

So, Cloud Dataproc is a significant part of Google’s Big Data product portfolio. It is available as an open beta version too. Moreover, it is designed with excellent features to speed up, simplify, and manage the clusters efficiently. Consequently, multinational enterprises using big data technologies can get the best out of the rapidly growing technology world.

Cloud Dataproc brings the best of Big data open-source technology available today into Google Cloud Platform (GCP) for the users.

What is Google Cloud Dataproc?

Dataproc is a managed Hadoop and Spark service that allows users to take advantage of open-source data tools. It supports batch processing, querying, machine learning, and streaming. Dataproc automation enables quick clusters set up, manages them easily, and saves money as it turns off the clusters when you don’t need them.

Cloud Dataproc is a complete platform from Google for data processing, Big data analytics, and machine learning. Cloud Dataproc by Google is the product that simplifies and economizes the clusters running the Apache Hadoop and Apache Spark ecosystem. The speed of creation of clusters increases using this, which leads to saving up a lot on per-second billing, so you only need to pay for exactly the resources you consume. Also, Dataproc offers frequently updated and native versions of Apache Spark, Hadoop, Pig, and Hive, and other related applications. Thus using Cloud Dataproc, the companies can quickly create clusters. Also, they can easily turn them off in case they are not required. Cloud Dataproc is a fully managed cloud service that integrates with Google Cloud Platform (GCP) services. Furthermore, it is a platform for fast and scalable data processing, machine learning, and analytical activities.

Cloud Dataproc provides frequent updates to native versions of Hadoop, Spark, Pig, and Hive. Thus the ease of implementation and operation of Spark and Hadoop clusters is of real advantage to the enterprises. Cloud Dataproc clusters also support Identity Access Management (IAM) that can control access rights. This allows keeping a check on who can do what using the service. The security issues hence are taken care of this. The IAM is a granular offering and permissions for the customers who want to secure one cluster to be used by one group only.

Additionally, Cloud Dataproc 1.4 brings the following:

-Apache Spark 2.4

-Python 3/ Miniconda 3

-Apache Flink 1.3

– CLI default of 1T disk size

-Supports Ubunty18

– Kerberos security component unlocks includes the ability to enable Hadoop secure mode with a single checkbox action

Some of the features of DataProc:

-Enable to perform fully managed deployment, logging, and monitoring. Cloud Dataproc clusters are stable, faster, and scalable.

– Cloud Dataproc allows both manual and automatic configuration of software and hardware.

– Autoscaling feature of Cloud Dataproc makes it happen to add or remove clusters nodes.

– Built-in integration with other Google cloud services like BigQuery, Bigtable, Stackdriver, AI Hub, monitoring, etc.

-Supports high availability

– Familiar with open source tools

-Ensures enterprise security with OS Login, VPC Control service, at-rest encryption etc.

As Cloud Dataproc has become widely popular among enterprises, no doubt, this is an excellent opportunity for Google as well. It will increase its load and customers with higher use. The economies of scale go higher, adding to efficiency. Cloud Dataproc is also shifting from a per-minute billing model to a per-second billing model. This will make it more customers centered.

Gaps in traditional Big Data technology

Though Big data technologies have become a significant part of businesses today, they are not perfect and are not easy to use. The complexity and time requirements also lead to the underachievement of the companies’ results using these technologies. Some of the common gaps and challenges are as follows:

1.Continuous Data growth

The amount of information is doubling every two years. Thus the need to store and analyze all this data can be a real challenge. Most of the data is unstructured, and it becomes even more challenging to analyze and search. This leads to technologies being used like Hadoop, NoSQL databases, analytics software, AI and machine learning, etc.

2.Need for Big Data Talent

With the rise in data and requirements for continuous data analysis, traditional big data technologies require many people. To analyze the data using these technologies, big data professionals are required in a large number. The recruitment and retention of talented Big data professionals is a big challenge today.

3.Deriving insights on time

If the organizations want to use the data for their business objectives, generating insights on time is essential. This means a time pressure on using big data technologies correctly on time. The operational efficiencies have to be achieved with continuous innovation. 

Hadoop came into the market intending to process a large set of data. MapReduce is the component that processes data. Though MapReduce can run workloads on a cluster, however, the model had its limitations. It implies that there is a possibility of staging intermediate results on the disk in the case of complex workloads.

To overcome this disk staging limitation Apache Spark was introduced into the Hadoop framework. With the concept of Resilient Distributed Database (RDD), it allows datasets only on memory. Thus Spark enables iterative processing of workloads without the need for staging data to disk.

This change in architecture, no doubt, offered a performance boost of up to two orders of magnitude on the average case, and soon Spark became the new gold standard of the industry. However, to run Apache Spark workloads, there is a need for a huge setup. If Spark is there in the middle, there are so many other Big Data tools in the framework to keep things in place. This is a troublesome model itself.

There comes Dataproc from Google that leverage this crazy set up easy. As per its sales pitch, Dataproc demands it can set up the entire cluster within 90 seconds, which is really awesome to sound! Isn’t it? It allows Spark jobs to run from the command line using gcloud command. Besides, it supports jobs written in Python or Scala to run.

In addition to that, it allows connectors to other Google Cloud platform products like BigQuery. You can run a machine learning Spark job using it and send the output to BigQuery for further analysis.

So, as a whole, Cloud Dataproc simplifies the deployment of the Big data stack. And within a couple of minutes, it makes ready the cluster for processing workloads. Also, it supports scaling up and down as per the requirements saving a considerable amount of cost for infrastructure upfront.

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 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 to data insight

Cloud Dataproc allows the user to spend much more time to work 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 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 gcloud command. Besides, Cloud Dataproc can run jobs written in Python and Scala.

# Capable to handle huge data processing

In Cloud Dataproc, you can test gigabytes or terabytes of the dataset, dividing them into multiple files. Here the computation happens on robust clusters as they have comprised of more workers nodes and computer resources.

# Connectivity with BigQuery

As mentioned previously, Cloud Dataproc has connectors to other Google Cloud Platform products. BigQuery is one of them. So, you can run a data processing job using Spark, where the output will go to BigQuery for analysis. Similarly, you can use BigQuery data as input to your workloads.

# Simplified deployment of Big data

Google Cloud Platform simplifies the deployment of the Big Data stack. With a few clicks and a couple of minutes, the cluster gets ready to deploy and process the workloads. This is also applicable in terms of scalability, which means you can scale up and down easily. It is a low-cost affair where you don’t need to pay for the upfront infrastructure.

# Addition of optional components

Apache Hadoop ecosystem components are automatically installed with Cloud Dataproc cluster. We can add additional components known as “optional components” on the cluster and it. These optional components can be added as fully pre-configured and support open-source tools.

# Auto-awesomeness

Cloud Dataproc supports Big data open-source softwares, can manage and integrate with them and offers some new capabilities. This is termed as auto-awesomeness. This helps to automate the data workloads of the Apache stack as they move to the cloud.

Why does Google Cloud Platform offer Dataproc for managed Hadoop and Spark, when it already has BigQuery and Dataflow?

This is a business strategy of Google to attract users who are using Hadoop and Spark as open-source Big data tools. Because using Dataproc means users are indirectly using Google Compute Engine and the other products from Cloud Platform. Dataproc is easy to start, run, and manage while users can use popular Big data tools along with it. You don’t have to install, setup Hardware and Software because Google does everything for you.

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 play around 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.

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