What is the difference between Cloud Dataproc and Cloud Dataflow?

In today’s digital world, data has its own importance and ways for extraction or interpretation. Keeping this as a priority, Google Cloud provides data solutions for data processing and storage using its popular services like Cloud Dataproc and Cloud Dataflow. These services are providing solutions to many top organizations to get high performance, low cost, or to transform data. Cloud Dataproc and Cloud Dataflow are both cloud-based data processing services provided by Google Cloud Platform. The primary difference between Cloud Dataproc and Cloud Dataflow is that Dataproc is designed for batch processing of large datasets using Hadoop and Spark, while Dataflow is designed for real-time and batch processing of large datasets using a variety of data processing frameworks, including Apache Beam.

Here is a table explaining the points of differences between Cloud Dataproc and Cloud Dataflow in Table form

In order to get clarity, we will be doing a comparison of both cloud Dataproc and Dataflow by covering its features, uses, and other important testcases. Let’s begin with an overview.

What is Cloud Dataproc?

Dataproc service scales Apache Spark, Apache Flink, Presto, and other open-source tools and frameworks. At a fraction of the cost, this service is also utilized for data lake modernization, ETL, and secure data science with Google Cloud. Dataproc also aids in the modernization of open-source data processing. That is to say, by rotating purpose-built environments on-demand, you can speed up your data and analytics processing.

Further, this service comes with autoscaling, cluster deletion, per-second pricing, integrated security, and options for lowering costs and security risks. Lastly, it has advanced security, compliance, and governance for managing user authorization and authentication using existing Kerberos and Apache Ranger policies or Personal Cluster Authentication.

Features of Cloud Dataproc

1. Completely managed and automated big data open-source software: Dataproc provides managed deployment, logging, and monitoring to help you focus on your data and analytics. Here, you can lower the TCO of Apache Spark management. Furthermore, data scientists and engineers may use common tools like Jupyter and Zeppelin notebooks to interface with Dataproc. Furthermore, Dataproc includes Jobs API and Metastore, with Jobs API allowing for simple integration of massive data processing into bespoke applications. Metastore, on the other hand, eliminates the need to host your own catalogue service.

2. Containerizing Apache Spark jobs with Kubernetes: In Dataproc, you can create your Apache Spark jobs using Dataproc on Kubernetes for using Dataproc with Google Kubernetes Engine (GKE). Using this, you can provide job portability and isolation.

3. Enterprise security combined with Google Cloud: By adding a Security Configuration to a Dataproc cluster, you may enable Hadoop Secure Mode using Kerberos. Default at-rest encryption, OS Login, VPC Service Controls, and customer-managed encryption keys are some of the most often utilized Google Cloud-specific security features with Dataproc (CMEK).

4. Open source with the best of Google Cloud: Dataproc can be applied to cloud-scale datasets by providing access to open-source tools, algorithms, and programming languages. Moreover, it combines with the rest of the Google Cloud analytics, database, and AI ecosystem. Where, data scientists and engineers can build data applications in which they can connect Dataproc to BigQuery, AI Platform, Cloud Spanner, Pub/Sub, or Data Fusion. 

5. Resizable clusters: In Dataproc, you can quickly build and scale clusters using virtual machine types, disk sizes, number of nodes, and networking options.

6. Autoscaling clusters: Dataproc autoscaling offers a procedure for automating cluster resource management. Moreover, it also enables automatic addition and subtraction of cluster workers (nodes).

7. Cloud integrated: Dataproc combines with Cloud Storage, BigQuery, Cloud Bigtable, Cloud Logging, Cloud Monitoring, and AI Hub for providing a fully robust data platform.

8. Versioning: Dataproc comes with image versioning that enables movement between different versions of Apache Spark, Apache Hadoop, and other tools.

9. Highly available

In this, you can run clusters in high availability mode using multiple master nodes and set jobs for restarting on failure to help in ensuring that clusters and jobs are highly available.

10. Cluster scheduled deletion

Here you get the option for avoiding incurring charges for an inactive cluster. That is to say, you can use Dataproc’s scheduled deletion that offers options for deleting a cluster:

• Firstly, after a specified cluster idle period
• Secondly, at a specified future time, or
• Lastly, after a specified time period.

11. Automatic or manual configuration: Dataproc has the ability to automatically configure hardware and software with having the option for accessing via manual control.

12. Developer tools & Initialization actions: Dataproc provides several ways for managing a cluster by offering a simple to use web UI, RESTful APIs, the Cloud SDK, and SSH access. And, you can run initialization actions for installing or customizing the settings and libraries required when your cluster is built.

13. Workflow templates: Dataproc workflow templates offer an easy-to-use procedure for managing and executing workflows. However, a workflow template can be considered as a reusable workflow configuration that specifies a job chart with details for running those jobs.

Use cases for Cloud Dataproc

1. Moving Hadoop and Spark clusters to the cloud

Dataproc is used by enterprises for managing costs and unlocking the power of elastic scale. They are moving their on-premises Apache Hadoop and Spark clusters to Dataproc. However, with Dataproc, enterprises get a completely managed, purpose-built cluster that has the ability for autoscaling in order to support any data or analytics processing job. 

2. Data science on Dataproc

In Dataproc, you can build your ideal data science environment by rotating a purpose-built Dataproc cluster. You can combine open-source software with Google Cloud AI services and GPUs for speeding up machine learning and AI development.  However, the software can be such as Apache Spark, NVIDIA RAPIDS, and Jupyter notebooks.

Cloud Dataproc pricing

Dataproc pricing depends on the size of Dataproc clusters and the duration of the running time. The size of a cluster is dependent on the aggregate number of virtual CPUs (vCPUs) over the entire cluster, including the master and worker nodes. Further, the duration of a cluster refers to the length of time between cluster creation and cluster deletion. Talking about the Dataproc pricing formula, it is: 

$0.010 * # of vCPUs * hourly duration.

You need to be aware that all Dataproc clusters are adjusted in one-second clock-time increments, subject to a one-minute minimum billing, and that the Dataproc is paid by the second. Moreover, the utilization is represented in fractional hours so that second-by-second use can be charged according to hourly rates.

What is Cloud Dataflow?

Google Cloud Data flow service is well-known for unified stream and batch data processing that comes with serverless, fast, and cost-effective features. This service provides clarified streaming data pipeline development with lower data latency. Moreover, it enables teams to focus on programming and removes operational overhead from data engineering workloads. Further, Dataflow has resource autoscaling paired with cost-optimized batch processing abilities for providing virtually limitless capacity for managing workloads without overspending.

Features of Dataflow

1. Autoscaling of resources and dynamic work rebalancing: Using data-aware resource autoscaling, Data Flow services reduce pipeline latency, maximize resource usage, and cut processing costs per data record. In this case, the data inputs are automatically partitioned and continuously rebalanced to balance worker resource usage and lessen the impact of “hotkeys” on pipeline speed.

2. Flexible scheduling and pricing for batch processing: Flexible resource scheduling (FlexRS), a feature of Google Data Flow services, provides a cheaper cost for batch processing with flexibility in task scheduling time. With the intention of retrieving and executing these flexible jobs within a six-hour window, they are added to a queue.

3. Real-time AI patterns: Dataflow’s real-time AI capabilities enable real-time reactions with near-human intelligence to large torrents of events. Using this, customers can create intelligent solutions varying from predictive analytics and anomaly detection to real-time personalization and other advanced analytics use cases.

4. Right fitting : Right fitting builds stage-defined pools of resources that are optimized for lowering resource wastage at every stage.

5. Streaming Engine: Streaming Engine is used for separating compute from state storage and travels parts of pipeline execution out of the worker VMs and into the Dataflow service back end, thus significantly improving autoscaling and data latency.

6. Horizontal autoscaling: Horizontal autoscaling enables the Dataflow service to automatically select the appropriate number of worker instances when conducting an operation. Nevertheless, the Dataflow service dynamically reallocates more or fewer workers during runtime to take into account the requirements of your operation.

7. Vertical autoscaling: Vertical autoscaling works with horizontal autoscaling for scaling workers to best fit the needs of the pipeline.

8. Dataflow Shuffle: Service-based Dataflow Shuffle shifts the shuffle operation, used for grouping and joining data, out of the worker VMs and into the Dataflow service back end for batch pipelines. Further, the batch pipelines scale smoothly without any tuning needed into hundreds of terabytes.

9. Dataflow SQL: Dataflow SQL builds streaming Dataflow pipelines from the BigQuery web UI using SQL skills. Here, you have access to:

• Firstly, join streaming data from Pub/Sub with files in Cloud Storage or tables in BigQuery
• Secondly, write results into BigQuery
• Lastly, create real-time dashboards using Google Sheets or other BI tools.

10. Dataflow templates: Dataflow templates are used for sharing pipelines with team members and over the organization. They also take advantage of many Google-provided templates for implementing useful data processing tasks. Further, this includes Change Data Capture templates and Flex Templates.

11. Inline monitoring: You can easily access task metrics for batch and streaming pipeline troubleshooting with dataflow inline monitoring. You may view step- and worker-level visibility monitoring charts in this.

12. Dataflow VPC Service Controls: Dataflow’s integration with VPC Service Controls offers additional security for the data processing environment by improving the ability for mitigating the risk of data exfiltration.

13. Private IPs: Turning off public IPs can help in securing your data processing infrastructure. However, you can lower the number of public IP addresses consumed against the Google Cloud project quota by not using public IP addresses for your Dataflow workers.

Use cases of Cloud Dataflow

1. Stream analytics

Google’s stream analytics ensures that data is accurately arranged, useful, and accessible from the time it’s created. However, the streaming solution provisions the resources required for ingesting, processing, and analyzing fluctuating volumes of real-time data for real-time business insights. Further, this abstracted provisioning lowers complexity and makes stream analytics accessible for both data analysts and data engineers.

2. Real-time AI

Dataflow initiates streaming events to Google Cloud’s Vertex AI and TensorFlow Extended (TFX) for enabling predictive analytics, fraud detection, real-time personalization, and other advanced analytics use cases. However, TFX utilizes Dataflow and Apache Beam as the distributed data processing engine for providing access to various aspects of the ML life cycle. Moreover, they all are supported with CI/CD for ML via Kubeflow pipelines.

Cloud Dataflow pricing

In Dataflow, the rate for pricing is dependent on the hour. However, the Dataflow service usage charge in per second increments, on a per-job basis. And, the usage expresses in hours in order for applying hourly pricing to second-by-second use. 

Workers and worker resources

Every Dataflow job utilizes at least one Dataflow worker. However, the Dataflow service offers two worker types: batch and streaming with both having separate service charges. Dataflow workers ingest the following resources, every charged on a per-second basis.

• Firstly, vCPU
• Secondly, Memory
• Thirdly, Storage: Persistent Disk
• Lastly, GPU (optional)

Dataflow services

Dataflow uses a shuffle implementation directing on worker virtual machines and consumes worker CPU, memory, and Persistent Disk storage. However, the charges for Data Shuffle are computed per Dataflow job via volume adjustments applied to the total amount of data processed during Dataflow Shuffle operations. 

Summarizing the differences

Cloud Dataproc and Cloud Dataflow are two different data processing services offered by Google Cloud Platform (GCP). Here are the main differences between them:

• Processing Model: Cloud Dataproc is a fully managed service for running Apache Hadoop, Apache Spark, and Apache Hive jobs. It provides a cluster of virtual machines on which you can run your big data processing jobs. Cloud Dataflow is a fully managed service for building and executing batch and streaming data processing pipelines. It provides a programming model for defining data processing transformations and automatically manages the underlying infrastructure.
• Language Support: Cloud Dataproc supports a variety of programming languages, including Java, Python, Scala, and R, for running Hadoop and Spark jobs. Cloud Dataflow supports Java and Python for defining data processing transformations.
• Pricing: Cloud Dataproc pricing is based on the number of virtual machines and the duration of their usage. Cloud Dataflow pricing is based on the number of CPU and memory resources used by the data processing pipeline.
• Scalability: Both services are designed to be highly scalable, but they have different scaling models. Cloud Dataproc allows you to scale the cluster up and down as needed, while Cloud Dataflow automatically scales the infrastructure based on the data processing workload.
• Data Source: Cloud Dataproc supports a variety of data sources, including HDFS, Google Cloud Storage, and Bigtable. Cloud Dataflow can read data from a variety of sources, including Google Cloud Storage, Google BigQuery, and Apache Kafka.

Final Words

Above we have understood the comparison between Google Cloud Dataproc and Dataflow. In conclusion, Cloud Dataproc is well-suited for processing large amounts of data in batch mode, while Cloud Dataflow is designed for processing large amounts of data in real-time and transforming data into a desired format for analysis. The choice between the two services depends on the specific data processing needs of the organization.

Both these services are providing solutions to many top organizations and enterprises. So, if you have an interest in any of these, just go through the blog covering Dataproc and Dataflow features, overview, and other details to help in understanding and choosing the right one.