What is the difference between Google Cloud Dataflow and Google Cloud Dataproc?

Question

I am using Google Data Flow to implement an ETL data ware house solution.

Looking into google cloud offering, it seems DataProc can also do the same thing.

It also seems DataProc is little bit cheaper than DataFlow.

Does anybody know the pros / cons of DataFlow over DataProc

Why does google offer both?

Answer 1

Yes, Cloud Dataflow and Cloud Dataproc can both be used to implement ETL data warehousing solutions.

An overview of why each of these products exist can be found in the Google Cloud Platform Big Data Solutions Articles

Quick takeaways:

• Cloud Dataproc provides you with a Hadoop cluster, on GCP, and access to Hadoop-ecosystem tools (e.g. Apache Pig, Hive, and Spark); this has strong appeal if you are already familiar with Hadoop tools and have Hadoop jobs
• Cloud Dataflow provides you with a place to run Apache Beam based jobs, on GCP, and you do not need to address common aspects of running jobs on a cluster (e.g. Balancing work, or Scaling the number of workers for a job; by default, this is automatically managed for you, and applies to both batch and streaming) -- this can be very time consuming on other systems
  • Apache Beam is an important consideration; Beam jobs are intended to be portable across "runners," which include Cloud Dataflow, and enable you to focus on your logical computation, rather than how a "runner" works -- In comparison, when authoring a Spark job, your code is bound to the runner, Spark, and how that runner works
  • Cloud Dataflow also offers the ability to create jobs based on "templates," which can help simplify common tasks where the differences are parameter values

Answer 2

Here are three main points to consider while trying to choose between Dataproc and Dataflow

• Provisioning
  Dataproc - Manual provisioning of clusters
  Dataflow - Serverless. Automatic provisioning of clusters

• Hadoop Dependencies
  Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem.

• Portability
  Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine. This helps with portability across different execution engines that support the Beam runtime, i.e. the same pipeline code can run seamlessly on either Dataflow, Spark or Flink.

This flowchart from the google website explains how to go about choosing one over the other.

https://cloud.google.com/dataflow/images/flow-vs-proc-flowchart.svg

Further details are available in the below link
https://cloud.google.com/dataproc/#fast--scalable-data-processing

Answer 3

Same reason as why Dataproc offers both Hadoop and Spark: sometimes one programming model is the best fit for the job, sometimes the other. Likewise, in some cases the best fit for the job is the Apache Beam programming model, offered by Dataflow.

In many cases, a big consideration is that one already has a codebase written against a particular framework, and one just wants to deploy it on the Google Cloud, so even if, say, the Beam programming model is superior to Hadoop, someone with a lot of Hadoop code might still choose Dataproc for the time being, rather than rewriting their code on Beam to run on Dataflow.

The differences between Spark and Beam programming models are quite large, and there are a lot of use cases where each one has a big advantage over the other. See https://cloud.google.com/dataflow/blog/dataflow-beam-and-spark-comparison .

Answer 4

Cloud Dataproc and Cloud Dataflow can both be used for data processing, and there’s overlap in their batch and streaming capabilities. You can decide which product is a better fit for your environment.

Cloud Dataproc is good for environments dependent on specific Apache big data components: - Tools/packages - Pipelines - Skill sets of existing resources

Cloud Dataflow is typically the preferred option for green field environments: - Less operational overhead - Unified approach to development of batch or streaming pipelines - Uses Apache Beam - Supports pipeline portability across Cloud Dataflow, Apache Spark, and Apache Flink as runtimes.

See more details here https://cloud.google.com/dataproc/

Pricing comparision:

• DataProc

• Dataflow

If you want to calculate and compare cost of more GCP resources, please refer this url https://cloud.google.com/products/calculator/

Answer 5

One of the other important difference is:

Cloud Dataproc:

Data mining and analysis in datasets of known size

Cloud Dataflow:

Manage datasets of unpredictable size

see

Answer 6

Cloud Dataflow is a serverless data processing service that runs jobs written using the Apache Beam libraries. When you run a job on Cloud Dataflow, it spins up a cluster of virtual machines, distributes the tasks in your job to the VMs, and dynamically scales the cluster based on how the job is performing. It may even change the order of operations in your processing pipeline to optimize your job.

So use cases are ETL (extract, transfer, load) job between various data sources / data bases. For example load big files from Cloud Storage into BigQuery.

Streaming works based on subscription to PubSub topic, so you can listen to real time events (for example from some IoT devices) and then further process.

Interesting concrete use case of Dataflow is Dataprep. Dataprep is cloud tool on GCP used for exploring, cleaning, wrangling (large) datasets. When you define actions you want to do with your data (like formatting, joining etc), job is run under the hood on Dataflow.

Cloud Dataflow also offers the ability to create jobs based on "templates," which can help simplify common tasks where the differences are parameter values.

Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data.

1. Super fast — Without using Dataproc, it can take from five to 30 minutes to create Spark and Hadoop clusters on-premises or through IaaS providers. By comparison, Dataproc clusters are quick to start, scale, and shutdown, with each of these operations taking 90 seconds or less, on average. This means you can spend less time waiting for clusters and more hands-on time working with your data.
2. Integrated — Dataproc has built-in integration with other Google Cloud Platform services, such as BigQuery, Cloud Storage, Cloud Bigtable, Cloud Logging, and Cloud Monitoring, so you have more than just a Spark or Hadoop cluster—you have a complete data platform. For example, you can use Dataproc to effortlessly ETL terabytes of raw log data directly into BigQuery for business reporting.
3. Managed — Use Spark and Hadoop clusters without the assistance of an administrator or special software. You can easily interact with clusters and Spark or Hadoop jobs through the Google Cloud Console, the Cloud SDK, or the Dataproc REST API. When you're done with a cluster, you can simply turn it off, so you don’t spend money on an idle cluster. You won’t need to worry about losing data, because Dataproc is integrated with Cloud Storage, BigQuery, and Cloud Bigtable.
4. Simple and familiar — You don’t need to learn new tools or APIs to use Dataproc, making it easy to move existing projects into Dataproc without redevelopment. Spark, Hadoop, Pig, and Hive are frequently updated, so you can be productive faster.

If you want to migrate from your existing Hadoop/Spark cluster to the cloud, or take advantage of so many well-trained Hadoop/Spark engineers out there in the market, choose Cloud Dataproc; if you trust Google's expertise in large scale data processing and take their latest improvements for free, choose DataFlow.

Here are three main points to consider while trying to choose between Dataproc and Dataflow

Provisioning Dataproc - Manual provisioning of clusters Dataflow - Serverless. Automatic provisioning of clusters Hadoop Dependencies Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem. Portability Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine. This helps with portability across different execution engines that support the Beam runtime, i.e. the same pipeline code can run seamlessly on either Dataflow, Spark or Flink.