What is the difference between Cloud Dataproc & Cloud Dataflow?
Data is an important part of today’s digital world. It can be extracted or interpreted in many ways. Google Cloud offers data solutions for data storage and processing using its popular services Dataproc, Dataflow. These services provide solutions for many top organizations to transform and get high-performance data at a low cost. There is confusion about which service to choose. This blog will compare cloud Dataproc to Dataflow, covering its features, uses, as well as other important areas. Let’s start with an overview.
What is Cloud Dataproc?
Dataproc service scales Apache Spark and Apache Flink, Presto and other open-source tools. This service can also be used for data lake modernization, ETL and secure data science using Google Cloud at a fraction of its cost. Dataproc is also a tool that modernizes open-source data processing. This means that you can speed up your data processing and analytics by using purpose-built environments that can be rearranged on-demand.
This service also includes autoscaling and cluster deletion, per second pricing, integrated security, as well as options for lowering security risks and costs. It also provides advanced security, compliance and governance for user authorization and authentication using existing Kerberos, Apache Ranger policies, or Personal Cluster Authentication.
1. Fully managed and automated open-source big data software
Dataproc offers managed deployment, monitoring, and log logging to help you concentrate on your data and analytics. This will lower the cost of Apache Spark management. Dataproc can also be used by data scientists and engineers to interface with common tools such as Jupyter notebooks and Zeppelin notebooks. Dataproc also includes Metastore and Jobs API, which allow for easy integration of large data processing into bespoke apps. Metastore, on other hand, eliminates the need for you to host your own catalog service.
2. Kubernetes and Apache Spark jobs containerized
Dataproc allows you to create Apache Spark jobs by using Dataproc on Kubernetes. This is for use with Dataproc with Google Kubernetes engine (GKE). This allows you to provide job portability and isolation.
3. Google Cloud for Enterprise Security
You can enable Hadoop Secure Mode with Kerberos by adding a Security Configuration (to a Dataproc cluster) to the Security Configuration. Default at-rest encryption and VPC Service Controls, OS Login, OS Login, and customer-managed encryption keys (CMEK) are some of the most popular Google Cloud-specific security features.
4. Open source with the best of Google Cloud
Dataproc is a tool that can be used to access cloud-scale data by providing open-source tools, algorithms and programming languages. It can also be used in conjunction with the rest of Google Cloud analytics, data, and AI ecosystem. Data scientists and engineers can create data applications that connect Dataproc with BigQuery, AI Platform or Cloud Spanner.
5. Resizable clusters
Dataproc allows you to quickly create and scale clusters by using virtual machine types, disk sizes and number of nodes.
6. Autoscaling clusters
Dataproc autoscaling is a method for automating cluster resource management. It also allows automatic addition and subtraction of cluster workers (nodes).
7. Cloud integrated
Dataproc is combined with Cloud Storage, BigQuery and Cloud Bigtable, Cloud Logging and Cloud Monitoring to provide a fully-integrated data platform.
Dataproc supports image versioning, which allows for movement between different versions Apache Spark, Apache Hadoop, or other tools.
9. Highly available
This allows you to run clusters in high availability mode with multiple master nodes, and set jobs for restarting if necessary.