Google Cloud Dataproc vs Databricks
Developers should use Dataproc when they need to process large-scale data workloads using open-source frameworks like Spark or Hadoop without managing the underlying infrastructure meets developers should learn databricks when working on large-scale data processing, real-time analytics, or machine learning projects that require distributed computing and collaboration. Here's our take.
Google Cloud Dataproc
Developers should use Dataproc when they need to process large-scale data workloads using open-source frameworks like Spark or Hadoop without managing the underlying infrastructure
Google Cloud Dataproc
Nice PickDevelopers should use Dataproc when they need to process large-scale data workloads using open-source frameworks like Spark or Hadoop without managing the underlying infrastructure
Pros
- +It's ideal for batch processing, machine learning, and ETL (Extract, Transform, Load) pipelines, especially in environments already leveraging Google Cloud for data storage and analytics
- +Related to: apache-spark, apache-hadoop
Cons
- -Specific tradeoffs depend on your use case
Databricks
Developers should learn Databricks when working on large-scale data processing, real-time analytics, or machine learning projects that require distributed computing and collaboration
Pros
- +It is particularly useful for building ETL pipelines, training ML models at scale, and enabling team-based data exploration with notebooks
- +Related to: apache-spark, delta-lake
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Google Cloud Dataproc if: You want it's ideal for batch processing, machine learning, and etl (extract, transform, load) pipelines, especially in environments already leveraging google cloud for data storage and analytics and can live with specific tradeoffs depend on your use case.
Use Databricks if: You prioritize it is particularly useful for building etl pipelines, training ml models at scale, and enabling team-based data exploration with notebooks over what Google Cloud Dataproc offers.
Developers should use Dataproc when they need to process large-scale data workloads using open-source frameworks like Spark or Hadoop without managing the underlying infrastructure
Disagree with our pick? nice@nicepick.dev