Databricks vs Batch pool

Question

Hi,

I have Googled the difference between using Azure Databrick and Batch pool to run python pipelines and ETL. But I haven’t found a clear difference between them two.

Based on what I have Googled, Databrick can become very expensive, so cost matters a lot in here.

The data I am working with is not a big data, but since I have started using Databrick. I have found this tool the best tool for ETL and pipelines.

Would it be possible to highlight the pros and cons from Databrick and Batch account ?

Answer 1

Hello @Renan

Both Azure Databricks and Azure Batch are powerful tools for running ETL and pipelines, but they have some key differences that may make one more suitable for your specific use case than the other.

Azure Databricks is an Apache Spark-based analytics platform that provides a collaborative workspace for data engineers, data scientists, and machine learning practitioners. It is designed for distributed data processing at scale and provides native support for Python along with data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Some of the advantages of using Azure Databricks include:

• The data is transformed on the most powerful data processing Azure service, which is backed up by Apache Spark environment.
• Native support of Python along with data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
• Collaborative workspace for data engineers, data scientists, and machine learning practitioners.

However, Azure Databricks can be expensive, especially for smaller-scale experiments and workflows. Additional cost is incurred for Azure Databricks.

On the other hand, Azure Batch is a platform service for running large-scale parallel and high-performance computing (HPC) batch jobs. It provides job scheduling and automatic scaling of compute resources, and can be used to run heavy algorithms and process significant amounts of data. Some of the advantages of using Azure Batch include:

• The data is processed on Azure Batch pool, which provides large-scale parallel and high-performance computing.
• Can be used to run heavy algorithms and process significant amounts of data.

However, Azure Batch pool must be created before use with Data Factory, and there is complexity of handling dependencies and input/output parameters**.**

In summary, if you are working with big data and require a collaborative workspace for data engineers, data scientists, and machine learning practitioners, Azure Databricks may be the better choice. However, if you are working with smaller-scale experiments and workflows, or require job scheduling and automatic scaling of compute resources, Azure Batch may be the better choice.

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