Data Factory, Synapse Analytics and DataBricks Comparison

Question

I'm little bit new on Azure and I'm wondering when is recommendable to use ADF, Synapse, or DataBricks. What are their use cases for best practices and performance?

Could you help me with this theorical question?

Cheers!

Answer 1

The straight-forward answer to your question is they all are ETL/ELT and Data Analytics tool with some different approach and features.

When comes to Azure Data Factory vs Synapse, they both are almost same except some features. When building an analytics solution in Azure, we recommend starting with Synapse since you have a fully integrated design experience and Azure analytics product conformance in a single pane of glass. Azure Data Factory used for Migration databases and copy files. You can find most differences between these two services here: Differences from Azure Data Factory - Azure Synapse Analytics

Azure Data Factory vs Databricks: Key Differences

Azure Data Factory vs Databricks: Purpose

ADF is primarily used for Data Integration services to perform ETL processes and orchestrate data movements at scale. In contrast, Databricks provides a collaborative platform for Data Engineers and Data Scientists to perform ETL as well as build Machine Learning models under a single platform.

Azure Data Factory vs Databricks: Ease of Usage

Databricks uses Python, Spark, R, Java, or SQL for performing Data Engineering and Data Science activities using notebooks. However, ADF provides a drag-and-drop feature to create and maintain Data Pipelines visually. It consists of Graphical User Interface (GUI) tools that allow delivering applications at a higher rate.

Azure Data Factory vs Databricks: Flexibility in Coding

Although ADF facilitates the ETL pipeline process using GUI tools, developers have less flexibility as they cannot modify backend code. Conversely, Databricks implements a programmatic approach that provides the flexibility of fine-tuning codes to optimize performance.

Azure Data Factory vs Databricks: Data Processing

Businesses often do Batch or Stream processing when working with a large volume of data. While batch deals with bulk data, streaming deals with either live (real-time) or archive data (less than twelve hours) based on the applications. ADF and Databricks support both batch and streaming options, but ADF does not support live streaming. On the other hand, Databricks supports both live and archive streaming options through Spark API.

Azure Synapse vs Databricks: Critical Differences

Azure Synapse vs Databricks: Data Processing

Apache Spark powers both Synapse and Databricks. While the former has an open-source Spark version with built-in support for .NET applications, the latter has an optimized version of Spark offering 50 times increased performance. With optimized Apache Spark support, Databricks allows users to select GPU-enabled clusters that do faster data processing and have higher data concurrency.

Azure Synapse vs Databricks: Smart Notebooks

Azure Synapse and Databricks support Notebooks that help developers to perform quick experiments. Synapse provides co-authoring of a notebook with a condition where one person has to save the notebook before the other person observes the changes. It does not have automated version control. However, Databricks Notebooks support real-time co-authoring along with automated version control.

Azure Synapse vs Databricks: Developer Experience

Developers get Spark environment only through Synapse Studio and do not support any other local IDE (Integrated Development Environment). It also lacks Git integration with Synapse Studio Notebooks. Databricks, on the other hand, enhances developer experience with Databricks UI, and Databricks Connect that remotely connects via Visual Studio or Pycharm within Databricks.

Azure Synapse vs Databricks: Architecture

Azure Synapse architecture comprises the Storage, Processing, and Visualization layers. The Storage layer uses Azure Data Lake Storage, while the Visualization layer uses Power BI. It also has a traditional SQL engine and a Spark engine for Business Intelligence and Big Data Processing applications. In contrast, Databricks architecture is not entirely a Data Warehouse. It accompanies a LakeHouse architecture that combines the best elements of Data Lakes and Data Warehouses for metadata management and data governance.

Source: https://hevodata.com/learn/azure-data-factory-vs-databricks/, https://hevodata.com/learn/azure-synapse-vs-databricks/