A Comparison of Azure Synapse and Databricks Data Platforms

Artificial Intelligence , Machine Learning , Predictive Maintenance

Azure Synapse, Databricks, Data management, Data ingestion, Data lake

Data Platforms: Which is Right for Your Business?

Jun 16, 2023

Data fuels this digital world. A massive volume of data is being produced every day by organizations. So, organizations need to blend their data into a single platform to perform various data analytics operations and generate actionable insights. Three popular data platforms are widely used: Azure Synapse, Databricks, and Snowflake. So, most people often need clarification to decide whether Azure Synapse, Databricks, or Snowflake would be the best option for their business.

However, Azure Synapse, Databricks are all different data platform solutions with similar features and functionalities, and all these platforms should be examined objectively to find their core differences. So, the best way is to compare them and understand the key differences to identify which data platform works best for your organization.

Explore the differences between Azure Synapse and Azure Databricks data platforms and discover which one suits your business needs. Learn about their components, key differences, and use cases to make an informed decision.

When it comes to platforms for processing large amounts of data, two of the common choices for enterprises are Azure Synapse and Databricks. Since both are solid options, deciding on which to go with often comes down to specific needs.

Overview of Azure Synapse

Start by introducing Azure Synapse (formerly Azure SQL Data Warehouse) and its key components. Discuss how Azure Synapse integrates data warehousing, big data analytics, and data integration in a unified analytics service. Highlight components like Azure SQL Data Warehouse, Apache Spark, and Azure Data Lake Storage that form the foundation of Azure Synapse's capabilities.

Overview of Azure Databricks

Provide an overview of Azure Databricks and its key components. Discuss how Azure Databricks offers a collaborative workspace for data engineering and data science workflows. Highlight components like Databricks Runtime, Delta Lake, and MLflow that enable data scientists and engineers to work seamlessly in a unified analytics environment.

Data Integration and Management

Compare the data integration and management capabilities of Azure Synapse and Azure Databricks. Discuss how Azure Synapse's components, such as Azure Data Factory and Azure Data Lake Storage, enable seamless data ingestion, preparation, and management. Contrast this with Azure Databricks, which provides a unified platform for data exploration, data transformation, and data integration through its collaborative workspace.

Analytics and AI Capabilities

Explore the analytics and AI capabilities of both platforms. Discuss how Azure Synapse leverages Azure SQL Data Warehouse and Apache Spark to enable batch and real-time analytics. Highlight its built-in AI capabilities, including Azure Machine Learning integration. Contrast this with Azure Databricks, which provides a robust environment for running data-intensive workloads, leveraging machine learning libraries, and enabling collaborative data science.

Scalability and Performance

Discuss the scalability and performance aspects of Azure Synapse and Azure Databricks. Explain how Azure Synapse's components, such as the distributed query engine and columnstore index, enable seamless scalability and performance optimization for data warehousing and analytics. Contrast this with Azure Databricks, which offers elastic scalability for processing large volumes of data and running complex analytics workloads with its distributed computing architecture.

Use Cases and Industry Examples

Provide use cases and industry examples for both Azure Synapse and Azure Databricks. Highlight scenarios where Azure Synapse's integrated analytics capabilities are beneficial, such as data warehousing, real-time analytics, and business intelligence. Showcase Azure Databricks' strengths in data exploration, machine learning, and collaborative data science, suitable for industries like healthcare, finance, and e-commerce.

Considerations for Choosing the Right Platform

Offer considerations for choosing between Azure Synapse and Azure Databricks. Discuss factors such as the nature of your data, analytics requirements, existing technology stack, and team expertise. Encourage readers to evaluate their specific business needs and align them with the strengths and use cases of each platform.

Key Differences: Azure Synapse vs. Databricks

Highlight the key differences between Azure Synapse and Azure Databricks. Discuss how Azure Synapse focuses on integrating data warehousing, big data analytics, and data integration into a single service. Contrast this with Azure Databricks, which emphasizes collaborative data science and provides a unified workspace for data engineers and data scientists. Discuss the differences in their architecture, components, and target use cases.

Features	Azure Synapse	Databricks
Overview	Azure Synapse integrates analytical services to bring the organization’s data warehouse and big data analytics into a single platform.	Along with big data analytics, Databricks lets users build ML products.
Type of Service	Platform as a Service (PaaS).	Software as a Service (SaaS).
Supported Languages	Python, SQL, Scala, Java, C#, etc.	SQL, Python, R, etc.
XML Support	Azure Synapse does not support XML.	Natively, XML is not supported but can be used after installing a library.
Architecture Overview	A unified platform integrated with data storage, data processing, and data visualization.	Databricks is a single unified data analytics platform that enables data scientists, data engineers, and data analyst teams to collaborate and work together.
Supported Cloud Platforms	It runs on the Azure cloud platform.	It runs on AWS, Microsoft Azure, and Google Cloud Platform.
Smart Notebook	Supports Nteract Notebooks and the notebooks do not have automated versioning. Additionally, Users cannot open the Nteract Notebooks simultaneously.	Databricks Notebooks supports automated versioning.
Compute resources	In Azure Synapse is a dedicated SQL pool and is required to create a SQL database that is compatible with Data Warehousing.	Databricks offers DB SQL, a serverless data warehouse on the Databricks lakehouse that allows users to run SQL at scale.
Machine Learning	Azure Synapse consists of built-in support for AzureML to handle machine learning workflows.	A robust machine-learning environment is available at Databricks for the creation of various models. It also allows programming in a variety of languages, making it simpler to employ libraries and modules.
Administration	Azure Synapse requires a platform-experienced administrator who is familiar with the native integration of Synapse with Spark Pool and Delta Lake, making it the best option for big data applications, including AI and ML.	Databricks requires an administrator who is familiar with data science, data engineering, data analysis, and machine learning to provide an effective data analytics solution.
Apache spark	Azure Synapse supports open-source Apache Spark.	Databricks supports Spark 3.0 and its latest versions.
Transaction	Azure Synapse Supports ACID transactions.	Databricks Supports ACID transactions.
Data Lake	You need to select a Data Lake as the primary Data Lake when creating Synapse.	In Databricks, you must mount a data lake before using it.
Data Security	Azure Synapse provides access control, authentication, and network security.	Databricks provides separate customer keys and role-based access control for workspace objects, jobs, clusters, pools, and table levels.
Scalability	It is simple to scale up and down.	Auto-scales based on the load.
Power BI	You can use Power BI from Azure Synapse Studio.	Provides access to the whole traditional BI for reporting.
Price	Based on your usage, you need to pay on an hourly basis.	Offers Pay-As-You-Go pricing approach. Pay for the computing resources you use on a granularity of per-second basis.

Which is Right for Your Business?

Summarize the key points discussed in the blog post, reiterate the differences between Azure Synapse and Azure Databricks, and emphasize the importance of selecting the right data platform for your business needs. Encourage readers to leverage the insights shared in the blog to make an informed decision that optimizes their data analytics capabilities.

This article has compared Azure Synapse, Databricks platforms and discussed their key differences. Now it’s time to unlock your data’s potential with an effective data platform.

Therefore, managing data effectively with appropriate data platforms could yield significant ROI for your business. We understand your business needs and deeply analyze the parameters such as data volume, workload, resources involved, data strategy, etc. and recommend the best data platform for your business.