Azure Synapse vs Databricks: 11 Key Differences Explained

- August 26, 2024
Time - 7:26 PM

Choosing the best platform to meet their data processing requirements is a problem for enterprises in the ever-changing field of big data analytics. Two prominent players are ruling the charts – Azure Synapse and Databricks.

Let’s understand more about both in this article. This understanding will help companies in using these technologies to the best of their advantage.

What is Azure Synapse?
What is Databricks?
11 Key Differences: Azure Synapse vs Databricks
Azure Synapse vs Databricks: Comparison Table
Azure Synapse vs Databricks: Why Do You Need a Comparison?
Final Thoughts
FAQs

What is Azure Synapse?

Azure Synapse offers an end-to-end analytical solution by blending data lake, big data analytics, data integration, and data warehousing in a single unified platform. By distributing intelligent queries among backend nodes in a fault-tolerant way, it can query both relational and non-relational data at a petabyte scale.

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It consists of four components – Spark, Synapse SQL, Studio, and Synapse Pipeline. Synapse SQL assists with running SQL queries, whereas Apache Spark handles batch and stream processing of large-scale data. Synapse Studio offers a secure, collaborative, cloud-based analytics platform that combines AI, ML, IoT, and BI in one place. In contrast, Synapse Pipeline offers ETL (Extract-Transform-Loading) and Data Integration capabilities.

Additionally, Synapse provides analytics based on T-SQL (Transact-Sequential Query Language), which includes Dedicated and Serverless SQL pools for comprehensive analytics and data storage. The serverless paradigm empowers ad hoc or unforeseen workloads without requiring the setup of data warehouses. At the same time, the dedicated pool of SQL Servers offers the infrastructure needed to construct data warehouses.

What is Databricks?

A data engineering cloud-based platform called Databricks is used to handle, manipulate, and explore massive amounts of data in order to create machine learning models. The AWS, Microsoft Azure, and Google Cloud platforms are supported. Microsoft’s collaboratively created Azure Databricks service is available via the Azure Portal.

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By offering a zero-management cloud platform based on Spark clusters, Databricks helps scientists, developers, and analysts work more effectively with large amounts of data. For ETL, data warehousing, and dashboarding, corporations utilize it, and it supports applications from other parties.

With its Lake House design, which combines data lake and data warehouse components, Databricks offers end-to-end streaming, data governance, low-cost data management with ACID transactions, and decoupled storage and computing. With support for SQL analytics, BI, data science, and machine learning applications, the Lakehouse platform unifies data, AI, and analytics into a single platform.

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For version tracking and data management, it leverages open file formats such as Delta Lake (based on Parquet), making data more accessible to ML programmers and data scientists.

11 Key Differences: Azure Synapse vs Databricks

Here are the top 11 differences explained in detail for your reference:

1. Overview

Azure Synapse is an infinite analytics service that unifies business data warehousing, data integration, and big data analytics into one cohesive platform. It includes integrated support for .NET, Spark applications, and Apache Spark, which are available for free. A cloud-based data warehousing platform called Databricks is used to handle, analyze, store, and convert massive volumes of data in order to create machine learning models.

2. Developer Experience

Using Synapse Studio, Spark development is done on Synapse Analytics. Through Databricks Connect, Databricks offers Databricks workspaces and remote connections from Pycharm, Visual Studio Code, and other programs.

3. Financial Considerations

Azure Synapse offers pay-as-you-go and reserved capacity models with flexible pricing choices; however, costs are subject to unanticipated increases. Pay-as-you-go is also the model used by Databricks, along with reserved instance settings.

4. Platform Focus

Big data analytics and data warehousing are combined in Azure Synapse Analytics, whereas Apache Spark-based big data processing and machine learning are the main features of Databricks.

5. Data Processing

Databricks and Synapse are both powered by Apache Spark. The latter features an optimized version of Spark that offers 50 times greater performance, while the former has an open-source Spark version with built-in support for .NET applications. Databricks users may choose GPU-enabled clusters with increased data parallelism and quicker data processing thanks to its improved Apache Spark support.

6. Machine Learning

Git and other versioning systems are essential to most ML settings in order to work together efficiently and produce a smooth workflow. While Azure integrates AzureML into its machine learning process, its limited support for Git may cause issues when team members collaborate. However, Databricks’ strong support for Git and GPU-enabled clusters facilitates increased collaboration and versioning of ML models.

7. Customer Base

When you compare the client bases of Microsoft Azure Synapse and Databricks, you will find that Microsoft Azure Synapse has 8,409 customers, while Databricks has 11,790. Databricks is ranked first in the Big Data Analytics category, followed by Microsoft Azure Synapse in fourth position.

8. Structured Streaming

Structured Streaming by Databricks is an excellent option for processing data almost in real time. With its close interaction with Delta Lake and Auto Loader features, it provides exact-once processing assurances along with end-to-end fault tolerance. While Azure Stream analytics may be used as a data warehouse to import near-real-time data into Azure Synapse Analytics, Delta format is not supported at this moment. Synapse is a platform for developers; its primary emphasis is not currently real-time transformations.

9. Target Group and Use Case

Synapse is perfect for businesses in need of a robust data warehouse with business intelligence integration capabilities. Databricks are ideal for situations requiring sophisticated analytics and potent data processing, particularly in the domains of AI and machine learning.

In the Big Data Analytics category, Microsoft Azure Synapse holds an 11.34% market share, while Azure Databricks holds a 15.03% share.

11. Notebook

Notebooks can be used with Azure Synapse, although automatic versions are not supported. The Nteract Notebook is the supported notebook, and before the other person can view the changes in Synapse, one person must save the notebook. Conversely, Databrick offers support for both automatic versioning features and notebooks. The Databricks notebook is the one that is supported. Furthermore, databricks provides automated version control and real-time co-authoring.

Azure Synapse vs Databricks: Comparison Table

Listed below are some critical differences between the two technologies:

Parameters	Azure Synapse	Databricks
Platform focus	Combines big data analytics and data warehousing	Focuses on Apache Spark-based machine learning and big data analytics.
Data storage integration	Integrates with Azure Blob storage and Azure Data Lake storage	Supports many data sources but has an excellent integration with cloud object storage, such as Amazon S3 and Azure Data Lake storage.
SQL support	Native SQL for data warehousing workloads	Uses Apache Spark SQL to perform SQL-based queries.
Ecosystem integration	Integrate with Azure tools and services	Robust integration with Apache Spark, the open-source ecosystem
Managed service offerings	Provide managed cloud services	Provides managed collaborative workspace for data teams
Apache Spark integration	Only for big data processing	Built on Apache Spark and offers seamless integration
Scalability	Can scale storage and compute resources independently	Can scale computed resources only on demand
Compliance and security	Provides industry compliance, role-based access control, and data encryption, among other security features	Provides industry compliance and security features
Programming languages support	Supports Python, SQL, and Scala	Supports Python, SQL, and Scala
Pricing model	Pay-as-you-go based on storage and compute usage	Pay-as-you-go based on compute usage
Core features	Integrate ML and BI, unified analytical workspace, and real-time insights	Data sharing, data engineering, data governance, advanced data warehousing, and AI and ML
AI and ML	Provide tools for business intelligence and machine learning applications by integrating with Power BI and Azure Machine Learning	Excels in AI and machine learning, utilizing technologies like MLflow to manage the ML life cycle and an optimized Spark engine

Azure Synapse vs Databricks: Why Do You Need a Comparison?

You must as a business, select the best analytical platform to unleash your data’s potential. Here are the reasons why a comparison between the two is essential:

Efficiency – If you choose the right platform, it will save resources and time. Moreover, it will make data analysis less labor-intensive and quick.
Accuracy – The right place will ensure your data is accurate and reliable, saving you from costly errors.
Informed decisions – Data is needed to make business decisions, and the right platform will give you a deep understanding of insights and advice. This will help you in making informed business decisions.
Cost savings – With the right platform in the system, you will not have to worry about unnecessary expenses. It will reduce the additional overhead costs and the expenses of using multiple tools.
Scalability – You must look for a platform that can grow with the growing demands of your business and data needs.

Simply put, if you choose the right platform, it will directly correlate with the success or failure of your business. More so, because these platforms come with a cost, the wrong one will incur not only the cost of the platform but also additional expenses to manage the workloads.

Final Thoughts

The final decision between Azure Synapse and Databricks will fall on whether or not the organization is well-versed in the Azure platform. If the company is dedicated to open-source tools, it should go for Databricks. However, if they know how to operate on Azure platforms, Azure Synapse will help them achieve their goals with less resistance.

Irrespective of the choice, people looking to make their careers in this field must undergo a certification course to improve their chances of getting a job. You can enroll in one of the cloud certification courses at CCSLA and learn from the experts in the field. In no time, you can be an expert in your chosen field and shine in your career.

FAQs

Q1. What is Azure Synapse?

Azure Synapse, formerly known as Azure SQL Data Warehouse, is an analytics service that brings together enterprise data warehousing and Big Data analytics. It offers a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Q2. What is Databricks?

Databricks is a data analytics platform founded by the original creators of Apache Spark. It integrates with Spark to provide a collaborative, cloud-based environment that supports data engineering, data science, machine learning, and analytics on a managed Apache Spark platform.

Q3. How do the purposes of Azure Synapse and Databricks differ?

Azure Synapse is primarily designed for big data and data warehousing solutions, providing tools for ETL processes, data querying, and reporting at scale. Databricks focuses more on data science and machine learning, offering robust support for collaborative projects and exploratory data analysis using Spark.

Q4. Which platform offers better integration with other services?

Azure Synapse is deeply integrated with other Microsoft services, including Power BI, Azure Data Lake, and Azure Machine Learning, providing a seamless experience within the Azure ecosystem. Databricks, while also offering integrations with various cloud services, is particularly strong in its native integration with Apache Spark and AI capabilities.

Q5. What are the key features of Azure Synapse?

Key features of Azure Synapse include big data integration, data warehousing, provisioning of on-demand or provisioned resources, and deep integration with BI tools. It supports T-SQL for analytics, offers a serverless query service, and has built-in security and management features.

Q6. What are the key features of Databricks?

Databricks’ key features include collaborative notebooks for data teams, native Spark integration, machine learning runtime environments, and optimized connectors for data storage systems. It also provides robust scalability options and an interactive workspace for developing and deploying machine learning models.

Q7. How do pricing models differ between Azure Synapse and Databricks?

Azure Synapse offers both on-demand and provisioned pricing models, allowing users to choose based on their workload requirements. Databricks charges based on Databricks Units (DBUs), which represent a combination of processing power and cloud resources.

Q8. Which platform is more user-friendly for data scientists, Azure Synapse or Databricks?

Databricks is generally more user-friendly for data scientists, especially those familiar with Apache Spark, due to its collaborative notebooks and built-in machine learning and data science frameworks. Azure Synapse, while powerful, is more oriented towards data engineers and BI professionals.

Q9. How does data security compare between Azure Synapse and Databricks?

Both platforms provide robust data security features. Azure Synapse benefits from Microsoft’s comprehensive security framework, including network security, access controls, and encryption. Databricks also offers strong security measures, including encryption at rest and in transit, as well as compliance with major security standards.

Q10. Which platform is better suited for real-time data processing, Azure Synapse or Databricks?

Databricks excels in real-time data processing due to its native Spark integration, which is designed for high-performance analytics and real-time data processing. Azure Synapse also supports real-time processing but is optimized for large-scale data warehousing and batch processing tasks.

Q11. What should you consider when choosing between Azure Synapse and Databricks?

Consider your specific needs: if your priority is data warehousing and integration within the Azure ecosystem, Azure Synapse may be the better choice. If you need a platform that excels in machine learning, data science, and collaborative projects with Spark, Databricks would be more suitable.

Azure Synapse vs Databricks: 11 Key Differences Explained

Table of Contents

What is Azure Synapse?

What is Databricks?