Azure Synapse vs. Databricks: A Comprehensive Examination of Leading Data Platforms

Tycho Löke

Aug 22, 2023 • 7 min read

In our data-driven world, where information is expanding at an unprecedented rate, the demand for robust and dynamic data platforms has never been higher. Businesses are on a quest to unify scattered data, perform multifaceted operations, and derive actionable insights to guide strategic decisions.

Within the data platform landscape, Azure Synapse and Databricks emerge as two prominent technologies. Both have proven their worth in reliability and efficiency. Yet, the choice between Synapse and Databricks requires a careful examination of an organization's specific data management requirements.

Comparing these two platforms reveals distinct characteristics. They both excel in enterprise data warehousing, machine learning, and ETL pipelines. A closer look at their features and capabilities will help determine the best fit for your organization.

What is Databricks?

Databricks Machine Learning's lakehouse serves as a foundational, data-native, and collaborative solution, encompassing all facets of machine learning, data, analytics, and AI. Developed by the creators of Apache Spark, Databricks is a versatile web-based tool suitable for diverse data applications. It enables interactive visualizations, text, and code, with smooth connections to tools like Tableau, Power BI, QlikView, and more.

With seamless integration across major platforms like Microsoft Azure, AWS, and GCP, Databricks simplifies data management for organizations dealing with vast data volumes. As a cloud-based tool, it facilitates data exploration through machine learning models, utilizing data engineering tools to process and transform extensive data sets.

Built atop distributed cloud computing technologies, Databricks is faster, more secure, scalable, and resilient. Its built-in visualization options are versatile, and its Lakehouse architecture simplifies Big Data analytics. By fully leveraging this architecture, it unifies data sources and minimizes unnecessary data components.

Databricks Features:

Integration with various data sources, development tools, partner solutions
Unification of data warehousing and AI on a single platform
Reliable data platform across different cloud systems
Streamlined data capture and management
In-depth insights into the data pool
Enhanced machine learning and team productivity
Comprehensive machine learning environment
Intuitive interface for multi-cloud Lakehouse creation
Advanced machine learning capabilities like managed MLflow, AutoML, and REST API for deployed models (as highlighted by industry experts)

What is Synapse?

Azure Synapse Analytics is a limitless analytics service, blending data integration, enterprise data warehousing, and big data analytics. As the evolved form of Azure SQL Data Warehouse, it merges big data analytics, data warehousing, data lakes, and data integration into a unified platform.

Examining Synapse reveals its ability to fetch both relational and non-relational data at petabyte scale. It offers T-SQL-based analytics, utilizing serverless and dedicated SQL pools for analytical information extraction and data storage. The SQL server group provides the infrastructure for vast data warehouses, while the serverless model facilitates ad hoc data lake queries and logical data warehouse creation.

Synapse ensures a customized user experience, implementing robust compliance and governance procedures for secure customer information. It enables users to glean profound insights from various data streams, including big data systems and diverse programming languages.

Azure Synapse features:

Efficient pipeline development and ETL/ELT processes
Unified workspace for big data analytics, data integration, and enterprise data warehousing
Seamless integration through Apache Spark, SQL engine, and languages like Python, .NET, etc.
Real-time security for sensitive data with row- and column-based protection
Cloud data service for both structured and unstructured data
Exploration of relational and non-relational data using SQL
Language compatibility with efficient information storage
Agile data engine with optimized query capabilities
Full integration with Power BI for a more traditional relational experience (a key differentiator as per industry insights)

Azure Synapse vs. Databricks: Top Competitors

Azure Synapse competitors include:

Databricks Lakehouse Platform: A unified data analytics platform designed for collaboration and integrated with the latest data and ML frameworks.
Google Cloud BigQuery: A fully-managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure.
Snowflake: A cloud-based data warehousing platform that allows many users to access and compute data simultaneously.
Amazon RedShift: A fully managed, petabyte-scale data warehouse service in the cloud.
Cloudera: Offers a hybrid, open-source data platform for machine learning and analytics.
Dremia: A SQL Lakehouse platform that simplifies data queries.
IBM DB2: A family of data management products, including database servers, developed by IBM.
RStudio: An integrated development environment for R, a programming language for statistical computing and graphics.
MongoDB: A cross-platform document-oriented database program.
Teradata: A provider of database and analytics-related software, products, and services.
SAP HANA: An in-memory, column-oriented, relational database management system.

Databricks Competitors include:

Azure Synapse Analytics: An analytics service that brings together big data and data warehouse management.
Qubole: A cloud-native, big data platform that self-manages and self-optimizes.
Google Cloud BigQuery: Similar to Azure Synapse, a serverless and highly scalable data warehouse.
Snowflake: Offers a cloud-based data warehousing platform.
Amazon Redshift: Competes directly with both Azure Synapse and Databricks in the data warehousing space.
Teradata Vantage: A powerful, modern cloud platform for analytics.
RStudio: Offers tools for both data science and data analytics.
IBM DB2: Similar to Azure Synapse, offers a suite of data management products.
Cloudera: Provides a platform for machine learning and analytics, optimized for the cloud.
AWS: Offers a broad array of data management and analytics services.
Dremio: Known for simplifying and accelerating data analytics.

Databricks vs. Azure Synapse: Pros and Cons

Databricks Benefits:

Streamlined data storage and accelerated ETL processes
Unified environment that encourages collaboration
Unmatched support for popular tools and organizations
Robust security features for crafting top-tier analytical solutions
Simplified data exploration, prototyping, and application development
Empowers teams to deploy performance-driven Spark clusters autonomously
Advanced machine learning capabilities for real-time scenarios (a unique advantage)

Databricks Cons:

Requires building and releasing code packages via CI/CD
Necessitates software engineering skills
Code must remain in Notebooks, which may not be user-friendly

Benefits of Azure Synapse:

Compatibility with languages like Python, Scala, Java, SQL, R, etc.
Tailored user experience with efficient data storage
Robust data security and fraud detection mechanisms
Swift and effective insight delivery from all data sources
Comprehensive analytical solutions with reduced development time
Utilization of MPP database technology for workload and large data management
Integration with existing Microsoft or Azure services (an aspect that may influence the choice for some organizations)

Azure Synapse Disadvantages:

Task planning capabilities may be challenging
Potential delays in updates, new features, and Spark integration
Seamless third-party integration may be complex

Azure Synapse vs. Databricks: Key Components

Components of Databricks:

Databricks SQL analytics
Databricks Workspace
Databricks Machine Learning
Data management in Databricks SQL
Clusters, notebooks, libraries, workspace, tasks
Delta Lake
Delta engine

Components of Synapse:

Synapse SQL
Furnished pool
Pole on request
Open-Source Spark and Delta
Synapse Pipelines
Studio

Databricks vs Synapse: The Similarities

Renowned data platforms
Deliver the speed, volume, and quality required by BI and analytics solutions
Facilitate data management and analysis
Enable ad-hoc data lake discovery
Inherent support for machine learning workflows

Azure Synapse vs Databricks: A One-to-One Comparison

Parameters	Synapse	Databricks
Overview	A comprehensive tool for data warehousing and analytics, with open-source Apache Spark and .NET support	A holistic platform for data storage, analysis, and visualization, with cloud-based integration
Architecture	Unified platform integrating data storage, processing, and visualization	Utilizes data Lakehouse in a cohesive cloud-based platform with cloud storage connectivity
Ease of use	User-friendly for organizations familiar with SQL and Azure	Facilitates data storage, cleansing, and visualization through a single platform, from basic ETL to complex BI
General competencies	Spark Engine, SQL Engine, data warehouse, interface tool	Notebook, Dashboard, Databricks SQL, Machine Learning, Data Science
Support for Apache Spark	Open-source Apache Spark with .NET support	Fully managed Spark clusters built on Apache Spark
Notebooks	Supports notebooks but lacks automated versioning; Nteract Notebook supported	Supports notebooks with automated version control; Databricks Notebook supported with real-time collaboration
Experience with developers	Accessible via Azure Synapse Studio	Accessible via Databricks Connect and UI
Supported languages	SQL, Python, Scala, etc.	Python, R, SQL, etc.
Experience with Power BI	Accessible from Azure Synapse Studio	Full traditional BI experience access
Data warehousing and SQL Analytics	Comprehensive SQL features with cutting-edge technologies	Delta lake-based data warehouse with limited full BI experience
Utilize Delta	Open-source Delta Lake	Optimized Databricks Delta
Data Security	Comprehensive security features including access control, network security, authentication, SQL injection protection	Role-based access management, automated encryption, and other vital security features

Synapse vs Databricks: When to Use What?

Choosing between Databricks and Synapse becomes clearer with a detailed comparison:

Use Synapse When:

SQL data analysis, big data analysis, and data warehousing are required
Interactive, self-service reports are needed through BI tools, as Power BI is directly accessible from Synapse Studio
SQL and BI development are preferred
Quick implementation of a robust data warehouse and analysis tool is desired without manual installation
Integration with existing Microsoft or Azure services is a priority (a consideration that may guide the choice)

Use Databricks When:

AI, real-time machine learning application development, and data science workloads are needed, as it offers an excellent developer experience
Data scientists prefer coding in languages like Python or R within Notebooks
A technical audience requires a broader-reaching data platform with superior competencies
Focus on data lake and data processing with Apache Spark familiarity is essential
Advanced machine learning capabilities are critical for the organization's operations (a unique advantage)

The Final Note: Azure Synapse Analytics vs. Databricks

When assessing Databricks vs. Azure Synapse, the overall perspective must guide the selection of the right tool for the right task. Both have successfully executed complex projects across various organizations.

Thus, the final decision between Databricks and Synapse rests with the organization, considering factors such as workload, data volume, usage pattern, data strategies, resources, timelines, costs, programming language, platform, open-source tool investment, and more. The choice may also be influenced by specific use cases, such as advanced machine learning needs or integration with existing technologies, as highlighted by industry insights.

If you want to know more about Microsoft Azure feel free to contact me:

Contact me via,
Mail: tycho.loke@peoplerock.nl
Phone: +31 6 39 41 36 65
LinkedIn: Tycho Löke