A Comparison of Azure Synapse, Databricks, and Snowflake Data Platforms
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, and Snowflake 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.
What is Azure Synapse?
Azure Synapse is a PaaS-based cloud data warehousing service offered by Microsoft. It is a limitless analytics service that combines big data analytics, enterprise data warehousing, and data integration. Synapse also integrates with Azure Data Share, Azure Machine Learning, and Power BI. Azure Synapse Analytics is the next generation of the Azure SQL Data Warehouse and gives you the flexibility to query data on your terms using serverless or dedicated options at scale.
Components of Azure Synapse:
- Synapse SQL
- Synapse Spark
- Synapse Pipelines
- ADX – Azure Data Explorer
- Synapse Studio
- PowerBI.
What is Databricks?
Databricks is a Saas-based lakehouse cloud data platform that offers the unification of all your data, analytics, and AI on one platform. Databricks integrates with cloud storage such as AWS, Microsoft Azure, and Google Cloud Platform and simplifies the data management process for organizations. Databricks Lakehouse Platform combines the elements of data lakes and data warehouses to deliver the reliability and performance of data warehouses with machine learning support. Databricks can derive valuable insights using SparkSQL and easily integrate with visualization tools such as Power BI, Tableau, and QlikView.
Data Lakehouse is built on an open and reliable data foundation that effectively handles all data kinds and uses a single, standardized approach to security and governance across all your data and cloud platforms.
The lakehouse provides the cornerstone of Databricks Machine Learning – a data-native and collaborative solution for the whole machine learning lifecycle, from featurization to production.
Components of Databricks (please check for new features):
- Databricks Engineering Workspace
- Databricks SQL Analytics Workspace
- Databricks Machine Learning Workspace
- Data management in Databricks SQL
- Delta Engine
- Delta Lake
- Workspace and Tasks
- SQL editor and dashboards
- Data ingestion and governance
- Compute management
- ML model serving
- Data discovery, annotation, and exploration
- Clusters, Notebooks, and Libraries.
What is Snowflake?
Snowflake is a SaaS-based data platform built to run on major cloud service providers such as AWS, Microsoft Azure, and Google Cloud Platform. This platform is widely used for data storage, data lakes, warehousing, engineering, data science, consumption of real-time data, data sharing, and security. Snowflake cloud platform consists of three main components such as database storage, Query processing, and Cloud services. It offers a complete end-to-end solution for managing and processing your data.
No current database technology or “big data” software platforms like Hadoop are used to build the Snowflake data platform. Instead, Snowflake combines an innovative architecture that is native to the cloud with a new SQL query engine.
Tools and Interface components of Snowflake:
- Snowsight
- SnowSQL
- Snowpipe
- Snowpark API
- Snowflake SQL API
- Snowflake Time Travel
- Snowflake File Safe
- Snowflake Scripting
- Snowflake Ecosystem
- Snowflake Information Schema
- Snowflake Partner Connect
- Snowflake Extension for Visual Studio Code
- Warehouse management.
Connectivity components of Snowflake:
- Python connector
- Spark connector
- Node.js driver
- Go Snowflake driver
- .NET driver
- JDBC client driver
- ODBC client driver
- PHP PDO driver.
Key Differences: Azure Synapse vs. Databricks vs. Snowflake
| Features | Azure Synapse | Databricks | Snowflake |
| 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. | Snowflake is built to harness the potential of big data analytics. The architecture physically separates but logically integrates storage and computing layers and offers relational database support. |
| Type of Service | Platform as a Service (PaaS). | Software as a Service (SaaS). | Software as a Service (SaaS). |
| Supported Languages | Python, SQL, Scala, Java, C#, etc. | SQL, Python, R, etc. | Java, JavaScript, Python, SQL, etc |
| XML Support | Azure Synapse does not support XML. | Natively, XML is not supported but can be used after installing a library. | Snowflake supports XML. |
| 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. | Snowflake consists of database storage, query processing, and cloud services. |
| Supported Cloud Platforms | It runs on the Azure cloud platform. | It runs on AWS, Microsoft Azure, and Google 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. | Snowflake integrates and connects to Jupyter Notebook using Python. |
| 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. | Snowflake is a serverless solution with fully independent storage and computation processing layers based on the ANSI SQL. |
| 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. | Snowflake does not have ML libraries, but you can integrate Snowflake with various ML tools using connectors. |
| 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. | Snowflake administrator requires minimal monitoring as it better suits conventional business intelligence, automatic clustering, and analytics needs with near-zero maintenance. |
| Apache spark | Azure Synapse supports open-source Apache Spark. | Databricks supports Spark 3.0 and its latest versions. | Snowflake Spark connector integrates Snowflake into the Apache Spark ecosystem. Snowflake supports Spark 3.1, Spark 3.2, and Spark 3.3. |
| Transaction | Azure Synapse Supports ACID transactions. | Databricks Supports ACID transactions. | Snowflake 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. | In Snowflake, it is necessary to deploy Data Lake before use, or you can use Spark configuration. |
| 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. | Always-On-Encryption is used. Provides separate customer keys and role-based access control. |
| Scalability | It is simple to scale up and down. | Auto-scales based on the load. | Enables scaling based on the current demand. |
| Power BI | You can use Power BI from Azure Synapse Studio. | Provides access to the whole traditional BI for reporting. | Power BI provides access to connect with Snowflake. |
| 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. | Offers Pay-As-You-Go pricing approach. Pay for the computing resources you use on a granularity of per-second basis. |
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This article has compared Azure Synapse, Databricks, and Snowflake 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 at iLink digital 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.
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