Azure Synapse vs Databricks: 6 Critical Differences [2023 Review]
Generating insights from big data is a challenge for every organization since data collected from various sources are mostly unstructured. To derive insights from unorganized data with traditional big data methods requires domain-specific expertise and precise monitoring when the process is scaled to a larger ecosystem. However, both Microsoft and Databricks provide scalable big data analytics platforms with Azure Synapse and Databricks Workspace that combine enterprise data warehousing, ETL pipelines, and machine learning workflows.
Table of Contents
This article will provide a comparative understanding of Azure Synapse vs Databricks. The article will also mention the key differences between the two platforms. Read along to find out in-depth information about Azure Synapse vs Databricks.
Table of Contents
- Prerequisites
- What is Azure?
- What is Azure Synapse?
- What is Databricks?
- Azure Synapse vs Databricks: What is the Difference?
- Azure Synapse vs Databricks: Tabular Comparison
- Conclusion
Prerequisites
- Understanding of data warehousing
- An idea of cloud data analytics
What is Azure?
Operated by Microsoft, Azure is a sophisticated Cloud Computing platform that can be used for analytics, virtual computing, storage, networking services, and more. It also has been one of the pioneers to offer clients end-to-end (storage to deployment) big data solutions. Today, businesses use Azure cloud services like Azure Machine Learning, Azure Data Factory, or Azure Synapse to build, deploy, and manage Machine Learning and Big Data Analytics applications.
What is Azure Synapse?
Azure Synapse provides an End-to-end Analytics Solution by blending Big Data Analytics, Data Lake, Data Warehousing, and Data Integration into a single unified platform. It has the ability to query relational and non-relational data at a petabyte-scale by running intelligent distributed queries among nodes at the backend in a fault-tolerant manner.
Synapse architecture consists of four components: Synapse SQL, Spark, Synapse Pipeline, and Studio. While Synapse SQL helps perform SQL queries, Apache Spark executes batch/stream processing on Big Data. Synapse Pipeline provides ETL (Extract-Transform-Loading) as well as Data Integration capabilities, whereas Synapse Studio provides a secure collaborative cloud-based analytics platform, providing AI, ML, IoT, and BI in a single space.
Synapse also offers T-SQL (Transact-Qequential Query language) based analytics that comprises ‘Dedicated’ and ‘Serverless’ SQL pools for entire analytics and data storage. While the dedicated pool of SQL Servers provides the necessary infrastructure for implementing Data Warehouses, the serverless model empowers unplanned or ad-hoc workloads without setting up data warehouses.
Key Features of Azure Synapse
Some of the key features of Azure Synapse are as follows:
- Cloud Data Service
- Supports Strutured and Unstructured Data
- Effective Data Storage
- Responsive Data Engine
- Language Compatibilty
- Query Optimization
1) Cloud Data Service
Synapse offers Data Warehousing, Machine Learning, Data Analytics, and Dashboarding service in a single workspace on the cloud. This ecosystem performs ETL, supports advanced ML algorithms, and visualizes data with Microsoft Power BI.
2) Supports Structured and Unstructured Data
Unlike data warehouses and data lakes that store relational and non-relational data respectively. Synapse powers businesses by handling relational and non-relational data like tabular, LOB, CRM, Graph, Image, Social, or IoT data under the same roof.
3) Effective Data Storage
As Synapse performs Big Data analytics, it uses Azure Data Lake Storage Gen 2 (ADLS Gen2) to provide storage solutions. ADLS Gen2 is combined with Azure Blob to offer next-level data storage that has high data availability and tiered data storage.
4) Responsive Data Engine
Irrespective of what data storage methods are accompanied, enterprises expect blazing results. Synapse provides Massive Parallel Processing (MPP) to handle analytical workloads and aggregate processes for large volumes of data efficiently.
5) Language Compatibility
As Synapse handles various data analysis and engineering profiles, it supports a wide range of scripting languages. Azure Synapse is compatible with multiple programming languages like Scala, Python, Java, SQL, or Spark SQL.
6) Query Optimization
Query concurrency has been a challenge for any analytics system. However, Synapse facilitates limitless concurrency and performance optimization. In addition, workload management is readily simplified in Synapse by prioritizing important queries. For instance, if the ‘CEO’ of a company runs a query, then such queries are automatically promoted instead of queuing them.
What is Databricks?
Databricks is a Cloud-based Data Engineering tool for processing, transforming, and exploring large volumes of data to build Machine Learning models intuitively. Currently, the Databricks platform supports three major cloud partners: AWS, Microsoft Azure, and Google Cloud. Azure Databricks is a jointly developed first-party service from Microsoft that can be accessed with a single click on Azure Portal.
Organizations find it challenging to handle big data because it requires an integration of various tools. However, Databricks facilitates a zero-management cloud platform that is built around Spark cluster to provide interactive workspace. It enables Data Analysts, Data Scientists, and Developers to extract values from big data efficiently. In addition, it seamlessly supports third-party applications such as BI and domain-specific tools for generating valuable insights. Large-scale enterprises utilize this platform for a broader spectrum to perform ETL, data warehousing, or dashboarding insights for internal users and external clients.
Enterprises operate their Data Warehouses independently of Data Lakes. While the former helps to derive valuable business insights, the latter is used for storage and data science applications. Databricks has a ‘Lake House’ architecture that leverages data lake and data warehouse elements to provide low-cost data management. This architecture facilitates ACID (Atomicity, Consistency, Isolation, and Durability) transaction, robust data governance, decoupled storage from computation, and end-to-end streaming.
The Lakehouse platform streamlines data, AI, and analytics in one platform to perform traditional SQL analytics, BI, as well as data science and machine learning applications. Although users of Lakehouse have access to a variety of standard tools (Python, Spark, or R), Delta Lake provides an open file format (parquet) to track version changes while offering data management capabilities. A Delta Lake is a Lake House architecture built on top of the data lake that provides an open format storage layer for both streaming and batch operations. Such open data file format simplifies data accessibility for data scientists as well as machine learning engineers to implement ML applications using some popular tools like pandas, TensorFlow, or PyTorch.
Key Features of Databricks
Some of the key features of Databricks are as follows:
1) Language Compatibility
While Azure Databricks is Spark-based, it is also compatible with programming languages like Python, R, and SQL for use. These languages are converted to Spark at the backend through APIs, allowing users to work in their preferred programming language.
2) Productivity and Collaboration
With Databricks, organizations can create an environment that offers a collaborative workspace between data scientists, engineers, and business analysts. Such interaction among multiple members brings novel ideas during the early stages of Machine Learning Applications life cycle. Additionally, version control of a source code becomes a painless task as all involved users have access to ongoing projects.
3) Connectivity
Apart from cloud-based services, Databricks easily imports CSV or JSON files and connects to SQL servers. It also bridges data sources like MongoDB, Avro files, and many others.
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Azure Synapse vs Databricks: What is the Difference?
Azure Synapse successfully integrates analytical services to bring enterprise data warehouse and big data analytics into a single platform. On the other hand, Databricks not only does big data analytics but also allows users to build complex ML products at scale. Below are a few key differences illustrating the comparative study of Azure Synapse vs Databricks:
- Azure Synapse vs Databricks: Data Processing
- Azure Synapse vs Databricks: Smart Notebooks
- Azure Synapse vs Databricks: Developer Experience
- Azure Synapse vs Databricks: Architecture
- Azure Synapse vs Databricks: Leveraging Lake
- Azure Synapse vs Databricks: Machine Learning Development
1) 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.
2) 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.
3) 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.
4) 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.
5) Azure Synapse vs Databricks: Leveraging Lake
While creating a project in Synapse, you can select a Data Lake to be the primary data source. Once a Data Lake is mounted on Synapse, it allows users to query from Notebooks or Scripts and analyze unstructured data. However, Databricks does not require mounting Data Lakes. Additionally, it enables users to leverage delta lakes by providing an open format storage layer that delivers reliability, security, and performance on existing data lakes.
6) Azure Synapse vs Databricks: Machine Learning Development
Azure Synapse has built-in support for AzureML to operationalize Machine Learning workflows. However, it does not provide full support of Git and a collaborative environment. In contrast, Databricks incorporates optimized ML workflows that provide GPU-enabled clusters and facilitate tight version control using Git.
Azure Synapse vs Databricks: Tabular Comparison
| Azure Synapse | Databricks | |
| Spark | It has Open-source Apache Spark and built-in support for .NET for Spark Applications. | Optimized Adaption of Apache Spark that delivers 50x performance. It has support for Spark 3.0. Moreover, it allows users to select Clusters with GPU enabled and choose between standard and high-concurrency Cluster Nodes. |
| Notebooks | Nteract Notebooks can not be opened at the same time and they don’t have automated Versioning. | Databricks Notebooks supports Automated Versioning. It further implements changes in real-time. |
| Developer Experience | Developer Experience powered by Synapse Studio. This is without Git integration. | Databricks Connect & Databricks UI. |
| Access Data from a Data Lake | You must select a Data Lake as the primary Data Lake, while creating Synapse. | It is necessary to install Data Lake before using it or you can use Spark configuration. |
| Harnessing Delta | Open-source Delta Lake. | Databricks Delta offers some additional optimizations. |
| Generic Capabilities | It has both Spark Engine & SQL Engine. It is a Data Warehouse as well as an Interface tool, | It supports a Spark-based tool for Data Engineering, MLOps and Data Science. This is a Notebook Tool. It also focuses on Spark, Delta Engine, MLflow and MLR. |
Conclusion
In this article, you have learned about the comparative study of Azure Synapse vs Databricks. This article also provided information on Microsoft Azure, Azure Synapse, Azure Databricks, and their key features.
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