Generating insights from big data is challenging 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.
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 Databricks vs Synapse.
What is Azure Synapse?
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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:
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.
Hevo integrates with dedicated SQL pools, which are SQL data warehouses in Azure Synapse Analytics. You must provision the dedicated SQL pool from within a Synapse workspace. A Synapse workspace helps to securely collaborate between your Azure resources, which are logically grouped together in a container called a resource group.
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?
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Databricks is a Cloud-based Data Engineering tool for processing, transforming, and exploring large volumes of data to build Machine Learning models. It supports AWS, Microsoft Azure, and Google Cloud platforms. Azure Databricks is a jointly developed service from Microsoft that can be accessed from the Azure Portal.
Databricks provides a zero-management cloud platform built around Spark clusters, enabling Data Analysts, Scientists, and Developers to work with big data efficiently. It supports third-party applications and is used by enterprises for ETL, data warehousing, and dashboarding.
Databricks has a ‘Lake House’ architecture that combines data lake and data warehouse elements, providing low-cost data management with ACID transactions, data governance, decoupled storage and computation, and end-to-end streaming.
The Lakehouse platform streamlines data, AI, and analytics in one platform, supporting SQL analytics, BI, data science, and machine learning applications. It uses open file formats like Delta Lake (based on Parquet) for version tracking and data management, simplifying data accessibility for data scientists and ML engineers.
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.
Azure Synapse vs Databricks: What is the Difference?
Azure Databricks vs Synapse Analytics for big data brings forth a key difference. Azure Synapse 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:
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1) Azure Synapse vs Databricks: Data Processing
A key difference between Azure Databricks and Azure Synapse Analytics is 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 perform quick experiments. Synapse provides co-authoring of a notebook, with the condition that one person must save the notebook before the other 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, which remotely connects via Visual Studio or Pycharm within Databricks.
4) Azure Synapse vs Databricks: Architecture
Another key difference between Databricks and Synapse is in terms of 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.
7) Azure Synapse vs Databricks: Ad-hoc Data Lake Discovery
Azure Synapse and Databricks services are well-suited for data lake discovery. In the case of Databricks, after mounting data lake to your workspace, you can query data using Python, R, or Scala. On the other hand, Synapse provides an on-demand Spark or SQL pool for querying data from your data lake. You can select the UI or tool that best aligns with your expertise and preferences.
8) Azure Synapse vs Databricks: Real-time Transformations
When it comes to Databricks vs Synapse Analytics for real-time analytics, there’s the matter of real-time transformations. Synapse can ingest real-time data using Stream Analytics, it lacks support for Delta currently and does not completely prioritize real-time transformations. Where as, Databricks stands out as preferred choice. It provides Spark Structured Streaming with features like join optimizations and Z-order clustering. It also facilitates incremental loading.
9) Azure Synapse vs Databricks: SQL Analyses & Data Warehousing
In case of extensive SQL analysis and robust data warehousing, Synapse is the best choice. It offers a complete data warehousing experience with stored procedures, relational data models, and complete standard T-SQL environment. It brings the best SQL technologies together like columnar indexing for enhanced performance.
10) Azure Synapse vs Databricks: Reporting and Self-Service BI
Synapse In case of reporting and sef-service Business Intelligence, Synapse is the best choice. Synapse gives you direct access to use Power BI from Synapse Studio. In enterprise data warehousing, SQL pool used for SQL Data Warehouse is widely recognized.
11) Azure Synapse vs Databricks: Pricing
In terms of Databricks vs Synapse pricing, Azure Synapse is charged based on data exploration, warehousing, storage options like number of TBs stored, data processed, data moved, runtime and cores used in data flow execution. Whereas, Databricks pricing is around $99 per month, a free version is also available which might be less expensive as storage is not included in the cost.
12) Azure Synapse vs Databricks: Data Security
Azure synapse offers robust data control, authentication, threat protection, network security, and data protection to detect authentication attacks, SQL injection attacks, and unauthorized access locations. Databricks offers a list of features like, role-based access control (RBAC), and security features. Therefore, both platforms excel in ensuring comprehensive data security.
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. |
Security | Uses Microsoft scope, masking, and encryption which enables security, prevent attacks and controls data access | It offers customer keys, encryption, and role-based access control to ensure proper governance and security. |
When to use Azure Synapse vs. Databricks?
Differences between Azure Synapse vs Data bricks becomes clear when you know when to use which data platform:
Use Azure Synapse :
- Power BI tools are easy to access directly from Synapse Studio using which you can create self-service reports.
- You can perform big data analytics, SQL data analytics, and data warehousing and quickly deploy a good data warehouse and analytics tool without a manual installation.
- You can use BI development with SQL technologies.
Use Databricks :
- AI/ML applications can be developed in real-time scenarios and data science workloads using Python or R.
- It is similar to Apache Spark with more focus on data processing and data lake.
- The data platform can have a larger audience with better competencies.
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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Amit Kulkarni specializes in freelance writing within the data industry, by creating informative and engaging content on data science by using his problem-solving and analytical thinking ability.