Cloud Computing

Know the Differences between Azure data bricks & Azure synapse analytics

January 4, 2023

Azure Synapse Analytics and Azure Databricks are cloud-based BI and data analytics platforms. Most enterprises that look for a single solution to unify all data operations—storage, processing, analysis, and visualization—eventually veer away from small-scale solutions and look toward such robust platforms.

While their features overlap, distinct differences between Azure Databricks and Azure Synapse Analytics make them ideal for different business use cases. This article will compare the platforms and highlight their differences so enterprises can determine the right solution.

Azure Databricks – Synopsis

Azure Databricks is a cloud-based platform that runs on Apache Spark. The data bricks management layer is built around Apache Spark’s distributed computing framework to simplify infrastructure management. At scale, the Azure Databricks platform offers a set of unified tools to build, deploy, share, and maintain enterprise-grade data solutions. As a result, Databricks positions itself more as a lakehouse (data lake plus data warehouse) than a data warehouse or a datalake which can be used to process large volumes of unprocessed data. That’s why databricks find its business use cases across ELT (extract, load, and transform), streaming, machine learning, and data science-based analytics.

One of the most significant advantages of Azure Databricks is that it does not force you to migrate your data to your proprietary system to leverage the platform. Instead, it allows you to configure a databricks workspace that you can integrate between your Azure Databricks platform and your cloud account. Azure Databricks deploys computer clusters to your account using cloud resources that help you store data but do not limit the customizations and control that your data, operations, and security teams require.

The Azure Databricks platform is predominantly used to store, process, clean, analyse, model, share and monetize datasets. In addition, you can use it to build applications such as data engineering workflows, analytics dashboards, business intelligence solutions, machine learning models, and more that enable innovation across your organization.

Key service capabilities of Azure Databricks

1. Optimized spark engine

The highly optimized Apache Spark engine enables simple data processing on autoscaling infrastructure, allowing 50x performance gains.

2. Machine learning run time

State-of-the-art frameworks such as PyTorch, TensorFlow, and sci-kit-learn databricks facilitate single-click access to preconfigured machine learning environments.

3. Language

Whether you use serverless or provisioned compute resources, you can use your preferred language—Python, Scala, R, Spark, SQL, and .Net.

4. Collaboration

Explore data quickly, find and share insights with teams, and easily collaborate with your preferred tools and language.

5. Delta lake

You can make your existing data lake more reliable and scalable with an open-source transactional storage layer.

6. Native Azure integrations

Azure Databricks can seamlessly integrate into Azure services such as Azure Data Factory, Azure Data Lake Storage, Azure Machine Learning, and Power BI.

7. Modern workspace

Enable seamless collaboration between teams—data engineers, data scientists, and business analysts.

8. Enterprise-grade security

Even when thousands of users access data, your datasets and workspaces can be secure, compliant, and private.

9. Production-ready

With CI/CD and monitoring feasibility and integrations ecosystem, you can run your mission-critical data workloads on a reliable data platform.

10. Unity Catalog

Unity Catalog can govern all your data and AI assets, such as files, tables, dashboards, and machine learning models within your lakehouse on any cloud.

Azure Synapse Analytics – Synopsis

Azure Synapse is an enterprise analytics service that combines data integration, data warehousing, and big data analytics. Azure Synapse helps ingest, prepare, transform, and manage data with a unified experience to serve the immediate needs of business intelligence and machine learning. In addition, Azure Synapse accelerates time to insights bringing together the best of SQL, Spark, Data Explorer, Studio, and Pipelines technologies, enabling deeper integration into Azure services such as AzureML, Power BI, and CosmosDB. Each component serves a unique purpose.

  • Synapse SQL: The backend query system enables data warehousing and virtualization. The SQL system also extends T-SQL (Transact-SQL) to deliver machine learning capabilities. Hence, you can extend Azure Synapse Analytics for ML use cases like Azure Databricks.
  • Apache Spark: Apache Spark is the big data engine used in Azure Synapse Analytics. It performs data cleaning, preparation, ETL, and other data processing functionalities to prepare data for analytics systems.
  • Data Explorer: The Data Explorer layer enables log and time series analytics. It complements the Apache Spark and SQL runtime engines by delivering the capability to run near real-time log analytics and IoT analytics and process other free-text and semi-structured data.
  • Pipelines: Pipelines enable businesses to integrate Azure Synapse Analytics with other Azure services and products, such as CosmosDB, Power BI, and AzureML.
  • Synapse Studio: Synapse Studio provides a front-end web UI in which teams can build, run and manage all operations and tasks related to Synapse Analytics. Tasks such as data ingestion, exploration, user role creation, accessing SQL tables, etc., can be executed via the UI rendered through Synapse Studio.

Unlike Databricks, Synapse Analytics does not have performance bottlenecks such as job and stage latency. It is fine-tuned for data analytics use cases rather than broad use cases such as ETL tasks or creating machine learning models. Its primary business use is analyzing structured data to derive meaningful BI insights. The architecture is built to process data and prepare it for analytics, reporting, and BI platforms.

Key service capabilities

1. Unified analytics platform

You can perform data exploration, integration, warehousing, big data analytics, and machine learning tasks from a unified environment.

2. Enterprise data warehousing

Since Azure synapse is the industry’s top-performing SQL engine, it can be the best foundation to build your mission-critical data warehouse.

3. Explore data lake

You can use the same service you used to build the data warehouse solution to query files in the data lake, bringing together relational and non-relational data.

4. Code-free hybrid data integration

You can quickly build both ELT/ETL processes in a code-free visual environment to ingest data from more than 95 native connectors.

5. Language

Whether you go for serverless or dedicated resources, you can choose your preferred language—KQL, T-SQL, Spark SQL, Python, Scala, and .Net.

6. Log, telemetry analytics

Since the Azure Synapse data explorer distributed query engine uses text-indexing technology, you can gain insights from time series, log, and telemetry data.

7. AI and BI integration

Azure Synapse can be your end-to-end enterprise analytics solution with deep integration of Azure Cognitive services, Azure Machine Learning, and Power BI.

8. Hybrid transactional/analytical processing (HTAP)

You can gain insights from real-time transactional data stored in operational databases in a single click, such as Azure CosmosDB.

Quick view – Differences between Azure Databricks and Azure Synapse Analytics

1. Foundation

Synapse Analytics is built on a foundation of SQL, Spark, and Data Explorer. In addition, the architecture can handle the parallel processing, so operations are not memory intensive, unlike in Azure Databricks.

Azure Databricks is based on Apache Spark. However, it uses a proprietary data processing engine built on an optimized version of Apache Spark. In contrast, Synapse Analytics uses the open-source version of Apache Spark, allowing GPU-enabled clusters (Graphics Processing Units) and switching between standard and high-concurrency cluster modes.

Both solutions are cloud-based.

2. Developer experience

Spark development on Synapse Analytics is carried out through Synapse Studio.

Databricks provides Databricks workspaces and remote connections from Pycharm, Visual Studio Code, etc., through Databricks Connect.

3. Use cases

Synapse Analytics is preferred for data warehousing, data analytics, SQL analysis, reporting, and BI. In addition, databricks are chosen for machine learning development, data transformation, and data processing activities.

4. Integration

Azure Synapse Analytics has built-in solutions for data processing (ETL and other processes), data storage (via Azure Data Lake Storage Gen 2), and even Power BI. In addition, it easily integrates with other Azure services and products.

Azure Databricks may require API configurations or third-party tools for some integrations.

Final words

The choice between Databricks and Synapse Analytics ultimately boils down to the use case and budget. Other comparisons, such as performance, support, security, etc., are evenly matched. Consensus pegs Synapse Analytics as the better choice for BI and reporting needs, whereas Databricks is preferred for organizations looking to build systems around machine learning. However, both solutions have SaaS-based pay-per-use pricing plans that you can compare here: Azure Databricks pricing and Azure Synapse Analytics pricing.

Choosing the right solution or integrating the solution into existing architecture is challenging. PreludeSys is a Microsoft Gold Partner with an expert team dedicated to Azure products. Feel free to reach out to us for a consult.