In a global environment ruled by an increasing volume of data, the need for effective and powerful data platforms is essential. Organizations are in dire need of enclosing all the spread-out data in one place and performing different data operations on it, to extract insightful information and make valuable business decisions.
In the world of data platforms, there are two popular technologies that are often contrasted with each other – Azure Synapse vs Databricks. Both have proven their worth as reliable and effective data platforms. But when it comes to choosing between both, it is the organization that needs to analyze its data management needs and finalize the technology – Synapse vs Databricks.
As you compare both, you come to know of the peculiarities of each. Both offer features of enterprise data warehousing, machine learning, and ETL pipelines. As you delve deeper into the features and functionalities, it becomes easier to adjourn which one is better for your organization.
Before we compare Databricks vs Azure Synapse, let us look at their individual characteristics, features, advantages, etc.
What Is Databricks?
The lakehouse forms the foundation of Databricks Machine Learning – a data-native and collaborative solution for the full machine learning lifecycle, all your data, analytics, and AI on one platform. Developed by the creators of Apache Spark, Databricks has been a web-based tool that is ideal for all types of data needs. It is competent for creating interactive visualizations, text, and code with easy connectivity to tools like Tableau, Power BI, QlikView, etc.
It offers seamless integration with tech giants like Microsoft Azure, AWS, and GCP, easing out data management tasks for organizations that are handling huge chunks of data. It is a cloud-based tool that offers data exploration through machine learning models. The data engineering tools process and transform huge bulk of data for the creation of such ML models.
Databricks is created on top of distributed cloud computing technologies and hence prove much faster, secure, scalable, and robust. There are inbuilt visualization capabilities that work well for any type of data. Since it has a Lakehouse architecture, it makes Big Data analytics easy to perform. It lessens the load of undesired data components and offers a unified data source by making the most of the Lakehouse architecture.
- Database integration with data sources, developer tools, partner solutions
- Unifies data warehousing and AI needs on a single platform
- A reliable data platform across different cloud systems
- Streamlines data ingestion and management
- Offers deeper insights into the data pool
- Quickens machine learning and team productivity
- The end-to-end machine learning environment
- Simple and easy interface for the creation of a multi-cloud Lakehouse
What Is Synapse?
Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. It is the new avatar of Azure SQL Data Warehouse. It brings together the enterprise data warehouse and the huge analytical workloads. It merges the attributes of big data analytics, data warehousing, data lake, and data integration as the sole joined platform.
As we perceive what is Synapse, we understand that it can query data – relational and non-relational at a petabyte level. It provides T-SQL-centred analytics that leverages serverless and devoted SQL pools for extracting analytical information and data storage. The SQL server group offers the needed infrastructure for huge data warehouses and the serverless model offers ad-hoc queries of the data lake with the establishment of logical data warehouses.
It offers a personalized user experience with the implementation of effective conformity and governance procedures for secure client information. Users can extract in-depth information from data through different streams of data including big data systems and different programming languages.
Azure Synapse Features:
- Effective development of pipelines and ETL/ELT processes
- Get together big data analytics, data integration, and enterprise data warehousing in a unified workspace
- Easy integration through Apache Spark, SQL engine, and languages such as Python, .NET, etc.
- Real-time sensitive data security and protection with row-based and column-based security
- Cloud data service with support for structured and unstructured data
- Data exploration of relational and non-relational data with SQL
- Language compatibility with efficient storage of information
- Responsive data engine with optimized query facilities
Azure Synapse vs Databricks: Top Competitors
Azure Synapse Competitors:
Here are some of the technologies that are Azure Synapse competitors:
Google Cloud BigQuery, Databricks Lakehouse Platform, G2 Deals, Snowflake, Amazon RedShift, Cloudera, Dremia, IBM DB2, RStudio, MongoDB, and more.
Here are some of the technologies that are Databricks competitors:
Qubole, G2 Deals, Google Cloud BigQuery, Dremio, Snowflake, Amazon Redshift, Teradata Vantage, RStudio, IBM DB2, Cloudera, AWS, and more.
Databricks vs Azure Synapse: Pros and Cons
Databricks Benefits –
- Accessible data stores and faster ETL processes
- Unified space that promotes collaboration via a multi-user environment
- Offers unequaled support from popular tools and organizations
- Offers security-enabled features for creating high-end analytical solutions
- Simplifies data exploration, prototyping, and driving data-driven applications
- Empowers teams to offer performance-driven Spark clusters in a self-service manner
Databricks Disadvantages –
- Build and release of code package via CI/CD
- Software engineering skills are a must
- Code must remain in Notebooks, not being user friendly
Azure Synapse Benefits –
- Compatibility with scripting languages like Python, Scala, Java, SQL, R, etc.
- Personalized user experience with effective data storage
- Fine data security and fraud detection
- Fast and effective delivery of insights from all data sources
- Creation of comprehensive analytical solutions with less project development time
- Makes use of MPP database technology, for the management of workloads and large volumes of data
Azure Synapse Disadvantages –
- Job scheduling competencies are tough to handle
- Lags in terms of updates, new features, and Spark integration
- Seamless third-party integration is difficult
Azure Synapse vs Databricks: Major Components
Components of Databricks –
- Databricks SQL analytics
- Databricks Workspace
- Databricks Machine Learning
- Data management in Databricks SQL
- Clusters, Notebooks, Libraries, Workspace, Jobs
- Delta Lake
- Delta Engine
Components of Synapse –
- Synapse SQL
- Provisioned Pool
- On-demand Pool
- Open-Source Spark and Delta
- Synapse Pipelines
Databricks vs Synapse: The Similarities
- Popular data platforms
- Offer speed, volume, and quality demanded by BI and analytical solutions
- Offer data management and data analytics
- Ad-hoc data lake discovery
- Inherent support for machine learning workflows
Azure Synapse vs Databricks: A One-on-one Comparison
|Overview||A data warehouse and analytics tool, with open-source Apache Spark and inbuilt support for .NET for Spark applications||A web-based comprehensive platform for data storage and analysis, insightful information, and interactive displays|
|Architecture||Consists of data storage, data processing, and visualization, integrated into one platform||Application of data Lakehouse in an integrated cloud-based platform with connection to cloud-based storage|
|Ease of Use||Depends on SQL and Azure, hence easy to use for those organizations and users who know these platforms||Helps storage, cleaning, and visualization of data through a single platform performing tasks from basic ETL to complex BI, hence easy to use|
|General Competencies||Spark Engine, SQL Engine, data warehouse, and interface tool||Notebook, Dashboard, Databricks SQL, Machine Learning, Data Science|
|Support for Apache Spark||Has open-source Apache Spark with inbuilt support for .NET||Built on top of Apache Spark with fully managed Spark clusters|
|Notebooks||Supports Notebooks but has no support for automated versioning. The supported Notebook is the Nteract Notebook. Users must save the notebook before another user can view changes.||Supports Notebooks and automated versioning features. The supported Notebook is Databricks Notebook. Offers real-time co-authoring with automatic version control.|
|Developer Experience||Through Azure Synapse Studio for accessing at a single point||Through Databricks Connect and UI for easy connect|
|Supported Languages||Supports SQL, Python, Scala, etc.||Supports Python, R, SQL, etc.|
|Power BI Experience||Use Power BI from Azure Synapse Studio||Access to the complete traditional BI experience|
|Data Warehousing and SQL Analytics||Offers all necessary SQL features that a BI user would need, with the latest SQL technologies||Offers a delta lake-based data warehouse but may not be able to offer a complete BI experience|
|Leveraging Delta||Delta Lake is open source||Has Databricks Delta with some more optimizations|
|Data Security||Provides access control, network security, authentication, data protection for SQL injection attacks, authentication attacks||Provides role-based access control and automated encryption with other security features that play an important role|
Synapse vs Databricks: When to Use What?
As we compare Databricks vs Synapse, it becomes clearer as to when to use which technology:
Use Synapse When –
- You have a need for SQL data analytics, Big Data analytics, and data warehousing
- There is a need to create interactive, self-service reports through BI tools since Power BI can directly be accessed from Synapse Studio
- You are an avid SQL user who likes BI development with SQL technologies
- Users wish to deploy a good data warehouse and analytics tool quickly without manual setup
Use Databricks When –
- There is a need for AI, Machine Learning application development in real-time scenarios and data science workloads since it offers a great developer experience
- You are a data scientist using Notebooks and opt for coding in languages like Python or R
- There is a technical audience, and the data platform has a wider reach with better competencies
- There is more focus on the data lake and data processing with familiarity to Apache Spark
The Final Note – Azure Synapse Analytics vs Databricks
As we evaluate the duo – Databricks vs Azure Synapse, what is important is the global viewpoint with which we choose the appropriate tool for the appropriate purpose. Both have been successful in implementing challenging projects for multiple organizations
Hence, the final verdict of Databricks vs Synapse lies in the hands of the organization after evaluating all involved parameters like workload, data volume, utilization pattern, data strategies, involved resources, project timelines, budgeted cost, programming language, platform, investment in open-source tools, etc.
Whatsoever you finalize between Azure Synapse analytics vs Databricks, it is a good deal!