Azure Synapse vs Databricks: Assessing The Leading Data Platforms

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 proves 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.

Databricks Features:

• 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.

Databricks Competitors:

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
• Studio

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