Amazon EMR vs Azure Synapse: What are the differences?

Introduction

Here we will compare Amazon EMR and Azure Synapse, two widely used big data processing platforms. Both platforms offer scalability and performance for analyzing big data, but they have some key differences in their architecture, features, and integration capabilities.

1. Architecture: Amazon EMR is built on Apache Hadoop and allows users to run distributed processing frameworks like Hive, Spark, and HBase on a cluster of EC2 instances. On the other hand, Azure Synapse is a unified analytics service that combines big data processing with data warehousing capabilities, enabling users to analyze both structured and unstructured data using scalable resources.

2. Data Integration: Amazon EMR integrates well with various AWS services such as S3, DynamoDB, and Redshift, allowing seamless data transfer and processing across these services. It also has integration with third-party tools and services. In contrast, Azure Synapse provides seamless integration with the Azure ecosystem, including Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Data Warehouse. It also has built-in connectors for popular data sources like Salesforce, SharePoint, and Dynamics 365.

3. Data Warehousing: While Amazon EMR focuses more on big data processing, Azure Synapse combines big data processing with data warehousing capabilities. Azure Synapse offers a dedicated SQL-based query engine for fast and interactive querying of structured and semi-structured data. It also provides built-in data transformation and data loading capabilities, making it easier to prepare and analyze data for reporting and insights.

4. Data Lake Analytics: Amazon EMR provides the option to create and utilize data lakes for storing and processing large volumes of data. With EMR, users can leverage tools like AWS Glue for building data catalogs and AWS Athena for interactive querying on data lakes. On the other hand, Azure Synapse integrates with Azure Data Lake Storage Gen2, empowering users to leverage its serverless analytics capabilities for on-demand data exploration and processing.

5. Scalability and Pricing: Both Amazon EMR and Azure Synapse offer scalability, allowing users to scale resources up or down based on their workload requirements. However, the pricing models differ. Amazon EMR pricing is based on the EC2 instances and storage used, while Azure Synapse pricing is based on processing units and data storage. Users should carefully assess their workload and data storage needs to choose the most cost-effective option for their specific use case.

6. Managed Service: In terms of being a managed service, Amazon EMR provides a highly flexible and customizable platform where users have more control over configuring and managing the infrastructure. Azure Synapse, on the other hand, provides a fully managed service that abstracts away much of the infrastructure management, allowing users to focus more on data analysis and insights.

In summary, while both Amazon EMR and Azure Synapse offer powerful big data processing capabilities, they differ in terms of architecture, data integration options, data warehousing capabilities, data lake analytics, scalability and pricing models, as well as managed service offerings. Choosing the right platform depends on specific requirements, existing infrastructure, and preference for customization versus ease of management.