Amazon EMR and Microsoft Azure Synapse Analytics compete in the cloud-based data processing category. Microsoft Azure Synapse Analytics appears to have the upper hand with its seamless integration with Microsoft tools, making it attractive for businesses already using these ecosystems.
Features: Amazon EMR provides a highly scalable solution capable of vast data processing with efficient dynamic storage and automatic scaling. It integrates with Hadoop Clusters and offers ease of integration and management. Microsoft Azure Synapse Analytics offers users scalability with massive parallel processing, real-time analytics, and integration with tools like Power BI. It supports SQL interfaces and provides comprehensive data management capabilities.
Room for Improvement: Amazon EMR could benefit from improved web support and interface functionality, as well as better configuration options for new users. Some users face challenges with open-source integrations. Microsoft Azure Synapse Analytics needs improved integration with other services, enhanced cost tracking, and better cloud-to-premises transition capabilities along with feature parity with on-prem solutions.
Ease of Deployment and Customer Service: Both Amazon EMR and Microsoft Azure Synapse Analytics can be deployed across various cloud environments. Microsoft offers additional support for hybrid cloud setups. Amazon EMR's customer service is generally well-received, with some inconsistencies, while Microsoft Azure Synapse Analytics users report variability in support quality despite having widespread service channels.
Pricing and ROI: Amazon EMR relies on a usage-based pricing model without fixed licensing fees, resulting in varied feedback about costs. Its ROI is considered high for enterprises shifting from on-premises infrastructure. Microsoft Azure Synapse Analytics is perceived as expensive, with its pay-as-you-go structure posing challenges for some, although aligning with Microsoft ecosystems enhances its value. Significant savings can be achieved with strategic use.
Some of my customers have indeed seen a return on investment with Microsoft Azure Synapse Analytics as they used it for analytics to drive decision-making, improving their processes or increasing revenue.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
We get all call support, screen sharing support, and immediate support, so there are no problems.
They are slow to respond and not very knowledgeable.
This is an underestimation of the real impact because we use big data also to monitor the network and the customer.
I would rate the support for Microsoft Azure Synapse Analytics as an eight out of ten.
Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.
Microsoft Azure Synapse Analytics is scalable, offering numerous opportunities for scalability.
For the scalability of Microsoft Azure Synapse Analytics, I would rate it a 10 until you remain in the Azure Cloud scalability framework.
Recovering from such scenarios becomes a bit problematic or time-consuming.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
Performance and stability are absolutely fine because Microsoft Azure Synapse Analytics is a PaaS service.
I find the service stable as I have not encountered many issues.
We have never integrated Microsoft Azure Synapse Analytics with Databricks, but we have mostly pulled data from on-premises systems into Azure Databricks.
The cost factor differs significantly. When you run Spark application on EKS, you run at the pod level, so you can control the compute cost. But in Amazon EMR, when you have to run one application, you have to launch the entire EC2.
There is room for improvement with respect to retries, handling the volume of data on S3 buckets, cluster provisioning, scaling, termination, security, and integration between services like S3, Glue, Lake Formation, and DynamoDB.
Microsoft Azure Synapse Analytics is an excellent product because it includes both SIEM and orchestration capabilities with playbooks.
There is a need for better documentation, particularly for customized tasks with Microsoft Azure Synapse Analytics.
Databricks is a very rich solution, with numerous open sources and capabilities in terms of extract, transform, load, database query, and so forth.
Costs are involved based on cluster resources, data volumes, EC2 instances, instance sizes, Kubernetes, Docker services, storage, and data transfers.
The cheapest tier costs about $4,000 to $4,700 a year, while the most expensive tier can reach up to $300,000 a year.
I think the price of Microsoft Azure Synapse Analytics is very expensive, but that's not only for Microsoft Azure Synapse Analytics—it's for the cloud in general.
I find the pricing of Microsoft Azure Synapse Analytics reasonable.
Amazon EMR helps in scalability, real-time and batch processing of data, handling efficient data sources, and managing data lakes, data stores, and data marts on file systems and in S3 buckets.
Amazon EMR provides out-of-the-box functionality because we can deploy and get Spark functionality over Hadoop.
One of the most valuable features in Microsoft Azure Synapse Analytics is the ability to write your own ETL code using Azure Data Factory, which is a component within Synapse.
Microsoft Azure Synapse Analytics offers significant visibility, which helps us understand our usage more clearly.
For Microsoft Azure Synapse Analytics, the integration is the most valuable feature, meaning that whatever you need is fast and easy to use.
| Product | Market Share (%) |
|---|---|
| Microsoft Azure Synapse Analytics | 6.3% |
| Amazon EMR | 3.3% |
| Other | 90.4% |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 5 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 18 |
| Large Enterprise | 55 |
Microsoft Azure Synapse Analytics is an end-to-end analytics solution that successfully combines analytical services to merge big data analytics and enterprise data warehouses into a single unified platform. The solution can run intelligent distributed queries among nodes, and provides the ability to query both relational and non-relational data.
Microsoft Azure Synapse Analytics is built with these 4 components:
Microsoft Azure Synapse Analytics Features
Microsoft Azure Synapse Analytics has many valuable key features, including:
Microsoft Azure Synapse Analytics Benefits
Some of the benefits of using Microsoft Azure Synapse Analytics include:
Reviews from Real Users
Below are some reviews and helpful feedback written by Microsoft Azure Synapse Analytics users who are currently using the solution.
PeerSpot user Jael S., who is an Information Architect at Systems Analysis & Design Engineering, comments on her experience using the product, saying that it is “Scalable, intuitive, facilitates compliance and keeps your data secure”. She also says "We also like governance. It looks at what the requirements are for the company to identify the best way to ensure compliance is met when you move to the cloud."
Michel T., CHTO at Timp-iT, mentions that "the features most valuable are the simplicity, how easy it is to create a dashboard from different information systems."
A Senior Teradata Consultant at a tech services company says, "Microsoft provides both the platform and the data center, so you don't have to look for a cloud vendor. It saves you from having to deal with two vendors for the same task."
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
