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Microsoft Azure Synapse Analytics vs Snowflake vs Teradata comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
7.0
Users experienced positive ROI with Azure Synapse, citing cost reductions, improved data efficiency, and flexibility over traditional solutions.
Sentiment score
6.8
Snowflake users experience mixed ROI; challenges in calculation exist, but long-term benefits include cost reduction and improved data management.
Sentiment score
8.1
Teradata boosts analytics speed over 100%, enhancing customer service and satisfaction, with high ROI and user approval.
 

Customer Service

Sentiment score
6.9
Microsoft Azure Synapse Analytics support quality varies; larger accounts receive better service, but smaller customers experience challenges.
Sentiment score
7.3
Snowflake's customer service is praised for expertise and helpfulness, though some note delays and lack of SLAs.
Sentiment score
7.1
Teradata's customer service is praised for expertise but criticized for delays, with ratings ranging from 6 to 10 out of 10.
They are slow to respond and not very knowledgeable.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
The technical support from Snowflake is very good, nice, and efficient.
The technical support from Teradata is quite advanced.
Customer support is very good, rated eight out of ten under our essential agreement.
 

Scalability Issues

Sentiment score
7.7
Microsoft Azure Synapse Analytics offers high scalability, enabling easy scaling for diverse industries despite potential high costs.
Sentiment score
7.8
Snowflake is praised for scalability and efficiency, but concerns exist regarding cost-effectiveness in medium to large-scale organizations.
Sentiment score
7.4
Teradata is praised for its scalability, speed, and flexibility, despite some complexity and cost challenges in cloud environments.
Microsoft Azure Synapse Analytics is scalable, offering numerous opportunities for scalability.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Snowflake is very scalable and has a dedicated team constantly improving the product.
This expansion can occur without incurring downtime or taking systems offline.
Scalability is complex as you need to purchase a license and coordinate with Teradata for additional disk space and CPU.
 

Stability Issues

Sentiment score
7.6
Microsoft Azure Synapse Analytics is stable, commendable in performance, but some users desire improvements for large data handling reliability.
Sentiment score
8.2
Snowflake is praised for stability and reliability, with users noting excellent performance, quick issue resolution, and robust architecture.
Sentiment score
8.4
Teradata excels in stability with minimal downtime, robust architecture, 99.9% uptime, and reliable performance, despite minor large dataset issues.
I find the service stable as I have not encountered many issues.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
I find the stability to be almost a ten out of ten.
The workload management and software maturity provide a reliable system.
 

Room For Improvement

Microsoft Azure Synapse Analytics needs improvements in pricing, usability, integration, performance, and advanced features for enhanced overall functionality.
Snowflake users seek improved UI, pricing transparency, analytics, integrations, AI features, and enhanced support, ETL, and machine learning capabilities.
Teradata users seek better transaction processing, enhanced scalability, modern interface, cloud focus, advanced analytics, and improved support and documentation.
There is a need for more expertise among the support team to guide us effectively.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
Cost reduction is one area I would like Snowflake to improve.
Unlike SQL and Oracle, which have in-built replication capabilities, we don't have similar functionality with Teradata.
 

Setup Cost

Enterprise users of Azure Synapse Analytics find pricing variable, ranging from affordable to expensive, with concerns over billing unpredictability.
Snowflake's pricing offers flexibility but can be unpredictable and expensive compared to Redshift or BigQuery, with room for transparency improvements.
Teradata's high cost is justified by its superior performance, competitive total ownership costs, and flexible pricing models.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
Initially, it may seem expensive compared to similar cloud databases, however, it offers significant value in performance, stability, and overall output once in use.
Teradata is much more expensive than SQL, which is well-performed and cheaper.
 

Valuable Features

Microsoft Azure Synapse Analytics offers scalability, seamless integration, user-friendly design, robust security, and efficient data processing for businesses.
Snowflake excels in scalable, secure data processing with fast queries, multi-format support, and seamless third-party integration for AI/ML.
Teradata offers efficient, scalable data management with fast query performance, robust security, automation, and cloud flexibility for businesses.
Microsoft Azure Synapse Analytics offers significant visibility, which helps us understand our usage more clearly.
The independence of the compute and storage within Snowflake is key.
Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses.
The data mover is valuable over the last two years as it allows us to achieve data replication to our disaster recovery systems.
 

Mindshare comparison

As of May 2025, in the Cloud Data Warehouse category, the mindshare of Microsoft Azure Synapse Analytics is 6.4%, down from 9.6% compared to the previous year. The mindshare of Snowflake is 19.6%, down from 22.9% compared to the previous year. The mindshare of Teradata is 8.7%, down from 9.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Renee Hoon - PeerSpot reviewer
Scalable performance tuning and optimization for finance and supply chain jobs
We run our finance and supply chain jobs using SQL BW, and we visualize our data loads on Power BI. We are using SQL Data Warehouse for data warehousing In terms of cost savings, we don't need specialization in skills as advanced as Teradata. We used to pay extensive licensing fees for Teradata,…
Snehasish Das - PeerSpot reviewer
Transformation in data querying speed with good migration capabilities
Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses. One key feature is the separation of compute and storage, which eliminates storage limitations. It also has tools for migrating data from legacy databases like Oracle. Its stability and efficiency enhance performance greatly. Tools in the AI/ML marketplace are readily available without needing development.
SurjitChoudhury - PeerSpot reviewer
Offers seamless integration capabilities and performance optimization features, including extensive indexing and advanced tuning capabilities
We created and constructed the warehouse. We used multiple loading processes like MultiLoad, FastLoad, and Teradata Pump. But those are loading processes, and Teradata is a powerful tool because if we consider older technologies, its architecture with nodes, virtual processes, and nodes is a unique concept. Later, other technologies like Informatica also adopted the concept of nodes from Informatica PowerCenter version 7.x. Previously, it was a client-server architecture, but later, it changed to the nodes concept. Like, we can have the database available 24/7, 365 days. If one node fails, other nodes can take care of it. Informatica adopted all those concepts when it changed its architecture. Even Oracle databases have since adapted their architecture to them. However, this particular Teradata company initially started with its own different type of architecture, which major companies later adopted. It has grown now, but initially, whatever query we sent it would be mapped into a particular component. After that, it goes to the virtual processor and down to the disk, where the actual physical data is loaded. So, in between, there's a map, which acts like a data dictionary. It also holds information about each piece of data, where it's loaded, and on which particular virtual processor or node the data resides. Because Teradata comes with a four-node architecture, or however many nodes we choose, the cost is determined by that initially. So, what type of data does each and every node hold? It's a shared-no architecture. So, whatever task is given to a virtual processor it will be processed. If there's a failure, then it will be taken care of by another virtual processor. Moreover, this solution has impacted the query time and data performance. In Teradata, there's a lot of joining, partitioning, and indexing of records. There are primary and secondary indexes, hash indexing, and other indexing processes. To improve query performance, we first analyze the query and tune it. If a join needs a secondary index, which plays a major role in filtering records, we might reconstruct that particular table with the secondary index. This tuning involves partitioning and indexing. We use these tools and technologies to fine-tune performance. When it comes to integration, tools like Informatica seamlessly connect with Teradata. We ensure the Teradata database is configured correctly in Informatica, including the proper hostname and properties for the load process. We didn't find any major complexity or issues with integration. But, these technologies are quite old now. With newer big data technologies, we've worked with a four-layer architecture, pulling data from Hadoop Lake to Teradata. We configure Teradata with the appropriate hostname and credentials, and use BTEQ queries to load data. Previously, we converted the data warehouse to a CLD model as per Teradata's standardized procedures, moving from an ETL to an EMT process. This allowed us to perform gap analysis on missing entities based on the model and retrieve them from the source system again. We found Teradata integration straightforward and compatible with other tools.
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Comparison Review

it_user232068 - PeerSpot reviewer
Aug 5, 2015
Netezza vs. Teradata
Original published at https://www.linkedin.com/pulse/should-i-choose-net Two leading Massively Parallel Processing (MPP) architectures for Data Warehousing (DW) are IBM PureData System for Analytics (formerly Netezza) and Teradata. I thought talking about the similarities and differences…
 

Top Industries

By visitors reading reviews
Educational Organization
51%
Computer Software Company
5%
Financial Services Firm
5%
Manufacturing Company
5%
Educational Organization
35%
Financial Services Firm
13%
Computer Software Company
9%
Manufacturing Company
6%
Financial Services Firm
26%
Computer Software Company
11%
Healthcare Company
7%
Manufacturing Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

How does Amazon Redshift compare with Microsoft Azure Synapse Analytics?
Amazon Redshift is very fast, has a very good response time, and is very user-friendly. The initial setup is very str...
What do you like most about Microsoft Azure Synapse Analytics?
The product is easy to use, and anybody can easily migrate to advanced DB.
What is your experience regarding pricing and costs for Microsoft Azure Synapse Analytics?
We use an enterprise-level subscription for Microsoft Azure Synapse Analytics. I would rate our satisfaction with the...
What do you like most about Snowflake?
The best thing about Snowflake is its flexibility in changing warehouse sizes or computational power.
What is your experience regarding pricing and costs for Snowflake?
Snowflake's pricing is on the higher side, rated as eight out of ten. If there were ways to reduce costs, it would be...
What needs improvement with Snowflake?
Cost reduction is one area I would like Snowflake to improve. The product is not very cheap, and a reduction in costs...
Comparing Teradata and Oracle Database, which product do you think is better and why?
I have spoken to my colleagues about this comparison and in our collective opinion, the reason why some people may d...
Which companies use Teradata and who is it most suitable for?
Before my organization implemented this solution, we researched which big brands were using Teradata, so we knew if ...
Is Teradata a difficult solution to work with?
Teradata is not a difficult product to work with, especially since they offer you technical support at all levels if ...
 

Also Known As

Azure Synapse Analytics, Microsoft Azure SQL Data Warehouse, Microsoft Azure SQL DW, Azure SQL Data Warehouse, MS Azure Synapse Analytics
Snowflake Computing
IntelliFlex, Aster Data Map Reduce, , QueryGrid, Customer Interaction Manager, Digital Marketing Center, Data Mover, Data Stream Architecture
 

Overview

 

Sample Customers

Toshiba, Carnival, LG Electronics, Jet.com, Adobe, 
Accordant Media, Adobe, Kixeye Inc., Revana, SOASTA, White Ops
Netflix
Find out what your peers are saying about Snowflake Computing, Microsoft, Google and others in Cloud Data Warehouse. Updated: April 2025.
850,043 professionals have used our research since 2012.