Microsoft Azure Synapse Analytics vs Teradata comparison

Microsoft Azure Synapse Ana...

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Executive SummaryUpdated on Jan 12, 2025

Teradata and Microsoft Azure Synapse Analytics compete in the large-scale data processing and analytics market. Microsoft Azure Synapse Analytics seems to have the upper hand due to its seamless integration within the Azure ecosystem and ease of use.

Features: Teradata is known for its parallel processing and scalability, offering reliable performance in structured data environments with powerful optimization features. Microsoft Azure Synapse Analytics provides strong analytics functions and flexibility for both structured and unstructured data with excellent integration capabilities.

Room for Improvement: Teradata could improve on pricing, flexibility in cloud environments, and handling of unstructured data. Microsoft Azure Synapse Analytics needs better integration for larger datasets, streamlined initial setup, and enhanced support for machine learning and IoT features.

Ease of Deployment and Customer Service: Teradata offers versatile deployment options, supporting on-premises and hybrid models with noted satisfaction in technical support. Microsoft Azure Synapse Analytics supports easier cloud-based deployments, with well-rated technical support and praised Azure services integration.

Pricing and ROI: Teradata is perceived as a high-end solution with significant costs, offering substantial ROI for large environments. Microsoft Azure Synapse Analytics offers flexible pricing models, including pay-as-you-go options, posing cost-effectiveness challenges for high usage scenarios.

To learn more, read our detailed Microsoft Azure Synapse Analytics vs. Teradata Report (Updated: February 2025).

Microsoft Azure Synapse Analytics vs. Teradata

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Review summaries and opinions

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

Categories and Ranking

Microsoft Azure Synapse Ana...

Ranking in Cloud Data Warehouse

2nd

Average Rating

8.0

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Teradata

Ranking in Cloud Data Warehouse

6th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Customer Experience Management (4th), Backup and Recovery (20th), Data Integration (16th), Relational Databases Tools (7th), Data Warehouse (3rd), BI (Business Intelligence) Tools (10th), Marketing Management (6th)

Mindshare comparison

As of March 2025, in the Cloud Data Warehouse category, the mindshare of Microsoft Azure Synapse Analytics is 6.7%, down from 10.6% compared to the previous year. The mindshare of Teradata is 8.9%, down from 9.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

Renee Hoon

Data Platform Specialist at Sasol

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,…

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SurjitChoudhury

Data engineer at Cocos pt

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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Quotes from Members

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

Pros

"It is a fantastic product; we are satisfied with its features and performance."

"Synapse Analytics' best feature is its ability to process large files."

"The product provides a seamless and adaptable approach to data processing and analytics."

"I love the way all the application resources from Microsoft can synchronize, help me with a lot of stuff, and reduce the time or hour of having maybe a resource for an hour a day while also being able to use artificial intelligence."

"The initial setup was really easy and straightforward."

"Very interactive and provides flexibility."

"Azure elasticity allows us to scale as much as we want, and it is pay-as-you-go, so we can scale up as we need to."

"It's quite quick for querying, even with large datasets, and it's scalable. It's also flexible to use, so it's easy to update and get data quickly without wasting time."

More Microsoft Azure Synapse Analytics pros

"Teradata is a great, industry-leading data warehousing product that has MPP architecture."

"The functionality of the solution is excellent."

"It has a solid set of tools and consulting services."

"Intelliflex is very scalable. In fact, scalability has improved 100 times by Intelliflex, in my personal opinion."

"I've never had any issues with scalability."

"Teradata has good performance, the response times are very fast. Overall the solution is easy to use. When we do the transformation, we have all of our staging and aggregation data available."

"It is very stable. It's 100% uptime. Speed and resilience are one of the greatest features of this product. In almost twenty years we've never had downtime, except for outages for patches and upgrades. We've never had a system failure in twenty years."

"It is a highly robust software solution."

More Teradata pros

Cons

"The support and price could improve."

"I'm not entirely happy with the billing model. I'm not entirely happy with how the enterprise services are pretty expensive, but that's about it."

"The initial setup has a bit of a learning curve."

"The initial setup process needs improvement. When you're moving to the cloud it takes a bit of time. It would be great if they could implement something that would make it faster."

"We'd, of course, always like to pay less for the service if we can."

"Integration with other vendors has limitations and could be improved."

"Microsoft Azure Synapse Analytics can improve by increasing the size of the files that we can load on the platform. We have some files that are too large to be loaded and it would be a benefit to us if the limit was increased. Additionally, the way we use the tool for generating reports can be made better. They should add some drag-and-drop rules without the need of programming these rules using some programming language. It would be helpful if we did not need someone that was technically advanced to be able to do it with, such as someone with no IT background. Having a reporting tool without code would be great."

"This solution needs to have query caching so that if the same query is run and the results are available, it will return the data from the cache without having to re-run the query."

More Microsoft Azure Synapse Analytics cons

"Azure Synapse SQL has evolved from a solely dedicated support tool to a data lake. It can store data from multiple systems, not just traditional database management systems. On the other hand, Teradata has limitations in loading flat files or unstructured data directly into its warehouse. In Azure Synapse SQL, we can implement machine learning using Python scripts. Additionally, Azure Synapse SQL offers advanced analytical capabilities compared to Teradata. Teradata is also expensive."

"Teradata could improve by being less complicated. There are some aspects that are not available on the Unix server and a Unix system is required to access some data, such as in case of an emergency."

"Teradata is somewhat late in adopting cloud technology."

"I'm not sure about the unstructured data management capabilities. It could be improved."

"The cost of Teradata Cloud Data Warehouse has room for improvement."

"The SQL Assistant is very basic. This tool can be improved for usability."

"It could use some more advanced analytics relating to structured and semi-structured data."

"The following could be better: licensing, architecture openness, integration with other tools."

More Teradata cons

Pricing and Cost Advice

"We normally pay between $300 and $500 per month, which is quite expensive for how much we actually use it, performance- and usage-wise. They have a cheap version and an expensive version, and our usage usually falls in the middle ground, which makes it not as cost-effective as it could be."

"The price of Microsoft Azure Synapse Analytics can vary. Other solutions are typically fixed prices and a fixed cost regardless of usage, but this solution is based on usage or consumption."

"This is an expensive solution."

"The price of this solution could be improved."

"I understand that Synapse Analytics' pricing is lower than Informatica's."

"Because it's cloud the cost is a different convention and the licensing costs are not the same."

"Our license is very expensive"

"The costs are billed such that we pay for what we use, which is a good thing."

More Microsoft Azure Synapse Analytics pricing and cost advice

"The cost is significantly high."

"The solution requires a license."

"Users have to pay a yearly licensing fee for Teradata IntelliFlex, which is very expensive."

"I rate the product price a nine on a scale of one to ten, where one is cheap and ten is expensive."

"Teradata used to be expensive, but they have been lowering their prices."

"The cost is substantial, totaling around $1.2 million, solely dedicated to upgrading the hardware."

"I would advise others to look into migration and setup as a fixed price and incorporate a SaaS option for other Teradata services."

"The price of the solution could be reduced, it is expensive."

More Teradata pricing and cost advice

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Comparison Review

it_user232068

Senior Data Architect at a pharma/biotech company with 1,001-5,000 employees

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…

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Top Industries

By visitors reading reviews

Educational Organization

52%

Financial Services Firm

Computer Software Company

Manufacturing Company

Financial Services Firm

27%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

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 straightforward. This solution can merge and integrate well with many different dat...

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?

The financial aspect, including pricing and cost reduction, is not something I focus on.

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 declare Teradata better than Oracle is the pricing. Both solutions are quite simi...

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 it would be compatible with our field. According to the product's site, the comp...

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 you just ask. There are some features that may cause difficulties - for example,...

SAP BW4HANA vs Microsoft Azure Synapse Analytics

Comparisons

Compared 18% of the time

Oracle Autonomous Data Warehouse vs Microsoft Azure Synapse Analytics

Compared 15% of the time

Snowflake vs Microsoft Azure Synapse Analytics

Compared 12% of the time

Azure Data Factory vs Microsoft Azure Synapse Analytics

Compared 9% of the time

AWS Lake Formation vs Microsoft Azure Synapse Analytics

Compared 8% of the time

More Microsoft Azure Synapse Analytics Competitors

Oracle Exadata vs Teradata

Compared 13% of the time

SQL Server vs Teradata

Compared 11% of the time

MySQL vs Teradata

Compared 7% of the time

Snowflake vs Teradata

Compared 7% of the time

Oracle Database vs Teradata

Compared 3% of the time

More Teradata Competitors

Product Reports

Microsoft Azure Synapse Analytics

Download Microsoft Azure Synapse Analytics product report

Microsoft Azure Synapse Analytics report

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Also Known As

Azure Synapse Analytics, Microsoft Azure SQL Data Warehouse, Microsoft Azure SQL DW, Azure SQL Data Warehouse, MS Azure Synapse Analytics

IntelliFlex, Aster Data Map Reduce, , QueryGrid, Customer Interaction Manager, Digital Marketing Center, Data Mover, Data Stream Architecture

Overview

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:

Synapse SQL
Spark
Synapse Pipeline
Studio

Microsoft Azure Synapse Analytics Features

Microsoft Azure Synapse Analytics has many valuable key features, including:

Cloud Data Service: WIth Microsoft Azure Synapse Analytics you can operate services (data analytics, machine learning, data warehousing, dashboarding, etc.) in a single workspace via the cloud.
Structured and unstructured data: Microsoft Azure Synapse Analytics supports both structured and unstructured data and allows you to manage relational and non-relational data - unlike data warehouses and lakes that tend to store them respectively.
Effective data storage: Microsoft Azure Synapse Analytics offers next-level data storage with high availability and different tiers.
Responsive data engine: Microsoft Azure Synapse Analytics uses Massive Parallel Processing (MPP) and is designed to handle large volumes of data and analytical workloads efficiently without any problems.
Several scripting languages: The solution provides language compatibility and supports different programming languages, such as Python, Java, Spark SQL, and Scala.
Query optimization: Microsoft Azure Synapse Analytics works well to facilitate limitless concurrency and performance optimization. It also simplifies workload management.

Microsoft Azure Synapse Analytics Benefits

Some of the benefits of using Microsoft Azure Synapse Analytics include:

Database templates: Microsoft Azure Synapse Analytics offers industry-specific database templates that make it easy to combine and shape data.
Better business insights: With Microsoft Azure Synapse Analytics you can expand discovery of insights from all your data and apply machine learning models to all your intelligent apps.
Reduce project development time: Microsoft Azure Synapse Analytics makes it possible to have a unified experience for developing end-to-end analytics, which reduces project development time significantly.
Eliminate data barriers: By using Microsoft Azure Synapse Analytics, you can perform analytics on operational and business apps data without data movement.
Advanced security: Microsoft Azure Synapse Analytics provides both advanced security and privacy features to ensure data protection.
Machine Learning: Microsoft Azure Synapse Analytics integrates Azure Machine Learning, Azure Cognitive Services, and Power BI.

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."

Microsoft

Teradata is a scalable data analytics platform designed to meet enterprise demands for large-scale data management and processing, focusing on performance, scalability, and security for complex query executions.

As a leading data warehousing solution, Teradata integrates advanced analytics enabling organizations to derive insights from massive datasets. It supports high-volume data workloads with its architecture optimized for analytical queries. Users benefit from its robust scalability, allowing seamless expansion as data grows. Teradata's SQL engine is compatible with a wide range of data types, ensuring flexibility in data analysis. With advanced security measures, it protects sensitive data across various environments, providing peace of mind to users handling critical information.

What are the most important features of Teradata?

Linear Scalability: Easily expands data processing capabilities to handle increasing data loads without performance degradation.
Advanced Analytics: Supports complex analytical operations integrating machine learning and AI for predictive insights.
SQL Compatibility: Offers a comprehensive SQL interface that supports complex queries across diverse data types.
Data Security: Implements rigorous security protocols to protect data integrity and confidentiality.

What benefits and ROI should users look for?

Improved Decision-Making: Derive actionable insights quickly for strategic business decisions.
Cost Efficiency: Optimized resource usage reduces operational costs associated with data processing.
Increased Productivity: Streamlined data operations save time, allowing teams to focus on analysis.
Enhanced Scalability: Seamless growth without significant additional investment as data volumes increase.

Teradata is widely used in industries like finance, telecommunications, and healthcare, where data-driven decisions are critical. Companies leverage its robust analytics capabilities to enhance customer experiences, streamline operations, and ensure compliance with regulatory requirements. In these sectors, quick access to data insights can significantly impact competitive advantage.

Sample Customers

Toshiba, Carnival, LG Electronics, Jet.com, Adobe,

Netflix

Microsoft Azure Synapse Analytics vs. Teradata