Anaconda vs Teradata comparison

The compared Anaconda and Teradata solutions aren't in the same category. Anaconda is ranked #12 in Data Science Platforms , with an average rating of 8.7, and holds a 2.1% mindshare in the category. Teradata is ranked #3 in Data Warehouse , with an average rating of 7.5, and holds a 15.9% mindshare. Additionally, 95% of Anaconda users are willing to recommend the solution, compared to 87% of Teradata users who would recommend it.

Anaconda

Read 19 Anaconda reviews

1,375 Views
1,124 Comparison Views

95% willing to recommend

Teradata

Read 76 Teradata reviews

3,838 Views
2,879 Comparison Views

87% willing to recommend

Anaconda

Teradata

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Executive Summary

We performed a comparison between Anaconda and Teradata based on real PeerSpot user reviews.

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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

Anaconda

Average Rating

8.2

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Data Science Platforms (12th)

Teradata

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Customer Experience Management (6th), Backup and Recovery (20th), Data Integration (17th), Relational Databases Tools (8th), Data Warehouse (3rd), BI (Business Intelligence) Tools (10th), Marketing Management (6th), Cloud Data Warehouse (6th)

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Anaconda is designed for Data Science Platforms and holds a mindshare of 2.1%, up 2.1% compared to last year.
Teradata, on the other hand, focuses on Data Warehouse, holds 15.9% mindshare, up 15.5% since last year.

Data Science Platforms

Data Warehouse

Featured Reviews

Rohan Sharma

AI/ML Co-Lead at Developer Student Clubs - GGV

Provides all the frameworks and makes it easy to create environments for multiple projects

The best thing is that it provides all the frameworks and makes it easy to create environments for multiple projects using Anaconda. It is easy for a beginner to learn to use Anaconda. Comparatively, it is easier than using virtual environments or other environments because of the Conda environment. However, there are many things in Anaconda that people need to be aware of, so it can be challenging.

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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

"With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages."

"It has a lot of functionality available, supports many libraries, and the developers are continually improving it."

"The most advantageous feature is the logic building."

"It provides a unified platform where you can install Jupyter, Python Spider, and other related tools without needing separate installations."

"The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly."

"The solution is stable."

"Voice Configuration and Environmental Management Capabilities are the most valuable features."

"The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code."

More Anaconda pros

"Auto-partitioning and indexing, and resource allocation on the fly are key features."

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

"The ability to handle machine data parallel processing is the most valuable feature of Teradata."

"Viewpoint, the detailed query logs and performance statistics are valuable features."

"It is a stable solution. Stability-wise, I rate the solution a nine out of ten."

"The data mover is valuable over the last two years as it allows us to achieve data replication to our disaster recovery systems."

"It effectively has allowed us to remove over 20 portion copies of the data sets on other DB platforms for real-time operational reporting purposes."

"It has given our business the ability to gain insights into the data and create data labs for analysis and PoCs."

More Teradata pros

Cons

"When you install Anaconda for the first time, it's really difficult to update it."

"The solution would benefit from offering more automation."

"Anaconda can't handle heavy workloads."

"The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform."

"Anaconda consumes a significant amount of processing memory when working on it."

"One thing that hurts the product is that the company is not doing more to advertise it as a solution and make it more well known."

"I think that the framework can be improved to make it easier for people to discover and use things on their own."

"Anaconda should be optimized for RAM consumption."

More Anaconda cons

"I think the UI is not there yet. It could be improved by being more user-friendly."

"Teradata needs to pay attention to the cloud-based solution to make sure it runs smoothly."

"Teradata should focus on functionality for building predictive models because, in that regard, it can definitely improve."

"Teradata's UI could be improved."

"There are some ways that the handling of unstructured data could be improved."

"The increasing volumes of data demand more and more performance."

"The reporting side wasn't very good in the past, but with the latest versions, it's getting better. Still, the friendliness of the PDC reporting and functionality needs to be improved."

"I would like to see an improved Knowledge Base on the web."

More Teradata cons

Pricing and Cost Advice

"My company uses the free version of the tool. There is also a paid version of the tool available."

"The licensing costs for Anaconda are reasonable."

"The product is open-source and free to use."

"The tool is open-source."

"Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks."

"The cost of running Teradata is quite high, but you get a good return on investment."

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

"We are looking for a more flexible cost model for the next version that we use, whether it be cloud or on-premise."

"Make sure you have the in-house skills to design and support the solution, as relying on external sources is extremely costly and tends to lock you into specific platforms, tools, and paradigms."

"Teradata is not cheap, but you get what you pay for."

"When looking into implementing this product, pricing is the main issue followed by technical expertise."

"It comes at a notably high cost for what it offers."

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

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

Financial Services Firm

20%

Computer Software Company

Government

Manufacturing Company

Financial Services Firm

26%

Computer Software Company

11%

Healthcare Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Anaconda?

The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.

See all answers

What is your experience regarding pricing and costs for Anaconda?

Anaconda is an open-source tool, so I do not pay anything for it. It is compatible with every tool, regardless of whether it is open source or a paid package.

See all answers

What needs improvement with Anaconda?

There is room for improvement, especially regarding deployment. The process could be streamlined as the number of actions needed to deploy is quite large compared to other tools.

See all answers

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...

See all answers

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...

See all answers

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,...

See all answers

Comparisons

Databricks vs Anaconda

Compared 36% of the time

Amazon SageMaker vs Anaconda

Compared 12% of the time

Microsoft Power BI vs Anaconda

Compared 12% of the time

Microsoft Azure Machine Learning Studio vs Anaconda

Compared 7% of the time

IBM SPSS Statistics vs Anaconda

Compared 5% of the time

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Oracle Exadata vs Teradata

Compared 13% of the time

SQL Server vs Teradata

Compared 11% of the time

Snowflake vs Teradata

Compared 7% of the time

MySQL vs Teradata

Compared 6% of the time

IBM Db2 Database vs Teradata

Compared 6% of the time

More Teradata Competitors

Product Reports

Buyer's Guide

Anaconda

May 2025

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Buyer's Guide

Teradata

April 2025

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Also Known As

No data available

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

Overview

Anaconda makes it easy for you to install and maintain Python environments. Our development team tests to ensure compatibility of Python packages in Anaconda. We support and provide open source assurance for packages in Anaconda to mitigate your risk in using open source and meet your regulatory compliance requirements.

Python is the fastest growing language for data science. Anaconda includes 720+ Python open source packages and now includes essential R packages. This powerful combination allows you to do everything you want from BI to advanced modeling on complex Big Data

Anaconda

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.

Teradata

Sample Customers

LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone

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