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Alteryx vs Anaconda 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:
 

Mindshare comparison

Predictive Analytics
Data Science Platforms
Data Warehouse
 

Featured Reviews

Theresa McLaughlin - PeerSpot reviewer
Quick development enables seamless data processing despite occasional support issues
There were times when the product would fail during development without an apparent reason. The support structure changed; initially, we received great support, however, it later became less reliable due to licensing issues and a tiered support system. Licensing negotiations were problematic, affecting our product usage. For instance, our licenses were temporarily lost during negotiations when an agreement couldn't be reached.
Rohan Sharma - PeerSpot reviewer
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.
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.

Quotes from Members

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

Pros

"Alteryx's connectivity is essential. We like the ability to connect the solution to multiple sources. It's easier than other data modeling and extraction solutions. It's built on a self-service concept, so it's easy for anyone to open the tool and directly import or export data from it."
"The design portion of this tool is easy to use without code, which his something that something we can appreciate."
"My primary focus is creating numerous data pulls, and Alteryx Server handles the automation well."
"Alteryx is a simple and easy-to-use solution."
"The three data signs and data engineering are great features."
"The scheduling feature for the automation is excellent."
"The data transformation feature is the most valuable. The ability to ingest data, visualize data, and transform that data is useful."
"One-stop shop for data preparation, blending, prediction, and optimization in a single workflow."
"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."
"The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly."
"The most valuable feature is the set of libraries that are used to support the functionality that we require."
"The documentation is excellent and the solution has a very large and active community that supports it."
"Voice Configuration and Environmental Management Capabilities are the most valuable features."
"It has a lot of functionality available, supports many libraries, and the developers are continually improving it."
"The virtual environment is very good."
"It helped us find find the optimal area for where our warehouse should be located."
"The data processing, clustering, and distributed computing are impressive."
"The performance is great, we are able to query our data in one operation."
"Teradata's best feature is its speed with historical data."
"I like writing preformance queries for preprocessing on AWS Cloud."
"It has a solid set of tools and consulting services."
"It's a pre-configured appliance that requires very little in terms of setting-up."
"The most valuable feature of Teradata is the quick processing of large data."
"Teradata can be deployed on-premise, on the cloud, or in a virtual machine, which means customers can move without having to create their architecture all over again."
 

Cons

"The only area where the product lags is documentation and videos on the analytical app and the batch macro."
"There are a few imputation techniques which they really need to include."
"What they're struggling with is it's not as mature as Tableau in the user management area. It was tougher to manage the server part of it right away, especially since the user base has grown."
"I'd like to see more artificial intelligence business tools or features in Alteryx."
"There is currently no cloud solution and this would be valuable for many clients."
"There are a few hiccups with specific data sets and languages or formats that the data comes in. That may be a minor problem, but we can work through it. We had some issues looking at XML format in added data, but it wasn't significant."
"In the database, it should be more functional and connect to more big data, especially using IPI."
"They can provide some pre-built tools for predictive analytics instead of us having to build all the tools. It should also be improved from the visualization aspect. It should have better visualization capabilities. There are tools out there that have better visualization capabilities, which Alteryx is lacking currently."
"It crashes once in a while. In case of a reboot or something unexpected, the unseen code part will get diminished, and it relatively takes longer than other applications when a reboot is happening. They can improve in these areas. They can also bring some database software. They have software for analytics and virtualization. However, they don't have any software for the database."
"When you install Anaconda for the first time, it's really difficult to update it."
"The process could be streamlined as the number of actions needed to deploy is quite large compared to other tools."
"Anaconda consumes a significant amount of processing memory when working on it."
"Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring."
"I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."
"Anaconda should be optimized for RAM consumption."
"The interface could be improved. Other solutions, like Visual Studio, have much better UI."
"The scalability could be better. The on-premises solution is always more complicated to scale."
"Teradata has a few AI models, but in data science, we need more flexibility."
"Teradata hardly supports unstructured data or semi-structured data"
"The increasing volumes of data demand more and more performance."
"I would like to see an improved Knowledge Base on the web."
"The setup is not straightforward."
"Needs compatibility with more Big Data platforms."
"It could be a bit more user-friendly."
 

Pricing and Cost Advice

"I rate the solution's pricing as a ten, as it is highly priced."
"In order to have designers, and, if you want to collaborate, you have to buy a server. If the designer is $5,000, and if you want a server, you have to pay $80,000."
"Alteryx is generally more suited for medium—to large companies due to its potentially high licensing costs."
"Alteryx isn't extortionately expensive, but it's not cheap either."
"The desktop platform costs $5,000 per year. It's very costly."
"The solution has a more costly license than other tools in the market."
"The pricing is $5000 per year per production license."
"If one is a high price, and ten is a low price, I rate the tool's price as a one. The tool is expensive."
"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."
"My company uses the free version of the tool. There is also a paid version of the tool available."
"The product is open-source and free to use."
"The licensing costs for Anaconda are reasonable."
"Teradata is currently making improvements in this area."
"The solution requires a license."
"The initial cost may seem high, but the TCO is low."
"The cost is significantly high."
"We had a lot of parties involved when purchasing from the AWS Marketplace. They are very flexible and aggressive in trying to close the deal. They are good at what they have to offer and listening to the customer. It's a two-way street."
"Teradata is expensive but gives value for money, especially if you don't want to move your data to the cloud."
"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 expensive, so it's typically marketed to big customers. However, there have been some changes, and Teradata is now offering more flexible pricing models and equipment leasing. They've added pay-as-you-go and cloud models, so it's changing, but Teradata is generally known as an expensive high-end product."
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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
Financial Services Firm
24%
Manufacturing Company
9%
Computer Software Company
9%
Healthcare Company
6%
Financial Services Firm
20%
Computer Software Company
9%
Manufacturing Company
8%
Government
8%
Financial Services Firm
26%
Computer Software Company
11%
Manufacturing Company
7%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?
One of the differences is that with Alteryx you can use it as an ETL and analytics tool. Please connect with me direc...
What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?
Alteryx is an extremely easy and flexible data tool, flexible in terms of drag and drop toolset and also has python, ...
What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?
I am not familiar with IBM SPSS Modeler, therefore, I cannot compare these two products. Regarding Alteryx I can say...
What do you like most about Anaconda?
The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using...
What is your experience regarding pricing and costs for Anaconda?
Anaconda does not require a pricing structure, and it is available as an open-source tool. The features of Python, Ju...
What needs improvement with Anaconda?
Anaconda consumes a significant amount of processing memory when working on it. This is something that needs improvem...
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 ...
 

Comparisons

 

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Overview

 

Sample Customers

AnalyticsIq Inc., belk, BloominBrands Inc., Cardinalhealth, Cineplex, Dairy Queen
LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone
Netflix
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