IBM watsonx.data PayGo
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Astra DB adoption in Enterprise
What do you like best about the product?
Datastax was able to provide enterprise class product for an Open Source project Apache Cassandra. It is continuing to contribute to open source project. Datastax provides multiple offerings of Cassandra - Dedicated On prem model, Managed Service and Pay per use model.
While migrating from Self managed to Datstax Astra DB, it was ease of implementation that made migration super successful. On going improvements to the product with its roadmap is great to work with
While migrating from Self managed to Datstax Astra DB, it was ease of implementation that made migration super successful. On going improvements to the product with its roadmap is great to work with
What do you dislike about the product?
Datastax Enterprise Support is still not completely matured, it is still work in progress.
What problems is the product solving and how is that benefiting you?
We wanted to address two problems - Ongoing maintenance of clusters and high cost associated with Self managed Apache Cassandra clusters. By migrating to Astra DB Database as Service pay per use model, we are able to solve both the problems effectively
Superb database for AI generative models.
What do you like best about the product?
IBM Watsonx is an open source tool that contains safe features and by which I have successfully integrated it with my company's data process system. The intention was to create a secure AI based process system that the company could rely upon. The reason we set up IBM Watsonx as part of our new AI projects at first is for the good data science that comes along. For this cause, Watsonx is one of the most scalable tools.
What do you dislike about the product?
Even though IBM watsonx.data is very scalable, it is also very costly and resource heavy and the cost can drastically increase as you try to analyze large amount of data and you may even notice slight lag too.
What problems is the product solving and how is that benefiting you?
I think you should put your money into IBM Watson if you can afford it. Along this line, we are definitely utilizing the models of APIs to build a sustainable flow workplace. We do give support for AI and ML application on an ongoing basis.
Drives Efficiency and Governance through Streamlined Data Analaysis and Visualization
What do you like best about the product?
I really appreciate how it integrates comprehensive data warehouse optimization with performance analysis and antrual language processing. It handles missing values, outliers, and provides real-time insights and exceptional data visualization.
What do you dislike about the product?
For users unfamiliar with advanced features like machine learning models and RAG, there might be a learning curve. The support documentation is thorough but navigationg these features can be complex.
What problems is the product solving and how is that benefiting you?
Watsonx.data has greatly enhanced our data strategy by integrating large financial datasets, optimizing performance, and delivering real-time insights and visualization. Its strong data governance and advanced analytics, including machine learning and natrual language analysis, significantly boost our decision-making and operational efficiency.
The top data tool that can handle any and all requirements of a company.
What do you like best about the product?
IBM Watsonx.data is the best choice for the safe use and best-featured data for your management. Working in a large collection just when needed, starting from scratch up to using the data to get solutions for various data problems, could be helpful in terms of time and effort. With WatsonX.Data, we can quickly train, tweak, and test different machine learning models and then put them to use. The sandstone models are used for fine-tuning tasks that are specific to them, and granite is the base for GPT-like architecture. It saves time, money, friendly to the environment, and is also so much effective.
What do you dislike about the product?
The APIs integration could be improved and the performance lag should be reduced also.
What problems is the product solving and how is that benefiting you?
With it we can quickly train, teak, and test different machine learning models, which saves us both time and money.
Astradb for VectorStore
What do you like best about the product?
The astradb for vectorstore and its integration to langchain for ease of orchestration of RAG workflows.
Datstax team is phenomenally talented and supportive to customers.
They are open to consider the feedback for enhancing the latest technologies and integrations.
Datstax team is phenomenally talented and supportive to customers.
They are open to consider the feedback for enhancing the latest technologies and integrations.
What do you dislike about the product?
I like everything about datastax but would like for a more evolved UI interface.
What problems is the product solving and how is that benefiting you?
Datstax is solving us the LLM token limitations and cost effectiveness with RAG-Vector solution.
Go-to database for production grade LLM apps
What do you like best about the product?
Support and robustness.
Astra DB is rock-solid. We experienced a 50x traffic increase on our conversational chatbot "AI Guru" with zero downtime during a live demo with 1 million students. This is a testament to the scale at which AstraDB can deliver in production usecases.
Astra DB is rock-solid. We experienced a 50x traffic increase on our conversational chatbot "AI Guru" with zero downtime during a live demo with 1 million students. This is a testament to the scale at which AstraDB can deliver in production usecases.
What do you dislike about the product?
Data ingestion and retrieval for analytics can be improved. I am hoping it will be a solved problem in the coming months.
What problems is the product solving and how is that benefiting you?
We are using AstraDB as our vector database for our GenAI apps. That includes real-time doubt solving, Counselling and mentoring, subjective paper evaluation.
IBM watsonx.data is both secure and scalable and suitable for big data analysis.
What do you like best about the product?
Watson. data from IBM provides central data access and administration capabilities to streamline data governance and avoid duplication, which makes it ideal for artificial intelligence and analytics applications. Multiple engines and tools can query and process the same data simultaneously, courtesy of the support for open table formats that include apache iceberg. It also brings in variable degrees of deployment options to meet differing organizational needs, including managed services on both ibm cloud and aws, as well as self-managed applications on-prem. This will allow businesses to quickly infuse AI into their operations to enhance productivity and make better decisions, smartly integrated with other ibm ai technologies like watsonx.ai.
What do you dislike about the product?
When we first setup IBM watsonx.data, we had to go through a number of problems as it did not integrate with our existing system properly. So we had to constantly seek help from the official IBM support team. But after integrating it, we had no major issues and it is working correctly.
What problems is the product solving and how is that benefiting you?
It does well in integrating and managing massive datasets from a myriad of sources. This leads to my ability to centralize my data, hence improving governance over the data while also streamlining processes. Now, with this, my analyses are more precise, and therefore I spend less time in data preparation.
wx.data usage
What do you like best about the product?
Getting started with watsonx.data is pretty straightforward, making it accessible even for those new to data analysis. The platform offers an intuitive interface to setup processes with ease. wx.data's integration with other IBM services further simplifies the implementation of simple solutions, allowing users to leverage machine learning models and analytics tools. This streamlined approach enables users to focus on deriving insights and value from their data rather than getting bogged down in the complexities of data infrastructure setup.
What do you dislike about the product?
When we are talking about advanced and in-depth analytics, the platform still lacks easier and faster integrations in order to be used as a service in a Python notebook, the libraries are very complex and take too much time to handle simple requests.
What problems is the product solving and how is that benefiting you?
We are currently using wx.data as a lab for future clients.
IBM Watsonx.data is one of the best Data Analysis tool.
What do you like best about the product?
After almost five years of use, I must say that I have been very impressed by IBM Watsonx.data. However, this is such a powerful and intuitive platform for easier data administration and analysis. Due to its adaptability, it can deal with different data types. It interacts seamlessly with the AI tools. The most striking feature is the visualization of data with advanced analytics. It could handle both structured and unorganized data with absolute brilliance, thus increasing my productivity by significant margins. Watsonx.data has been my tool of choice in all my data work.
What do you dislike about the product?
IBM Watson.data is very resource heavy and since the introduction of AI, it can consume quite a bit of RAM. So the cost can drastically increase if you don't keep an eye on how much resources it is consuming.
What problems is the product solving and how is that benefiting you?
Unlike other data analysis platform, IBM watsonx.data can deal with different types of data types and is much more quicker and efficient.
My overall experience using IBM watsonx.data.
What do you like best about the product?
I've found the IBM Watsonx.data tool to be redefining how management and analysis of enormous datasets can be conducted. Its user friendly interface makes possible the preparation of data, considering a large number of sources and the AI features of this software are very good at getting insights fast. I also like the solid enterprise class security and scalability features. The real time analytics feature has dramatically improved my decision making process. The initial setup was rather complex and there's also a bit of a learning curve. In a word, it must be a powerhouse for whoever wants to use AI in data management and analytics.
What do you dislike about the product?
I personally don't dislike anything about IBM watsonx.data but many people around me have complained about their pricing structure as it is very hard to determine what your total bill will be at the end of the month.
What problems is the product solving and how is that benefiting you?
IBM watsonx.data is a very user friendly tool that helps us analyse large amounts of data rather quickly and extract useful insights from them.
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