IBM watsonx.data
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I can just drag and drop the workflow for my Datastax Langflow AI application
What do you like best about the product?
I like the wide range of Open-source options available included by datastax.
What do you dislike about the product?
I need more videos tutorials and templates for this datastax's Open-source platforms provided.
What problems is the product solving and how is that benefiting you?
I am using RAG for my e-commerce website to streamline my response for the customers with predefined dataset and the best thing it can train on its own.
Devops engineer
What do you like best about the product?
Cost efficiency great in the market, excelent scalability and optimized for AI data
What do you dislike about the product?
Complexity of the whole system and the learning curve is too much a burden compared with other ones
What problems is the product solving and how is that benefiting you?
Ai model training woks great with a relatively low cost
Great Data Platform, just a couple issues
What do you like best about the product?
IBM Watsonx.data has been super helpful for handling all of our data. One of the things I appreciate most is how easily it connects with different cloud platforms. We deal with a lot of data, and the platform has made it much easier to manage and analyze everything without slowing down. The AI features are a big plus—they save us time by automating a lot of the heavy lifting when it comes to data analytics.
What do you dislike about the product?
It’s definitely not the easiest platform to get the hang of. If you’re new to IBM’s tools, it can take a while to really figure things out. Setting it up and getting it customized for what we need took longer than expected, and the documentation could be a bit clearer, especially when you're trying to solve specific problems.
What problems is the product solving and how is that benefiting you?
We use Watsonx.data to make our data processing and analysis more efficient. It’s cut down the time we spend preparing and cleaning data, which lets us focus on actually getting insights from it. The AI features have really helped with predictive analytics, so we’re able to make smarter decisions based on real-time data. Overall, it’s improved our workflow a lot, but we’re still working through some of the more complicated setup.
Working on free experimental database for open access, publicly sourced data
What do you like best about the product?
The team has been in close contact throughout the process, providing tutorials and answers that helped us understand the expectations and processes.
The benefits have been customer support
The benefits have been customer support
What do you dislike about the product?
The tutorials often do not match my needs. I needed specific details to use the APIs in a format that did not match my expectations or possibly my needs. I am able to build apps that upload our data to DataStax. But I am unable to access the vector search parameters because the method that I used to upload the data is not the preferred method to query the database. In order to use the tutorials for querying the database, we would have to completely start from scratch with uploading the dataset using the same tutorials. Unfortunately, those tutorials are not set up in a manner that I understand how to upload the data. As such, I find that I might have to build my own database query pipeline
This is a problem for ease of use, ease of integration, ease of implementation
This is a problem for ease of use, ease of integration, ease of implementation
What problems is the product solving and how is that benefiting you?
We are starting a new, free experiment with DataStax to provide Wikidata's data to the world via a DataStax vector database.
We are new to the process of vectorising our data. DataStax has been very helpful in assisting us to build the database and understand the implementation of a vectorised database for integrating our data.
We are new to the process of vectorising our data. DataStax has been very helpful in assisting us to build the database and understand the implementation of a vectorised database for integrating our data.
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.
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