IBM watsonx.data as a Service
IBM SoftwareExternal reviews
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Personal review
What do you like best about the product?
DataStax offers high performance, scalability, and enterprise-grade features built on Apache Cassandra, making it ideal for handling large-scale, real-time data.
What do you dislike about the product?
Setting up and managing it can be complex, especially for beginners, and pricing may be high for smaller teams.
What problems is the product solving and how is that benefiting you?
DataStax makes it easy to handle big data and ensures high availability with minimal downtime. It helps us scale smoothly and manage data across multiple locations.
Powerful Data Platform with AI Integration
What do you like best about the product?
The most impressive part, however, is how AI and analytics work together, enabling data query and management on both structured and unstructured formats from a single platform. It is also Agile, scale-outable, and interoperable with open data formats like Parquet and Iceberg.
What do you dislike about the product?
IBM watsonx is undoubtedly powerful, but it is not without its drawbacks. For teams inexperienced with IBM’s ecosystem, the setup is multifaceted, the integration is tedious, and the ramp-up phase can be frustrating due to the advanced learning curve. Pricing models are often ambiguous for smaller teams, and along with uneven performance on larger datasets, it becomes increasingly costly. Furthermore, community support is limited and still in the developmental phase, leading to fears around vendor lock-in.
What problems is the product solving and how is that benefiting you?
IBM Watsonx.data addresses critical issues concerning the accessibility, integration, and analytics of data at scale. It helps by consolidating structured and unstructured data across multiple clouds and on-premises systems utilizing an open data lakehouse framework. This allows me to analyze and parse through extensive datasets from various locations without physically relocating them, thus optimizing processes and minimizing expenses associated with storage. It also ensures governance, security, and AI model readiness which supports me by accelerating trusted decision-making while simplifying the operational processes from raw data into insights.
IBM Watson studio best for learning and application for machine learning
What do you like best about the product?
Best in using loaded data interact with datasets and use accordingly and learn with projects
What do you dislike about the product?
UI can be more specific and easy to understand the flow
What problems is the product solving and how is that benefiting you?
Learning project through ciursera
Easy and Reliable
What do you like best about the product?
User friendly, easy click and connect features, End to end data services.
i like the data security with governance, keep the lilits of the data. I frequently use this for my easy data integration and processing
i like the data security with governance, keep the lilits of the data. I frequently use this for my easy data integration and processing
What do you dislike about the product?
cosstly to use with heavy resources, ifeel i should incorporate more anytical parts and streamlining of data
What problems is the product solving and how is that benefiting you?
Watsonx is very reliable for non coding analysts with easy navigation and ease of integration of data. its a great platform to streamline data and great use of AI. Mainly it provide great data governance to share data across the team with no worries
Review of IBM watsonx.data
What do you like best about the product?
Seamless Integration: Watsonx.data integrated smoothly with our existing hybrid cloud setup and data lakes, eliminating the need for costly migrations. It effortlessly pulled data from SQL databases, CRM platforms, and even legacy systems.
AI-Powered Insights: The built-in automation for data governance saved us countless hours. For example, identifying anomalies in real-time transaction data became 40% faster, improving our fraud detection accuracy.
Scalability: Handling terabytes of customer behavior data during peak sales periods was seamless. We scaled resources without downtime, which was a game-changer for quarterly reporting.
Hybrid Flexibility: The ability to deploy on-premises and cloud environments gave us control over sensitive data while leveraging cloud elasticity for analytics workloads.
AI-Powered Insights: The built-in automation for data governance saved us countless hours. For example, identifying anomalies in real-time transaction data became 40% faster, improving our fraud detection accuracy.
Scalability: Handling terabytes of customer behavior data during peak sales periods was seamless. We scaled resources without downtime, which was a game-changer for quarterly reporting.
Hybrid Flexibility: The ability to deploy on-premises and cloud environments gave us control over sensitive data while leveraging cloud elasticity for analytics workloads.
What do you dislike about the product?
Learning Curve: Initial setup required deep dives into documentation, and some team members found the interface non-intuitive. However, IBM’s customer support provided helpful tutorials.
Cost Considerations: While powerful, the pricing model may be prohibitive for smaller teams. We’d love more flexible tiers for mid-sized projects.
Cost Considerations: While powerful, the pricing model may be prohibitive for smaller teams. We’d love more flexible tiers for mid-sized projects.
What problems is the product solving and how is that benefiting you?
campaign analysis, watsonx.data correlated social media sentiment with sales data, revealing untapped customer segments. This led to a 15% boost in targeted ad ROI—a win our execs celebrated.
My review
What do you like best about the product?
The IBM watsonx.data has the built-in machine learning features.so it helps us to find the risk,fraud and autumate the services. It is helpful in financial services like finding frauds,risk factors of financial growth/development etc.
What do you dislike about the product?
Most dislike about watsonx.data is like sometimes it is too slow.
What problems is the product solving and how is that benefiting you?
It is helping us to process the larger data sets efficiently.
Best Vector DB in the market
What do you like best about the product?
Advanced vector search capabilities, real-time processing, and fully managed database services.
What do you dislike about the product?
Overall the experience was good while leveraging Datastax Astra DB.
What problems is the product solving and how is that benefiting you?
Managing vast volumes of unstructured data across domains required a database solution capable of handling high-throughput, low-latency queries while ensuring data relevance and accuracy.
Seamless communication between your app & cassandra
What do you like best about the product?
Simplified database operations with builtin support for query execution.
DataStax's QueryBuilder API can handle diverse types of queries
DataStax's QueryBuilder API can handle diverse types of queries
What do you dislike about the product?
Learning curve can be steep because of their long list of Query APIs.
What problems is the product solving and how is that benefiting you?
Datastax help overcome the complexity of handling distributed database systems like cassandra.
The drivers can efficiently interact with cassandra without compromising on latency and HA
The drivers can efficiently interact with cassandra without compromising on latency and HA
Started with comparison, but ended up using it for for my MVP
What do you like best about the product?
I would really stress on the Simplicity, when compared to other vectorstores I really liked the astradb and Langflow as well where I played around rag for my MVP and I was the one to introduce the langflow in my team
What do you dislike about the product?
DataStax has a steep learning curve and high costs.
What problems is the product solving and how is that benefiting you?
For rag basically and also used it to compare it with other databases
More than a partner
What do you like best about the product?
I started using AstraDB because it was mentioned as one of the options available to implement memory in Langchain.
The implementation was straightforward and it's worked seamesly from day one.
In the DataStrax team I found a reliable partner and they helped me to find solutions when I was stuck in the process, advicing and using their experience and experties to our service.
The implementation was straightforward and it's worked seamesly from day one.
In the DataStrax team I found a reliable partner and they helped me to find solutions when I was stuck in the process, advicing and using their experience and experties to our service.
What do you dislike about the product?
Honestly nothing. Even when my requests were outside their main experities, they found a way to be supportive.
What problems is the product solving and how is that benefiting you?
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