Watsonx.data is presented as an open, hybrid and governed data store that makes it possible for enterprises to scale analytics and AI with a fit-for-purpose data store, built on an open lakehouse architecture, supported by querying, governance and open data formats to access and share data.
Snowflake is a more mature, simpler to use product but watsonx.data has a more open architecture and it better for hybrid cloud environments. In addition, watsonx.data is part of an entire watsonx platform that offers many advantages over is closest competitors. The single …
IBM watsonx.data stacks up against Snowflake very well. It come in at a less expensive price. Also, you can run IBM watsonx.data on any cloud. or on prem.. Much more flexible.
with iceberg open table format and presto engine the performance and flexibility increased and also with watsonx.ai with GENAI capability which other tools lag as of now.
We use IBM watsonx.data as a unified data platform to integrate and govern data across systems, eliminating silos and improving data quality. Its open lakehouse architecture enables faster, trusted access to data for AI, analytics, and reporting, forming the foundation for …
Oracle really cost effective solution, where it has the support of community, with rich integration of all wide range of oracle products. Amazon sageMaker is another cost effective solution, where is tightly coupled with AWS platform, in terms of performance it copes up really …
IBM watsonx.data integrates well with other IBM services used in our deployment and provides enterprise grade security which is critical for our regulated business
AstraDB was giving me vector database solutions, Retrieval Augmented Generation features and even Agentic workflows that IBM watsonx.data does not have currently. But the volume of data I've coming everyday and has to deal with everyday, can do anomaly detection just in plain …
Pinecone and IBM watsonx.data (Milvus in our case) both work great as a full-managed cloud-based vector database. We selected IBM watsonx.data because it integrates well with watson.ai and is a little more beginner friendly than pinecone, but I think both are great anyway.
IBM watsonx.data helps in reducing data warehousing costs. IBM AIOps Insights focuses mainly on incident management, while IBM watsonx.data provides a flexible data store.
May be I cannot say why I choose, business preferred to use IBM watsonx.data which is good for me as well to learn. I cannot compare this tool with others because it has unique feature which alteryx or Amazon or Azure dont have. So this tool is going good for us.
I believe DataStax Enterprise is the best in class. There are some things that are different with the schema-less systems but I found DataStax Enterprise easiest to implement while evaluating. The replication is on par or better than others in practice. We are evaluating …
DataStax Enterprise offered best-in-class write performance and scalability. The customer support team was very helpful in the adoption of new technology.
DataStax has an amazing community built around it and is also Cassandra is an open-source technology. The customer support is quite good compared to other vendors. Though you initially need to spend some hefty amount on infrastructure, in the long run, it makes up for it. We …
We chose datastax because we need a system always available and capable of ingesting a large amount of data per second, even if eventually consistent and with multi data center sync native support.
We considered Cloudera as an alternative using Kafka as the ingestion layer but …
Amazon DynamoDB and Datastax Cassandra are similar on masterless architecture and principles, DynamoDB is managed and needs cost analysis. If you need to have better control, Datastax is better.
I also did a prototype with Google Spanner in one of the recent innovation days, it …
Datastax Cassandra is a Java based linearly scalable NoSQL database, best-in-class tunable performance, fault tolerant, distributed, masterless, time series database and has easy-to-use administration and monitoring functionality with opscenter. Configured correctly there is no downtime and no data loss. The documentation is exhaustive, and the community is agile and supportive, and Datastax provides good support. For all these reasons, Datastax Cassandra has become a NoSQL technology of choice for many platforms. However it has some time investment on infrastructure and regular operational tasks, and if you do not have bandwidth for it, a managed NoSQL solution like DynamoDB might be more appropriate. Also if you have search needs on Cassandra and do not have corresponding Spark/Solr setup, Datastax Cassandra might not be ideal for you.
Integration complexity with Security Tools while watsonx.Data is well-suited for native tools, but integration with third-party security tools requires custom connectors or manual ETL pipelines. which leads to an increase in setup time.
As an open source technology Cassandra can be readily used with or without any commercial support. DataStax provides value-added services and features, and in the end it is up to individual situations to strike a balance between the desirability of such support/service versus the associated cost.
DataStax has a good community built around it and has amazing scalability options. Though the initial setup is a bit costly, in the long run, it makes up for it. It also has powerful monitoring tools and a clean UI.
We have had a few situations where we caused an outage or something has gone wrong and we are able to get a support person to offer live help within minutes. The escalation process is excellent - the best I've seen - and the support team is incredibly strong. Outside of emergencies, the team is very helpful with general questions and working through data model exercises and the subscription I believe still comes with some hours to help get the data model reviewed.
I believe DataStax Enterprise is the best in class. There are some things that are different with the schema-less systems but I found DataStax Enterprise easiest to implement while evaluating. The replication is on par or better than others in practice. We are evaluating Astra in our test environment and that has additional benefits we are looking forward to using.