Astra DB, now part of IBM watsonx.data vs. Riak

We've been super happy with Astra DB. It's been extremely well-suited for our vector search needs as described in previous responses. With Astra DB’s high-performance vector search, Maester’s AI dynamically optimizes responses in real-time, adapting to new user interactions without requiring costly retraining cycles.

Incentivized

Riak is well suited to applications such as:

Transaction logging e.g. financial transactions and/or exchange rates.

Storing time series data, especially IoT.

Storing massive amounts of data e.g. corporation wide backups, data lakes etc.

A fully s3 compatible replacement for Amazon s3 ensuring data privacy.

Riak is not as well suited to:

Traditional RDBMS functions, especially those that join the outputs of one or more queries together to produce the desired result.

• We need to be able to process a lot of data (our biggest clients process hundreds of milions of transactions every month). However, it is not only the amount of data, it is also an unpredictable patterns with spikes occuring at different points of time - something athat Astra is great at.
• Our processing needs to be extremaly fast. Some of our clients use our enrichment in a synchronous way, meaning that any delay in processing is holding up the whole transaction lifecycle and can have a major impact on the client. Astra is very fast.
• A close collaboration with GCP makes our life very easy. All of our technology sits in Google Cloud, so having Astra in there makes it a no-brainer solution for us.

Incentivized

• Highly available: If nodes go offline for any reason, the system still operates.
• Highly scalable: There is a minimum of 5 nodes, which can handle a lot by themselves. When scaling is required, it can be done easily, with minimal to no downtime on large scales.
• Very fast searching: Riak has SOLR indexing built-into the core product, which makes querying for data very fast.

Incentivized

• Need better fine-grained Security options.
• The support team sometimes requires the escalate button pressed on tickets, to get timely responses. I will say, once the ticket is escalated, action is taken.
• They require better documentation on the migration of data. The three primary methods for migrating large data volumes are bulk, Cassandra Data Migrator, and ZDM (Zero Downtime Migration Utility). Over time I have become very familiar will all three of these methods; however, through working with the Services team and the support team, it seemed like we were breaking new ground. I feel if the utilities were better documented and included some examples and/or use cases from large data migrations; this process would have been easier. One lesson learned is you likely need to migrate your application servers to the same cloud provider you host Astra on; otherwise, the latency is too large for latency-sensitive applications.

Incentivized

• Deletes!!! We've seen on numerous occasions where Riak has "resurrected" deleted data. We've worked with Basho numerous times and tried multiple changes to the way we interact with Riak to prevent the problem but it still remains. The deletes seem to reappear weeks, even months, after the delete was issued. We've had to work around this issue by providing a "deleted" flag for all data objects stored in Riak. Thus, we do no delete but simply flip the flag. Excess baggage we would really like to not have to worry about.
• Search. Currently there's no way to tell what data you have in Riak without already knowing a particular bucket/key. There is a way to list the keys for a given bucket but due to performance implications, this is not a viable method to lookup data. Especially when you have a large amount of keys in the bucket.

Incentivized

Right now, I'm on a project where we need databases that can run on embedded systems. Riak isn't necessarily the best fit for that scenario. But when we need a clustered database, that's where we'd start considering Riak.

Incentivized

It's a great product but suffers with counters. This isn't a deal breaker but lets down what is otherwise a good all round solution

Incentivized

Their response time is fast, in case you do not contact them during business hours, they give a very good follow-up to your case. They also facilitate video calls if necessary for debugging.

Incentivized

Despite Basho going bankrupt and the project becoming fully open-source, community support is reasonably good, albeit a little slow at times. Paid enterprise-grade support is also available from former Basho engineers but the same company also contributes to the community support for free for basic questions or specific knowledge areas.

Graph, search, analytics, administration, developer tooling, and monitoring are all incorporated into a single platform by Astra DB. Mongo Db is a self-managed infrastructure. Astra DB has Wide column store and Mongo DB has Document store. The best thing is that Astra DB operates on Java while Mongo DB operates on C++

Incentivized

Because of the RESTful HTTP interface, the consistency model, and because of the catalog-driven data model, Riak was an easy win over Redis and Memcached.

Incentivized

We are well aware of the Cassandra architecture and familiar with the open source tooling that Datastax provides the industry (K8sSandra / Stargate) to scale Cassandra on Kubernetes.

Having prior knowledge of Cassandra / Kubernetes means we know that under the hood Astra is built on infinitely scalable technologies. We trust that the foundations that Astra is built on will scale so we know Astra will scale.

Incentivized

• Better uptime due to the managed service having no outages
• Less technical debt because we don't need to worry about upgrading our Cassandra clusters
• Lower cost on infrastructure as a whole
• Quick and easy to integrate vector search into our tech stack

Incentivized

• Riak has been a key part of our company's build process for our client's search backend. It is valuable for is in that it provides a reliable way to view the current search index.

Incentivized