The Aerospike Real-time Data Platform aims to enable organizations to act instantly across billions of transactions while reducing server footprint up to 80%. The vendor states Aerospike multi-cloud platform powers real-time applications with predictable sub-millisecond performance up to petabyte scale with five-nines uptime with globally distributed, consistent data. Aerospike boasts customers such as Airtel, Experian, European Central Bank, Nielsen, PayPal, Snap, Verizon Media and Wayfair.
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Qubole
Score 5.1 out of 10
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Qubole is a NoSQL database offering from the California-based company of the same name.
We were developing an advertisement time auction application, where we had to store the client's personal details, advertisement-related details, location, and many other details. Moreover, we required a promotion, cookies, and a few more details from the front end. All this information is heavy in terms of size and cannot be lost if the server crash. So, we required an extremely fast disk database with high scalability and low throughput.
I find Qubole is well suited for getting started analyzing data in the cloud without being locked in to a specific cloud vendor's tooling other than the underlying filesystem. Since the data itself is not isolated to any Qubole cluster, it can be easily be collected back into a cloud-vendor's specific tools for further analysis, therefore I find it complementary to any offerings such as Amazon EMR or Google DataProc.
Providing an open selection of all cloud provider instance types with no explanation as to their ideal use cases causes too much confusion for new users setting up a new cluster. For example, not everyone knows that Amazon's R or X-series models are memory optimized, while the C and M-series are for general computation.
I would like to see more ETL tools provided other than DistCP that allow one to move data between Hadoop Filesystems.
From the cluster administration side, onboarding of new users for large companies seems troublesome, especially when trying to create individual cluster per team within the company. Having the ability to debug and share code/queries between users of other teams / clusters should also be possible.
If money isn't an issue, and you're not on the cloud, then I'd go with Aerospike. If you're the cloud ie, aws or azure, then i'd stick with dynamoDB or Cosmos then. Aerospike is definitely not something you want to put into the cloud. It doesn't work well w/ cross regions. If cross DC, you'll have to write some stuff for data integrity checks.
Personally, I have no issues using Amazon EMR with Hue and Zeppelin, for example, for data science and exploratory analysis. The benefits to using Qubole are that it offers additional tooling that may not be available in other cloud providers without manual installation and also offers auto-terminating instances and scaling groups.
We were in dilemma in deciding the database and it was the first time we were using Aerospike. Eventually, everything went as expected and resolved the client's requirement along with positive feedback and appreciation
Qubole was decided on by upper management rather than these competitive offerings. I find that Databricks has a better Spark offering compared to Qubole's Zeppelin notebooks.
We like to say that Qubole has allowed for "data democratization", meaning that each team is responsible for their own set of tooling and use cases rather than being limited by versions established by products such as Hortonworks HDP or Cloudera CDH
One negative impact is that users have over-provisioned clusters without realizing it, and end up paying for it. When setting up a new cluster, there are too many choices to pick from, and data scientists may not understand the instance types or hardware specs for the datasets they need to operate on.
