Likelihood to Recommend If you want a serverless NoSQL database, no matter it is for personal use, or for company use, Google Cloud Datastore should be on top of your list, especially if you are using Google Cloud as your primary cloud platform. It integrates with all services in the Google Cloud platform.
Read full review 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.
Read full review Pros Automatically handles shards and replication. Schema-less & NoSQL. Fully managed. Read full review From a UI perspective, I find Qubole's closest comparison to Cloudera's HUE; it provides a one-stop shop for all data browsing and querying needs. Auto scaling groups and auto-terminating clusters provides cost savings for idle resources. Qubole fits itself well into the open-source data science market by providing a choice of tools that aren't tied to a specific cloud vendor. Read full review Cons It is hosted on GCP, which makes it harder if your company have multi-cloud strategy. When you want to migrate to other cloud providers, there can be a caveat. Read full review 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. Read full review Likelihood to Renew For the amount of use we're getting from Google Cloud Datastore, switching to any other platform would have more cost with little gain. Not having to manage and maintain Google Cloud Datastore for over 4 years has allowed our teams to work on other things. The price is so low that almost any other option for our needs would be far more expensive in time and money.
Read full review 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.
Read full review Alternatives Considered We selected Google Cloud Datastore as one of our candidates for our NoSQL data is because it is provided by Google Cloud, which fits our needs. Most of our infrastructure is on Google Cloud, so when we think about the NoSQL database, the first thing we thought about is Google Cloud Datastore. And it proves itself.
Read full review 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.
Read full review Return on Investment Simple billing part of Google Cloud Platform No time spent configuring and maintaining Google Cloud Datastore. Very good uptime for our applications. Read full review 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. Read full review ScreenShots