203 Reviews and Ratings
8 Reviews and Ratings
It’s great for server less and real-time applications. It would be great for gaming and mobile apps. However, if you need relational database and have fixed budget, do not use it. While budget can be managed, you need to be careful. Also this is not a tool for storing big data, there are other wide-column database types you could use for it ins the ad
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.Incentivized
To manage varying workloads, it enables users to increase capacity as necessary and decrease it as needed.Users can take advantage of its auto-scaling, in-memory caching, and backup without paying for the services of a database administrator.We can use it for low scale operations.Incentivized
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.Incentivized
Cost model may not be easy to control and may lead to higher costs if not carefully plannedIndexing may be a cost culprit when not planned, because it's not included on the data costsThe Query Language may not fulfill everybody's expectations, as it has less features than those of competitors.Incentivized
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.Incentivized
It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.Incentivized
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.Incentivized
Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.Incentivized
It works very well across all the regions and response time is also very quick due to AWS's internal data transfer. Plus if your product requires HIPPA or some other regulations needs to be followed, you can easily replicate the DB into multiple regions and they manage all by it's own.Incentivized
I have not had to contact support for this service, however I have had to contact AWS for other services and their support has been good.Incentivized
The only thing that can be compared to DynamoDB from the selected services can be Aurora. It is just that we use Aurora for High-Performance requirements as it can be 6 times faster than normal RDS DB. Both of them have served as well in the required scenario and we are very happy with most of the AWS services.Incentivized
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.Incentivized
I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.Incentivized
Some developers see DynamoDB and try to fit problems to it, instead of picking the best solution for a given problem. This is true of any newer tool that people are trying to adopt.It has allowed us to add more scalability to some of our systems.As with any new technology there was a ramp up/rework phase as we learned best practices.Incentivized
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 CDHOne 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.Incentivized