Qubole Reviews

I like all the great and innovative features of this. It is easy to manage big data programmatically. All the features part are easy to use and understandable.

The user interface needs to improve. When the query is executed, it returns the number of query. The notebook feature is not user-friendly. The interface is straightforward.

It helps us save a lot of costs. Massive data can be extracted easily. all function is necessary for data analysis. Support service is beneficial to me. Maintaining and cleaning big data very quickly.

Qubole is an excellent self- service platform to host Big Data. The ease of managing data on S3 which can be the end place of data, coupled with Hadoop's HDFS system provide an ideal environment for ETL processes.

Qubole has fewer ETL tools in comparison to its competitors. Also, cluster management seemed to be a problem sometime. A feature that allows team members to share codes amongst them will also benefit analysts working in a team.

It is an amazing data lake platform to access Big Data.

I worked on Qubole in a Hadoop environment and used Hive for querying on the data. We used S3 to store clients' sales data on the back-end. Considering our database had ~150mn rows of data, I was impressed by the querying speed, even when running multiple queries.

All great and innovative features of this

I do not see anything to dislike here. All Superb.

machine learning, streaming, and ad-hoc analytics

Love the exquisite technology presented by Qubole, the products really meet the global standards.

There is nothing much to dislike here, but I believe they lack some marketing boost towards promoting their products more openly.

I am using Qubole notebooks to perform Data analysis on my firm's huge chunks of sensory data generated from a couple of our products and thus helping my firm to understand evolving challenges of the business and make required decisions using the same knowledge.

Seamless Integration. Unparalleled Personalised Support.

The constant need to update services or request support for simpler things - don't get me wrong, the necessities are taken care of - just the smaller things need some finesse.

Understand the platform and learn from the tutorials + ask queries during the demonstration to help ease your access into the workings of the platform.

Model Compartmentalisation. Multiple Experiments. Continuous Changes and Runs of Models. Data Science Overview, Simplicity and Ease.

Benefits: Ease of Working and Scheduling; Complete Understanding of the Complicated Aspects of Data Science is covered with Qubole and the Support Team - with their professionalism in understanding the problem and their turn around time is something that makes Qubole stand out.

Qubole provides uninterrupted analysis of data regardless of the number and types of jobs being carried out, it also allows for the scaling of data and users without any additional cost.

Fewer tools are provided for moving data between Hadoop file system

Qubole is a really good tool to use when getting started with the analysis of data in the cloud

I personally use Qubole to analyze big data and use it to scale data depends on it size.

Qubole is incredibly easy to use. We were able to implement our Big Data workloads very easily. We got great support from them during implementation. We are continuing to urge great support with our new projects and existing implementations. it's scaled with our increased needs for processing. the largest advantage is that it's reduced our cloud computing costs

The product has the ability for interactive serverless query service and data science notebooks. it's not as feature-rich and compelling as other competitors. Hence, we are using other services for this purpose.

Qubole is a great platform for reducing cloud computing costs

With Qubole, we have been able to reduce the time to market our products and increase our work output rate. Qubole has generally increased our efficiency

1. Customer service and representatives. They are very patient, friendly and knowledgeable. They always show up on time for the office hours. The technical questions can be answered on the spot!

2. We can have the questions answered in a timely fashion using the support portal. Also, the support persons are following up on any questions we have using the Slack channel until the issue is solved!

3. The product is stable and continuously improving. The product release is communicated well to us.

4. We can use modern technologies such as Spark, Airflow etc. on Qubole.

1. Cost. It would be great to have discounts on the cost for my organization. We are small org and would like to cut down the cost as much as we can.

2. Also, I would like Qubole to release new features as often as possible. For example, integrating Jupyter: our data scientists are waiting for this.

1. We are using Qubole for data ETL jobs with schedulers.

2. The clusters can be scale up and down. It makes our job much easy on the execution side.

3. Our Data Scientists also use Notebooks for their ML model.

4. We use Qubole to set up ML pipeline for the execution.

5. The good aspect of using Qubole is the easy to use, one place to track all the jobs and track the status with logs etc. etc.

6. We also use Dashboard to track the status of our data products.

1. Customer service and representatives. They are very patient, friendly and knowledgeable. They always show up on time for the office hours. The technical questions can be answered on the spot!

2. We can have the questions answered in a timely fashion using the support portal. Also, the support persons are following up on any questions we have using the Slack channel until the issue is solved!

3. The product is stable and continuously improving. The product release is communicated well to us.

4. We can use modern technologies such as Spark, Airflow etc. on Qubole.

1. Cost. It would be great to have discounts on the cost for my organization. We are small org and would like to cut down the cost as much as we can.

2. Also, I would like Qubole to release new features as often as possible. For example, integrating Jupyter: our data scientists are waiting for this.

What problems are you solving with the product? What benefits have you realized?