Qubole is the open data lake company that provides a simple and secure data lake platform for machine learning, streaming, and ad-hoc analytics. No other platform provides the openness and data workload flexibility of Qubole while radically accelerating data lake adoption, reducing time to value, and lowering cloud data lake costs by 50 percent. Qubole’s Platform provides end-to-end data lake services such as cloud infrastructure management, data management, continuous data engineering, analytics, and machine learning with near-zero administration. Qubole is trusted by leading brands such as Expedia, Disney, Oracle, Gannett and Adobe to spur innovation and to transform their businesses for the era of big data. For more information, visit us at www.qubole.com.
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Qubole Reviews
Ferdinand P.
Advanced user of QuboleWhat do you like best?
It is very easy to use and provides many choices with limited effort to retrieve data. For the EBS volume and the number of nodes, automated cluster termination to prevent waste, easy to turn between different languages such as Hive, Spark, Presto etc, I like the scalability function of Qubole. Clusters can be used well, registry is very fine.
What do you dislike?
Perhaps a slightly confusing structuring of alternatives contributes to time usage. Wide scrolling times are needed for the notebook. Like its rival Databricks, the laptop should be easy to use. Logic is not successful bootstrap script. Much like the library Databricks. To begin the cluster, it takes more time. Do you need some other Lambda price substitute for this.
Recommendations to others considering the product:
Versatile and easy-to-use tool, use the trial time to buy that suits all your needs.
What problems are you solving with the product? What benefits have you realized?
I can collect data in a very relaxed way by means of Qubole. It helped us save a great deal of money. The time required for processing was quicker, and resources were decreased due to Qubole. The scheduler is quick to set up jobs and the internal tables are saved, which are very handy when a workflow fails. The use of Qubole makes exchanging resources simpler. Spark Cluster launch. Launch. Works are executed. Using the scientific notebook.
