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Qubole VS StreamSets Data Collector

Compare Qubole VS StreamSets Data Collector and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Qubole logo Qubole

Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

StreamSets Data Collector logo StreamSets Data Collector

The StreamSets Data Collector (SDC) is used to build, test and execute dataflow pipelines for data lake and multi-cloud data movement plus cybersecurity, IoT and customer 360 applications.
  • Qubole Landing page
    Landing page //
    2023-06-22
  • StreamSets Data Collector Landing page
    Landing page //
    2023-10-20

Qubole features and specs

  • Scalability
    Qubole allows seamless scalability, adjusting resources automatically based on workload, which facilitates efficient handling of large data sets and peaks in demand.
  • Multi-cloud Support
    Qubole offers support for multiple cloud providers, including AWS, Azure, and Google Cloud, giving users flexibility and freedom to choose or shift between cloud services.
  • Unified Interface
    The platform provides a unified interface for diverse data processing engines such as Apache Spark, Hadoop, Presto, and Hive, simplifying the management of big data operations.
  • Cost Management
    Qubole includes features for cost management and optimization, such as intelligent spot instance usage, which can reduce operational costs significantly.
  • Data Security
    Qubole offers robust security features, including encryption, access controls, and compliance with various regulations, which assists in maintaining data privacy and protection.
  • Integration Capabilities
    The platform supports integration with many other tools and services, which enables a streamlined pipeline for data extraction, transformation, loading (ETL), and analysis.

Possible disadvantages of Qubole

  • Complex Setup
    For users unfamiliar with big data infrastructure and cloud platforms, the initial setup and configuration of Qubole may present a steep learning curve.
  • Cost Overruns
    Without careful management and monitoring, the automatic scaling and utilization of cloud resources can lead to unexpected and potentially high costs.
  • Dependency on Cloud Availability
    As a cloud-based platform, Qubole's performance and availability are contingent on the underlying cloud provider, which means service disruptions or performance issues in the cloud can affect Qubole’s operations.
  • Vendor Lock-in
    While Qubole supports multiple clouds, migrating away from the platform to another big data solution can be complex due to dependency on Qubole-specific configurations and optimizations.
  • Support and Documentation
    Some users have reported that the quality and depth of support and documentation provided by Qubole can vary, which may affect troubleshooting and learning.
  • User Interface
    While the interface is comprehensive, some users may find it less intuitive compared to other platforms, which can hinder ease of use and efficiency.

StreamSets Data Collector features and specs

No features have been listed yet.

Qubole videos

+ Add

Fast and Cost Effective Machine Learning Deployment with S3, Qubole, and Spark

More videos:

  • Review - Migrating Big Data to the Cloud: WANdisco, GigaOM and Qubole
  • Review - Democratizing Data with Qubole

StreamSets Data Collector videos

+ Add

Data Pipeline Preview with StreamSets Data Collector

Category Popularity

0-100% (relative to Qubole and StreamSets Data Collector)

Qubole

StreamSets Data Collector

Data Dashboard
100 100%
Data Dashboard
0% 0
Data Management
0 0%
Data Management
100% 100
Big Data
84 84%
Big Data
16% 16
Data Warehousing
100 100%
Data Warehousing
0% 0

User comments

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What are some alternatives?

When comparing Qubole and StreamSets Data Collector, you can also consider the following products

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

Snowflake - Snowflake is the only data platform built for the cloud for all your data & all your users. Learn more about our purpose-built SQL cloud data warehouse.

Spark Streaming - Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.

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