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Altiscale

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Panoply

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Altiscale vs Panoply: What are the differences?

Altiscale vs. Panoply

Introduction: When comparing Altiscale and Panoply, it's crucial to understand their key differences to make an informed decision for data management and analytics.

1. **Data Storage**: Altiscale is a cloud-based data platform that offers Apache Hadoop as a service, providing scalable storage solutions for big data. In contrast, Panoply is a smart data warehouse that automates data integration and storage, allowing for easy access to insights without the complexities of managing infrastructure.

2. **Data Processing**: Altiscale focuses on distributed data processing using Hadoop clusters, enabling parallel processing of large datasets. On the other hand, Panoply uses an in-memory processing engine to speed up queries and analyses, optimizing performance for quicker insights.

3. **Data Integration**: Altiscale allows seamless integration with various data sources through Hadoop connectors and APIs, facilitating a holistic view of data across platforms. Meanwhile, Panoply's data integration capabilities involve automatic data ingestion and transformation, reducing the time and effort needed for data preparation.

4. **Cost Structure**: Altiscale follows a pay-as-you-go pricing model based on usage and storage, offering flexibility for organizations with fluctuating data needs. Panoply, on the other hand, offers transparent pricing based on the data volume and users, ensuring predictable costs without hidden fees.

5. **Ease of Use**: Altiscale provides a more technical interface suitable for data engineers and developers to customize data pipelines and workflows according to specific requirements. In contrast, Panoply offers a user-friendly interface with drag-and-drop features, making it accessible for business users without technical expertise.

6. **Scalability**: Altiscale can scale vertically by adding more resources to existing nodes or horizontally by adding more nodes to the cluster, adapting to growing data demands efficiently. Panoply's platform automatically scales resources based on the workload, ensuring optimal performance and resource utilization without manual intervention.

In Summary, understanding the differences between Altiscale and Panoply is essential for determining the best data management solution tailored to specific business needs.
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Pros of Altiscale
Pros of Panoply
  • 3
    An ops team comes with it, so you're free to analyze
  • 3
    SOC2, PCI, HIPAA, Kerberos
  • 3
    The easiest, lowest cost, highest performing option
  • 3
    Hadoop ops experts run it all for you, don't need ops
  • 3
    Our data sci & analysts would scream if went back toEMR
  • 3
    Automated compute bursting
  • 3
    Super elastic, scalable, secure
  • 2
    Optimized cloud gives faster results, better completion
  • 2
    Simple, easy to use UI - Data Navigator is great
  • 2
    Easier pricing. No node worries. No surprise bills
  • 1
    Real domain experts running hadoop@scale, great service
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    What is Altiscale?

    we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

    What is Panoply?

    It is the data warehouse built for analysts. Our data management platform automates all three key aspects of the data stack: data collection, management, and query optimization.

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    What companies use Altiscale?
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    What tools integrate with Altiscale?
    What tools integrate with Panoply?
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      What are some alternatives to Altiscale and Panoply?
      MySQL
      The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
      PostgreSQL
      PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
      MongoDB
      MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
      Redis
      Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
      Amazon S3
      Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
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