StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Altiscale vs Panoply

Altiscale vs Panoply

OverviewComparisonAlternatives

Overview

Altiscale
Altiscale
Stacks6
Followers10
Votes28
Panoply
Panoply
Stacks9
Followers17
Votes0

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.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Altiscale
Altiscale
Panoply
Panoply

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.

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.

Hadoop Dialtone;“Infinite” Hadoop;A Proactive Hadoop Helpdesk;Predictable, Hadoop-based pricing
Data warehouse; Business Intelligence;Optimized Query Engine
Statistics
Stacks
6
Stacks
9
Followers
10
Followers
17
Votes
28
Votes
0
Pros & Cons
Pros
  • 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
No community feedback yet
Integrations
No integrations available
HubSpot
HubSpot
MySQL
MySQL
Metabase
Metabase
Google Analytics
Google Analytics
Airbrake
Airbrake
Braintree
Braintree
Amazon S3
Amazon S3
QuickBooks
QuickBooks
Tableau
Tableau
PostgreSQL
PostgreSQL

What are some alternatives to Altiscale, Panoply?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Stitch

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Dremio

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

Cloudera Enterprise

Cloudera Enterprise

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

Airbyte

Airbyte

It is an open-source data integration platform that syncs data from applications, APIs & databases to data warehouses lakes & DBs.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase