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Panoply VS Hadoop

Compare Panoply VS Hadoop and see what are their differences

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Panoply logo Panoply

Panoply is a smart cloud data warehouse

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing
  • Panoply Landing page
    Landing page //
    2023-09-27

Panoply is a smart data warehouse that automates all three key aspects of the data analytics stack: data collection & transformation (ETL), database storage management, and query performance optimization. Panoply empowers anyone working with data analytics to quickly gain actionable insights on their own - without the need of IT and Engineering.

  • Hadoop Landing page
    Landing page //
    2021-09-17

Panoply features and specs

  • Ease of Use
    Panoply is user-friendly and allows for easy data integration without requiring extensive technical knowledge. Its intuitive interface simplifies the data management process for users.
  • Quick Setup
    Setting up Panoply is relatively quick and does not require substantial infrastructure. This allows businesses to get started with their data operations promptly.
  • Scalability
    Panoply provides scalability options for growing businesses, allowing them to efficiently manage increasing volumes of data without significant performance degradation.
  • Integrations
    Panoply offers a wide range of integrations with various data sources, including popular tools and platforms like AWS, Google Analytics, and Salesforce, making it versatile for different business needs.
  • Automated Data Management
    Panoply automates data ingestion, storage, and management tasks, reducing the manual effort required and ensuring up-to-date data availability.

Possible disadvantages of Panoply

  • Cost
    Panoply can be relatively expensive, particularly for small businesses or startups with limited budgets. The pricing model may seem high compared to other solutions in the market.
  • Limited Advanced Analytics
    While Panoply is excellent for data integration and management, it might fall short in providing advanced analytics and machine learning capabilities, requiring users to employ other tools for complex analytics.
  • Learning Curve for Complex Setups
    Despite its general ease of use, setting up more complex data workflows in Panoply can still require a learning curve, especially for users unfamiliar with data warehousing concepts.
  • Support Response Time
    Some users have reported that the response time for customer support could be slow, leading to delays in resolving issues or getting assistance.
  • Customization Constraints
    Panoply may have limitations when it comes to highly customized data workflows or unique integration needs, potentially necessitating additional tools or workarounds for specific requirements.

Hadoop features and specs

  • Scalability
    Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.
  • Cost-Effective
    It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.
  • Fault Tolerance
    Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.
  • Flexibility
    It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.
  • Parallel Processing
    Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.
  • Community Support
    As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Possible disadvantages of Hadoop

  • Complexity
    Setting up, maintaining, and tuning a Hadoop cluster can be complex and often requires specialized knowledge.
  • Overhead
    The MapReduce model can introduce additional overhead, particularly for tasks that require low-latency processing.
  • Security
    While improvements have been made, Hadoop's security model is considered less mature compared to some other data processing systems.
  • Hardware Requirements
    Though it can run on commodity hardware, Hadoop can still require significant computational and storage resources for larger datasets.
  • Lack of Real-Time Processing
    Hadoop is mainly designed for batch processing and is not well-suited for real-time data analytics, which can be a limitation for certain applications.
  • Data Integrity
    Distributed systems face challenges in maintaining data integrity and consistency, and Hadoop is no exception.

Analysis of Panoply

Overall verdict

  • Overall, Panoply is a strong choice for businesses that need a user-friendly and efficient data management platform. It excels in simplifying complex data processes and offers scalability to accommodate growing data needs. Users appreciate its ease of use and the ability to quickly gain insights, although it may not have as extensive customization options as some larger, more technical platforms.

Why this product is good

  • Panoply (panoply.io) automates much of the data ingestion, transformation, and management processes, making it ideal for users who want to streamline their data workflow without needing extensive technical expertise. It supports a wide range of data sources and provides robust tools for data analysis, which can save time and resources for teams focusing on immediate insights and decision making. Its cloud-based infrastructure also allows for scalability and flexibility suitable for growing businesses.

Recommended for

    Panoply is recommended for small to medium-sized businesses, teams lacking a dedicated data engineering team, and organizations looking for an easy-to-use solution to integrate multiple data sources for analytics. It is also well-suited for analysts and decision-makers who value speed and simplicity in data processing and insights generation.

Analysis of Hadoop

Overall verdict

  • Hadoop is a robust and powerful data processing platform that is well-suited for organizations that need to manage and analyze large-scale data. Its resilience, scalability, and open-source nature make it a popular choice for big data solutions. However, it may not be the best fit for all use cases, especially those requiring real-time processing or where ease of use is a priority.

Why this product is good

  • Hadoop is renowned for its ability to store and process large datasets using a distributed computing model. It is scalable, cost-effective, and efficient in handling massive volumes of data across clusters of computers. Its ecosystem includes a wide range of tools and technologies like HDFS, MapReduce, YARN, and Hive that enhance data processing and analysis capabilities.

Recommended for

  • Organizations dealing with vast amounts of data needing efficient batch processing.
  • Businesses that require scalable storage solutions to manage their data growth.
  • Companies interested in leveraging a diverse ecosystem of data processing tools and technologies.
  • Technical teams that have the expertise to manage and optimize complex distributed systems.

Panoply videos

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Panoply demo: Get faster data analytics in minutes!

Hadoop videos

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What is Big Data and Hadoop?

More videos:

  • Review - Product Ratings on Customer Reviews Using HADOOP.
  • Tutorial - Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank Kane

Category Popularity

0-100% (relative to Panoply and Hadoop)

Panoply

Hadoop

Data Management
100 100%
Data Management
0% 0
Databases
0 0%
Databases
100% 100
Data Integration
100 100%
Data Integration
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Panoply and Hadoop

Panoply Reviews

Top 14 ETL Tools for 2023
Panoply is an automated, self-service cloud data warehouse that aims to simplify the data integration process. Any data connector with a standard ODBC/JDBC connection, Postgres connection, or AWS Redshift connection is compatible with Panoply. In addition, users can connect Panoply with other ETL tools, such as Stitch and Fivetran, to further augment their data integration...
Source: www.integrate.io
Top 5 BigQuery Alternatives: A Challenge of Complexity
Although Panoply was developed for data analysts, you don't have to be one to use it. Anyone with a good understanding of SQL can get a data pipeline up and running within a matter of minutes. This frees up your time to focus on analysis, whether you’re running queries directly in Panoply or in your favorite BI tool.
Source: blog.panoply.io
Top ETL Tools For 2021...And The Case For Saying "No" To ETL
Under the hood, Panoply uses a flexible ELT approach (rather than traditional ETL), which makes data ingestion much faster and more dynamic, since you don’t have to wait for transformation to complete before loading your data. And since Panoply builds managed cloud data warehouses for every user, you won’t need to set up a separate destination to store all the data you pull...
Source: blog.panoply.io
Top 7 ETL Tools for 2021
Panoply is an automated, self-service cloud data warehouse that aims to simplify the data integration process. Any data connector with a standard ODBC/JDBC connection, Postgres connection, or AWS Redshift connection is compatible with Panoply. In addition, users can connect Panoply with other ETL tools such as Stitch and Fivetran to further augment their data integration...
Source: www.xplenty.com

Hadoop Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
Companies considering Hadoop should be aware of its costs. A significant portion of the cost of implementing Hadoop comes from the computing power required for processing and the expertise needed to maintain Hadoop ETL, rather than the tools or storage themselves.
Source: www.datacamp.com
16 Top Big Data Analytics Tools You Should Know About
Hadoop is an Apache open-source framework. Written in Java, Hadoop is an ecosystem of components that are primarily used to store, process, and analyze big data. The USP of Hadoop is it enables multiple types of analytic workloads to run on the same data, at the same time, and on a massive scale on industry-standard hardware.
Source: www.analytixlabs.co.in
5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Hadoop should be more popular than Panoply. It has been mentiond 25 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Panoply mentions (3)

Hadoop mentions (25)

  • Apache Hadoop: Open Source Business Model, Funding, and Community
    This post provides an in‐depth look at Apache Hadoop, a transformative distributed computing framework built on an open source business model. We explore its history, innovative open funding strategies, the influence of the Apache License 2.0, and the vibrant community that drives its continuous evolution. Additionally, we examine practical use cases, upcoming challenges in scaling big data processing, and future... - Source: dev.to / 25 days ago
  • What is Apache Kafka? The Open Source Business Model, Funding, and Community
    Modular Integration: Thanks to its modular approach, Kafka integrates seamlessly with other systems including container orchestration platforms like Kubernetes and third-party tools such as Apache Hadoop. - Source: dev.to / 25 days ago
  • India Open Source Development: Harnessing Collaborative Innovation for Global Impact
    Over the years, Indian developers have played increasingly vital roles in many international projects. From contributions to frameworks such as Kubernetes and Apache Hadoop to the emergence of homegrown platforms like OpenStack India, India has steadily carved out a global reputation as a powerhouse of open source talent. - Source: dev.to / about 1 month ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 3 months ago
  • Apache Hadoop: Pioneering Open Source Innovation in Big Data
    Apache Hadoop is more than just software—it’s a full-fledged ecosystem built on the principles of open collaboration and decentralized governance. Born out of a need to process vast amounts of information efficiently, Hadoop uses a distributed file system and the MapReduce programming model to enable scalable, fault-tolerant computing. Central to its success is a diverse ecosystem that includes influential... - Source: dev.to / 3 months ago
View more

What are some alternatives?

When comparing Panoply and Hadoop, you can also consider the following products

QuickBI - Export data from over 300 sources to a data warehouse and analyze it with a reporting tool of your choice. Quick and easy setup.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Supermetrics - Supermetrics simplifies marketing analytics by connecting, consolidating, and centralizing data from 150+ platforms into your favorite tools. Trusted by 200K+ organizations, we empower marketers to focus on insights, not manual work.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

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

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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