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Salesforce Platform VS Hadoop

Compare Salesforce Platform VS Hadoop and see what are their differences

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

Salesforce Platform logo Salesforce Platform

Salesforce Platform is a comprehensive PaaS solution that paves the way for the developers to test, build, and mitigate the issues in the cloud application before the final deployment.

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing
  • Salesforce Platform Landing page
    Landing page //
    2023-06-05
  • Hadoop Landing page
    Landing page //
    2021-09-17

Salesforce Platform features and specs

  • Customization
    Salesforce Platform offers extensive customization options that allow businesses to tailor the platform to suit their specific needs. From custom objects and fields to custom workflows and processes, users have a high level of control over their environment.
  • Integration
    The platform supports integration with a wide range of third-party applications and services through APIs. This flexibility ensures that businesses can create a seamless workflow across different software systems.
  • Scalability
    Salesforce Platform is highly scalable, making it suitable for businesses of all sizes. As a cloud-based solution, it can easily handle growth in terms of users, data volume, and functionality without significant downtime or degradation in performance.
  • Mobile Accessibility
    With Salesforce Mobile App, users have access to their data and applications from anywhere, enhancing productivity and ensuring that critical tasks can be completed while on the go.
  • Security
    Salesforce Platform offers robust security features, including data encryption, regular security updates, and compliance with various industry standards and regulations, providing peace of mind for businesses concerned about data protection.
  • Community and Support
    Salesforce has a vast community of users, developers, and experts, along with extensive documentation and support resources. This community can be invaluable for troubleshooting, best practices, and ongoing learning.

Possible disadvantages of Salesforce Platform

  • Cost
    Salesforce Platform can be expensive, particularly for small and medium-sized businesses. The costs can quickly add up with additional features, customizations, and third-party integrations.
  • Complexity
    While the customization options are a significant benefit, they can also add complexity, especially for users without technical expertise. This can lead to a steep learning curve and may require additional training or hiring specialized personnel.
  • Performance
    While generally reliable, the platform can experience performance issues, particularly during peak usage times or with complex customizations. This can potentially affect the efficiency and response times for users.
  • Dependency on Internet
    As a cloud-based solution, Salesforce Platform requires a stable internet connection to be fully functional. This dependency can be a drawback in areas with unreliable internet service.
  • Customization Limits
    Despite its flexibility, there are still limits to what can be customized within Salesforce. In some cases, achieving certain functionalities may require complex workarounds or may not be possible at all within the provided framework.
  • Data Migration
    Migrating data to and from Salesforce can be challenging, particularly for large datasets or complex data structures. This process often requires careful planning and execution to avoid data loss or integrity issues.

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 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.

Salesforce Platform videos

+ Add

Salesforce Platform Overview (1)

Hadoop videos

+ Add

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 Salesforce Platform and Hadoop)

Salesforce Platform

Hadoop

Cloud Computing
100 100%
Cloud Computing
0% 0
Databases
0 0%
Databases
100% 100
Cloud Hosting
100 100%
Cloud Hosting
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 Salesforce Platform and Hadoop

Salesforce Platform Reviews

3 easy app makers you can start on today
Salesforce Platform: If you use the popular customer relationship management system, Salesforce’s low-code tools allow you to build custom apps that can include AI and connect with the company’s various cloud services.
Source: www.intheblack.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 seems to be more popular. 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.

Salesforce Platform mentions (0)

We have not tracked any mentions of Salesforce Platform yet. Tracking of Salesforce Platform recommendations started around Sep 2021.

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 / 26 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 / 27 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
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What are some alternatives?

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

Google App Engine - A powerful platform to build web and mobile apps that scale automatically.

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

Dokku - Docker powered mini-Heroku in around 100 lines of Bash

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

Google Cloud Functions - A serverless platform for building event-based microservices.

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

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