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Heroku Enterprise VS Apache Spark

Compare Heroku Enterprise VS Apache Spark and see what are their differences

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Heroku Enterprise logo Heroku Enterprise

Heroku Enterprise is a flexible IT management for developers that lets them build apps using their preferred languages and tools like Ruby, Java, Python and Node.

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Heroku Enterprise Landing page
    Landing page //
    2023-01-23
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Heroku Enterprise features and specs

  • Scalability
    Heroku Enterprise offers robust tools for scaling applications easily. You can add more compute resources with just a few clicks, making it simpler to handle traffic spikes and growing user bases.
  • Ease of Use
    Heroku is known for its developer-friendly environment, which simplifies deployment and management of applications. The platform abstracts much of the underlying infrastructure complexity, allowing developers to focus more on coding.
  • Integration
    Heroku Enterprise integrates smoothly with other Salesforce services and third-party tools, providing versatility and extending the capabilities of your applications.
  • Security
    Heroku Enterprise offers enhanced security features such as private spaces, TLS encryption, and compliance with industry standards (e.g., HIPAA, PCI). It ensures that enterprise-level security requirements are met.
  • Support
    Heroku Enterprise clients receive premium support services, including 24/7 customer service, which ensures that any technical issues are resolved quickly and efficiently.

Possible disadvantages of Heroku Enterprise

  • Cost
    Heroku Enterprise can be quite expensive, especially for smaller companies or startups. The pricing structure might be prohibitive for some organizations.
  • Limited Control
    While the ease of use is a strong point, it also means less control over the underlying infrastructure. This can be a drawback for businesses with specific configurations or those requiring deep infrastructure customizations.
  • Performance
    Despite its strong scalability features, some users report that Heroku applications can experience latency issues under heavy load, which might affect performance.
  • Vendor Lock-in
    Relying heavily on Heroku Enterprise for application deployment could pose a risk of vendor lock-in, making it challenging to migrate to other platforms in the future.
  • Customization Limitations
    While Heroku offers numerous add-ons and integrations, it still has limitations in terms of customization compared to managing your own infrastructure, which could be a disadvantage for highly specialized applications.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Analysis of Heroku Enterprise

Overall verdict

  • Heroku Enterprise is a solid choice for organizations that need a reliable and developer-friendly platform to support their application development lifecycle. It balances simplicity with the advanced features required by enterprises, making it suitable for handling complex projects and larger teams.

Why this product is good

  • Heroku Enterprise is generally considered a good option for businesses looking for a robust platform-as-a-service (PaaS) to deploy, manage, and scale their applications. Key reasons include its ease of use, strong support for a wide range of programming languages, seamless integration with popular development tools, and a comprehensive set of features tailored for enterprise needs, such as enhanced security, compliance, and monitoring capabilities.

Recommended for

    Heroku Enterprise is recommended for mid-sized to large businesses, startups experiencing rapid growth, and teams that value streamlined deployment processes, scalability, and integration with other cloud services. It is particularly well-suited for developers who prefer a platform that abstracts much of the underlying infrastructure management, allowing them to focus on code and innovation.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Heroku Enterprise videos

No Heroku Enterprise videos yet. You could help us improve this page by suggesting one.

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Apache Spark videos

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Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Heroku Enterprise and Apache Spark)

Heroku Enterprise

Apache Spark

Monitoring Tools
100 100%
Monitoring Tools
0% 0
Databases
0 0%
Databases
100% 100
Backup & Restore
100 100%
Backup & Restore
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 Heroku Enterprise and Apache Spark

Heroku Enterprise Reviews

We have no reviews of Heroku Enterprise yet.
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Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark seems to be more popular. It has been mentiond 70 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.

Heroku Enterprise mentions (0)

We have not tracked any mentions of Heroku Enterprise yet. Tracking of Heroku Enterprise recommendations started around Mar 2021.

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 2 months ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / about 2 months 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
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 4 months ago
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What are some alternatives?

When comparing Heroku Enterprise and Apache Spark, you can also consider the following products

ManageEngine RecoveryManager Plus - RecoveryManager Plus is one such enterprise backup solution which has the ability to easily backup and restores both the domain controllers and virtual machines.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

SECDO - SECDO offers automated endpoint security and incident response solutions

Hadoop - Open-source software for reliable, scalable, distributed computing

Traverse Monitoring - Traverse Monitoring is an IT Management software that provides businesses with a network monitoring solution which is capable of handling the tasks of monitoring private clouds, distributed network infestation and virtualized infrastructure.

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

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