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Apache Spark VS Helm.sh

Compare Apache Spark VS Helm.sh and see what are their differences

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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.

Helm.sh logo Helm.sh

The Kubernetes Package Manager
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Helm.sh Landing page
    Landing page //
    2021-07-30

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.

Helm.sh features and specs

  • Ease of Use
    Helm simplifies the deployment and management of Kubernetes applications by providing a package manager format that is easy to understand and use. It abstracts complex Kubernetes configurations into simple YAML files called Charts.
  • Reusable Configurations
    Helm Charts allow for reusable Kubernetes configurations, making it easier to maintain and share best-practice templates across different environments and teams.
  • Versioning
    Helm supports versioning of Helm Charts, enabling rollbacks to previous application states, which is critical for managing updates and rollbacks in production environments.
  • Extensibility
    Helm is highly extensible with Plugins and the ability to use community-contributed Charts. This extensibility facilitates customizations and leveraging the community for improved and varied functionality.
  • Templating Engine
    Helm Charts support Go templating, which allows for dynamic configuration values, making Helm Charts more flexible and powerful.
  • Broad Adoption
    Helm is widely adopted in the Kubernetes ecosystem, leading to a vast repository of pre-built Charts, extensive documentation, and strong community support.

Possible disadvantages of Helm.sh

  • Complexity
    While Helm simplifies many tasks, the templating language and Chart configurations can become complex and hard to manage, especially for large-scale applications.
  • Learning Curve
    New users of Helm may face a steep learning curve, particularly those who are not already familiar with Kubernetes concepts or YAML configuration syntax.
  • Security
    Helm's default Tiller component (used in Helm v2) had security concerns related to role-based access control (RBAC). While Helm v3 removed Tiller, previous versions may still be in use, leading to potential security risks.
  • Debugging
    Debugging issues with Helm Charts can be challenging, especially due to the abstraction and layering between the Helm template engine and the actual Kubernetes resources deployed.
  • Resource Abstraction
    Helm can sometimes abstract away too much of the Kubernetes internals, which might hinder advanced users who need fine-grained control over their deployments.
  • Dependency Management
    Managing dependencies between different Helm Charts can become cumbersome and lead to complex dependency trees that are hard to manage and debug.

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.

Analysis of Helm.sh

Overall verdict

  • Yes, Helm is considered a good tool for managing Kubernetes applications due to its ability to streamline deployment processes, provide version control and rollback configurations, and enable easier management of complex application dependencies and configurations. It is widely adopted in the Kubernetes ecosystem and backed by a strong open-source community, which continuously contributes improvements and enhancements.

Why this product is good

  • Helm (helm.sh) is a popular package manager for Kubernetes applications that simplifies the deployment and management of applications on Kubernetes clusters. It provides users with a convenient way to package, configure, and deploy applications and dependencies, utilizing a system of charts for managing complex application architectures. This capability reduces the complexity and effort needed to maintain and update Kubernetes applications, contributing to more efficient and error-free deployments.

Recommended for

  • DevOps teams managing Kubernetes applications
  • Software engineers looking for simplified Kubernetes deployments
  • Organizations seeking more efficient CI/CD pipelines with Kubernetes
  • Teams managing complex multi-service applications with numerous dependencies
  • Kubernetes beginners who need a powerful yet accessible tool to manage deployments.

Apache Spark videos

+ Add

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

Helm.sh videos

+ Add

Review: Helm's Zind Is My Favorite Black Boot (Discount Available)

More videos:

  • Review - Helm Free VST/AU Synth Review
  • Review - Another Khracker From Helm - Khuraburi Review

Category Popularity

0-100% (relative to Apache Spark and Helm.sh)

Apache Spark

Helm.sh

Databases
100 100%
Databases
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Big Data
100 100%
Big Data
0% 0
DevOps Tools
0 0%
DevOps Tools
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 Apache Spark and Helm.sh

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

Helm.sh Reviews

We have no reviews of Helm.sh yet.
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Social recommendations and mentions

Based on our record, Helm.sh should be more popular than Apache Spark. It has been mentiond 170 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.

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 1 month 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 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
  • 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 / 3 months ago
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Helm.sh mentions (170)

  • Install Red Hat Developer Hub with AI Software Templates on OpenShift
    Helm installed: brew install helm or from https://helm.sh. - Source: dev.to / about 1 month ago
  • Even more OpenTelemetry - Kubernetes special
    Docker Compose is great for demos: docker compose up, and you're good to go, but I know no organization that uses it in production. Deploying workloads to Kubernetes is much more involved than that. I've used Kubernetes for demos in the past; typing kubectl apply -f is dull fast. In addition to GitOps, which isn't feasible for demos, the two main competitors are Helm and Kustomize. I chose the former for its... - Source: dev.to / about 2 months ago
  • Kubernetes and Container Portability: Navigating Multi-Cloud Flexibility
    Helm Charts – An open-source solution for software deployment on top of Kubernetes. - Source: dev.to / about 2 months ago
  • Chart an Extensible Course with Helm
    Clicks, copies, and pasting. That's an approach to deploying your applications in Kubernetes. Anyone who's worked with Kubernetes for more than 5 minutes knows that this is not a recipe for repeatability and confidence in your setup. Good news is, you've got options when tackling this problem. The option I'm going to present below is using Helm. - Source: dev.to / 2 months ago
  • IKO - Lessons Learned (Part 1 - Helm)
    Looks like we're good to go (assuming you already have helm installed, if not install it first)! Let's install the IKO. We are going to need to tell helm where the folder with all our goodies is (that's the iris-operator folder you see above). If we were to be sitting at the chart directory you can use the command. - Source: dev.to / 3 months ago
View more

What are some alternatives?

When comparing Apache Spark and Helm.sh, you can also consider the following products

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

Kubernetes - Kubernetes is an open source orchestration system for Docker containers

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

Rancher - Open Source Platform for Running a Private Container Service

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

Docker Compose - Define and run multi-container applications with Docker

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