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Ansible VS Apache Spark

Compare Ansible VS Apache Spark and see what are their differences

Unimus
Unimus is a Network Automation and Configuration management (NCM) solution designed for fast deployment network-wide and ease of use. Unimus does not require learning any abstraction or templating languages, and does not require any coding skills. featured
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Ansible logo Ansible

Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine

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.
  • Ansible Landing page
    Landing page //
    2023-02-05
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Ansible features and specs

  • Agentless
    Ansible is agentless, meaning it doesn't require any software to be installed on the remote nodes. This simplifies management and reduces overhead.
  • Ease of Use
    Ansible uses a simple, easy-to-read YAML syntax for its playbooks, reducing the learning curve and making it accessible to those without extensive programming experience.
  • Scalability
    Ansible is designed to handle large-scale deployments, making it suitable for managing numerous machines or services efficiently.
  • Extensive Modules
    Ansible has a rich library of modules that support a wide variety of system tasks, cloud providers, and application deployments, offering great versatility.
  • Strong Community
    There is a large and active Ansible community that contributes to its development and provides support, which can be valuable for troubleshooting and learning best practices.
  • Idempotency
    Tasks in Ansible are idempotent, meaning they can be run multiple times without changing the system beyond the intended final state, ensuring reliable deployments.

Possible disadvantages of Ansible

  • Performance Overhead
    Being agentless, Ansible relies on SSH for communication with nodes, which can add performance overhead, especially when managing a large number of hosts.
  • Limited Windows Support
    Ansible's core is primarily designed for Unix-like systems, and while there is support for Windows, it's not as robust or as seamless as it is for Unix/Linux systems.
  • Lack of Built-in Error Handling
    Ansible's error handling is somewhat rudimentary out-of-the-box. Complex error handling scenarios often require custom solutions, which can complicate playbooks.
  • Learning Curve for Complex Scenarios
    While simple tasks are easy to set up, more complex configurations can become challenging quickly and may require a deep understanding of Ansible's modules and templating.
  • Reliance on YAML
    The use of YAML, while human-readable, can be prone to syntax errors such as incorrect indentation, which can potentially lead to hard-to-track-down bugs.
  • Dependency on Python
    Ansible requires Python to be installed on managed nodes. This could be an issue in environments where it's not feasible or desired to have Python installed.

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.

Ansible videos

+ Add

What Is Ansible? | How Ansible Works? | Ansible Tutorial For Beginners | DevOps Tools | Simplilearn

More videos:

  • Review - Automation with Ansible Playbooks | Review on Ansible Architecture
  • Review - Book Review : Mastering Ansible (Jesse Keating) by Zareef Ahmed

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

Category Popularity

0-100% (relative to Ansible and Apache Spark)

Ansible

Apache Spark

DevOps Tools
100 100%
DevOps Tools
0% 0
Databases
0 0%
Databases
100% 100
Continuous Integration
100 100%
Continuous Integration
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using Ansible and Apache Spark. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Ansible and Apache Spark

Ansible Reviews

What Are The Best Alternatives To Ansible? | Attune, Jenkins &, etc.
To put it simply, Ansible automates a wide range of IT aspects that includes configuration management, application deployment, cloud provisioning, etc. Plus, while using Ansible, you can patch your application, automate deployments, and run compliances and governance on your application. You can easily manage it by using a web interface known as Ansible Tower. Furthermore,...
Source: www.apzomedia.com
Best 8 Ansible Alternatives & equivalent in 2022
Ansible is a simple IT automation tool that is easy to deploy. It connects to your nodes and pushes out small programs called “Ansible modules” to those nodes. Then it executes these models over SSH and removes them when finished. The library of modules will reside on any machine, therefore there is no requirement for any servers and databases.
Source: www.guru99.com
Top 5 Ansible Alternatives in 2022: Server Automation Solutions by Alexander Fashakin on the 19th Aug 2021 facebook Linked In Twitter
Your project connects to Ansible through nodes called Ansible Modules. You can use these modules to manage your project. As an agentless architecture, Ansible allows you to run modules on any system or server. It doesn’t require client/server software or an agent to be installed. With Ansible, you can use Python Paramiko modules or SSH protocols.
Source: www.servertribe.com
Ansible vs Chef: What’s the Difference?
For Ansible, Simplilearn presents the Ansible Foundation Training Course. Ansible 2.0, a simple, popular, agent-free tool in the automation domain, helps increase team productivity and improve business outcomes. Learn with
Source: www.simplilearn.com
Chef vs Puppet vs Ansible
Ansible supports considerable ease of learning for the management of configurations due to YAML as the foundation language. YAML (Yet Another Markup Language) is closely similar to English and is human-readable. The server can help in pushing configurations to all the nodes. The applications of Ansible are clearly suitable for real-time execution along with the facility of...
Source: www.whizlabs.com

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 should be more popular than Ansible. 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.

Ansible mentions (9)

  • Mentorship Group
    We are open to practice using any open-source project, however, we want to set a sharp focus on projects maintained by the Red Hat, and our own projects in the Caravana Cloud organization on github. If there is no reason to do differently, we'll build using technologies such as OpenShift, Quarkus, Ansible and related projects. - Source: dev.to / almost 2 years ago
  • Observability Mythbusters: Yes, Observability-Landscape-as-Code is a Thing
    *Codifying the deployment of the OTel Collector *(to Nomad, Kubernetes, or a VM) using tools such as Terraform, Pulumi, or Ansible. The Collector funnels your OTel data to your Observability back-end. ✅. - Source: dev.to / over 2 years ago
  • Maintenance mode - vmware.vmware_rest Ansible collection
    Most of what I've learnt today was purley from this blog and only because it's from ansible.com - dated now I guess ... Source: almost 3 years ago
  • Proactive Kubernetes Monitoring with Alerting
    I installed the helm release using Ansible, but you can install with the following helm commands:. - Source: dev.to / almost 3 years ago
  • Cannot run a playbook in crontab - Python error
    [root@ansible ~]# pip show ansible Name: ansible Version: 2.9.25 Summary: Radically simple IT automation Home-page: https://ansible.com/ Author: Ansible, Inc. Author-email: info@ansible.com License: GPLv3+ Location: /usr/lib/python2.7/site-packagesRequires: jinja2, PyYAML, cryptography Required-by:. Source: over 3 years ago
View more

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 / 21 days 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 / 23 days 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 / 2 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 / 2 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
View more

What are some alternatives?

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

Chef - Automation for all of your technology. Overcome the complexity and rapidly ship your infrastructure and apps anywhere with automation.

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

Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development

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

Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

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