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Terraform VS Apache Spark

Compare Terraform VS Apache Spark and see what are their differences

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Terraform logo Terraform

Tool for building, changing, and versioning infrastructure safely and efficiently.

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.
  • Terraform Landing page
    Landing page //
    2023-09-24
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Terraform features and specs

  • Infrastructure as Code
    Terraform allows you to define your infrastructure in configuration files that can be versioned and stored in a version control system. This makes it easy to track changes, roll back if necessary, and collaborate with team members.
  • Multi-Cloud Support
    Terraform supports various cloud providers such as AWS, Azure, Google Cloud, and others. This allows you to manage your entire infrastructure using a single tool, regardless of the underlying provider.
  • Immutability
    Terraform promotes immutable infrastructure, meaning once a component is created, it is not modified in place but replaced if changes are needed. This leads to more predictable and stable deployments.
  • State Management
    Terraform maintains the state of your infrastructure, which helps in tracking resource changes over time and making incremental updates. This is crucial for applying changes in a controlled manner.
  • Community and Ecosystem
    Terraform has a large and active community, along with a rich ecosystem of providers and modules. This makes it easier to find support, share solutions, and leverage pre-built components.

Possible disadvantages of Terraform

  • Complex State Management
    While state management is a significant feature, managing state files can become complex and risky. Issues like state file corruption or sharing between team members can lead to challenges.
  • Learning Curve
    Terraform has a steep learning curve for beginners, especially those who are not familiar with infrastructure as code concepts or the HashiCorp Configuration Language (HCL).
  • Partial Updates
    Terraform's plan and apply operations are not atomic, meaning that partial updates can sometimes leave your infrastructure in an inconsistent state if an error occurs during execution.
  • Dependency Management
    Managing dependencies between resources can be challenging in Terraform. Misconfigured dependencies can lead to issues during resource creation, deletion, or updates.
  • Cost Management
    While Terraform is excellent for provisioning resources, it does not have built-in cost management or optimization features. Users need to rely on third-party tools to manage and optimize costs.

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.

Terraform videos

+ Add

Wampler Terraform | Reverb Tone Report Demo

More videos:

  • Review - MOD PEDAL POWERHOUSE! Wampler TERRAFORM
  • Demo - IT'S FINALLY HERE! | Wampler Terraform Demo | It's as good as you hoped!!!

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 Terraform and Apache Spark)

Terraform

Apache Spark

DevOps Tools
100 100%
DevOps Tools
0% 0
Databases
0 0%
Databases
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using Terraform 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 Terraform and Apache Spark

Terraform Reviews

Do not use AWS CloudFormation
Terraform, on the other hand, will occupy your shell until the directly-involved AWS service coughs up an error. No additional tooling is required. Terraform will just relay the error message from the affected service indicating what you’ve done wrong.
Source: gswallow.medium.com
Top 5 Ansible Alternatives in 2022: Server Automation Solutions by Alexander Fashakin on the 19th Aug 2021 facebook Linked In Twitter
Although Terraform and Ansible are both server automation tools, there are still a few significant differences between the two. For example, Terraform is declarative while Ansible allows for both procedural configurations and declarative configurations. Also, Ansible works best as a configuration management tool while Terraform leans towards cloud orchestration.
Source: www.servertribe.com
35+ Of The Best CI/CD Tools: Organized By Category
Terraform is compatible with a wide range of Cloud providers, including Azure, VMWare, and AWS. If you’re subscribed to multiple cloud providers, Terraform is a great way to ensure that they have consistent configurations.
Source: www.cloudzero.com
Why we use Terraform and not Chef, Puppet, Ansible, SaltStack, or CloudFormation
Example: Terraform and Ansible. You use Terraform to deploy all the underlying infrastructure, including the network topology (i.e., VPCs, subnets, route tables), data stores (e.g., MySQL, Redis), load balancers, and servers. You then use Ansible to deploy your apps on top of those servers.This is an easy approach to start with, as there is no extra infrastructure to run...
Source: blog.gruntwork.io
Ansible overtakes Chef and Puppet as the top cloud configuration management tool
Breaking these results down year-over-year, use of Ansible grew from 36% in 2018 to 41% in 2019--surpassing Chef, which grew from 36% to 37%, as well as Puppet, which grew from 34% to 37%. Rounding out the list is Terraform, which experienced a jump from 20% to 31%, and Salt, which increased in usage from 13% to 18%.
Source: www.techrepublic.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 Terraform. 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.

Terraform mentions (32)

  • Scaffolding Serverless Web Application on AWS
    Terraform is an infrastructure as code tool that lets you build, change, and version infrastructure safely and efficiently. Terraform code is in the terraform directory. - Source: dev.to / 10 months ago
  • Integrating Terraform with CI/CD Pipelines
    In recent years, there has been a significant shift towards automation of infrastructure deployment processes. One popular tool that has emerged as a key player in this space is Terraform, an open-source infrastructure as code (IaC) software tool developed by HashiCorp. This article will explore how Terraform can be integrated into continuous integration and delivery (CI/CD) pipelines using GitHub Actions as an... - Source: dev.to / about 1 year ago
  • Deploying Your Outdoor Activities Map with Terraform
    Terraform is an open-source infrastructure-as-code software tool created by HashiCorp. It allows you to define and manage your infrastructure as code, making it easy to provision and manage resources across multiple cloud providers. With Terraform, you can ensure consistent and repeatable deployments, making it an ideal choice for automating your cloud infrastructure. - Source: dev.to / over 1 year ago
  • Trigger CI using Terraform Cloud
    Continuous Integration(CI) pipelines needs a target infrastructure to which the CI artifacts are deployed. The deployments are handled by CI or we can leverage Continuous Deployment pipelines. Modern day architecture uses automation tools like terraform, ansible to provision the target infrastructure, this type of provisioning is called IaaC. - Source: dev.to / about 2 years ago
  • Using Let's Encrypt with the Puppet Enterprise console
    Had an itch I've been meaning to scratch for a while. I build my Puppet environment using Terraform, which makes it nice and easy to tear things down and rebuild them. That is great, but it does leave me with an issue when it comes to the console SSL certificates. - Source: dev.to / about 2 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 / 19 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 / 21 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 Terraform and Apache Spark, you can also consider the following products

Rancher - Open Source Platform for Running a Private Container Service

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

Puppet Enterprise - Get started with Puppet Enterprise, or upgrade or expand.

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

Packer - Packer is an open-source software for creating identical machine images from a single source configuration.

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

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