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AWS CloudFormation VS Databricks

Compare AWS CloudFormation VS Databricks and see what are their differences

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AWS CloudFormation logo AWS CloudFormation

AWS CloudFormation gives developers and systems administrators an easy way to create and manage a...

Databricks logo Databricks

Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat is Apache Spark?
  • AWS CloudFormation Landing page
    Landing page //
    2023-03-22
  • Databricks Landing page
    Landing page //
    2023-09-14

AWS CloudFormation features and specs

  • Infrastructure as Code
    CloudFormation allows you to define your infrastructure using code or templates, promoting version control, reviewability, and collaborative planning.
  • Automated Provisioning
    It automates the provisioning and updating of infrastructure, reducing the manual intervention required and minimizing human errors.
  • Consistency and Repeatability
    Ensures consistent configurations by deploying the same template multiple times across different environments, eliminating configuration drift.
  • Integration with Other AWS Services
    Tightly integrated with other AWS services, allowing for comprehensive infrastructure management, security policies, monitoring and logging.
  • Scalability and Flexibility
    Facilitates easy scaling and modifying of resources according to the application requirements without significant downtime.

Possible disadvantages of AWS CloudFormation

  • Complexity
    Large templates can become complex and difficult to manage, making troubleshooting and updating challenging.
  • Learning Curve
    Requires time and effort to learn and master, especially for newcomers to AWS or Infrastructure as Code (IaC) concepts.
  • Limited Cross-Platform Support
    Primarily tailored for AWS services, with limited support for managing infrastructure on other cloud platforms.
  • State Management
    Managing the state of your infrastructure can be complex, as creating or updating resources is highly dependent on the current state of your stack.
  • Debugging Issues
    Error messages and stack traces can sometimes be cryptic, making it difficult to pinpoint the exact cause of deployment failures.

Databricks features and specs

  • Unified Data Analytics Platform
    Databricks integrates various data processing and analytics tools, offering a unified environment for data engineering, machine learning, and business analytics. This integration can streamline workflows and reduce the complexity of data management.
  • Scalability
    Databricks leverages Apache Spark and other scalable technologies to handle large datasets and high computational workloads efficiently. This makes it suitable for enterprises with significant data processing needs.
  • Collaborative Environment
    The platform offers collaborative notebooks that allow data scientists, engineers, and analysts to work together in real-time. This enhances productivity and fosters better communication within teams.
  • Performance Optimization
    Databricks includes various performance optimization features such as caching, indexing, and query optimization, which can significantly speed up data processing tasks.
  • Support for Various Data Formats
    The platform supports a wide range of data formats and sources, including structured, semi-structured, and unstructured data, making it versatile and adaptable to different use cases.
  • Integration with Cloud Providers
    Databricks is designed to work seamlessly with major cloud providers like AWS, Azure, and Google Cloud, allowing users to easily integrate it into their existing cloud infrastructure.

Possible disadvantages of Databricks

  • Cost
    Databricks can be expensive, especially for large-scale deployments or high-frequency usage. It may not be the most cost-effective solution for smaller organizations or projects with limited budgets.
  • Complexity
    While powerful, Databricks can be complex to set up and manage, requiring specialized knowledge in Apache Spark and cloud infrastructure. This might lead to a steeper learning curve for new users.
  • Dependency on Cloud Providers
    Being heavily integrated with cloud providers, Databricks might face issues like vendor lock-in, where switching providers becomes difficult or costly.
  • Limited Offline Capabilities
    Databricks is primarily designed for cloud environments, which means offline or on-premise capabilities are limited, posing challenges for organizations with strict data governance policies.
  • Resource Management
    Efficiently managing and allocating resources can be challenging in Databricks, especially in large multi-user environments. Mismanagement of resources could lead to increased costs and reduced performance.

Analysis of AWS CloudFormation

Overall verdict

  • Good

Why this product is good

  • AWS CloudFormation can be a powerful tool for managing infrastructure as code, allowing you to model and set up your Amazon Web Services resources so that you can spend less time managing those resources and more time focusing on your applications. It provides a consistent, repeatable process for provisioning infrastructure, improves change management, enhances resource tracking, and reduces the possibility of human errors. Additionally, it integrates seamlessly with other AWS services and enables the deployment of infrastructure through code, which can be version controlled, tested, and automated.

Recommended for

  • Organizations that heavily utilize AWS services and wish to manage resources through a codified approach
  • Software teams that implement CI/CD pipelines and require infrastructure code to be included in those pipelines
  • DevOps teams striving for automation, consistency, and scalability in their cloud infrastructure management
  • Developers and IT professionals who need to manage complex infrastructures or regularly spin up and tear down environments

AWS CloudFormation videos

What is AWS Cloudformation? Pros and Cons?

More videos:

  • Demo - AWS CloudFormation Tutorial | AWS CloudFormation Demo | AWS Tutorial | AWS Training | Edureka
  • Tutorial - AWS CloudFormation Template Tutorial

Databricks videos

Introduction to Databricks

More videos:

  • Tutorial - Azure Databricks Tutorial | Data transformations at scale
  • Review - Databricks - Data Movement and Query

Category Popularity

0-100% (relative to AWS CloudFormation and Databricks)
DevOps Tools
100 100%
0% 0
Data Dashboard
0 0%
100% 100
Continuous Integration
100 100%
0% 0
Big Data Analytics
0 0%
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 AWS CloudFormation and Databricks

AWS CloudFormation Reviews

5 Best DevSecOps Tools in 2023
There are multiple providers for Infrastructure as Code such as AWS CloudFormation, RedHat Ansible, HashiCorp Terraform, Puppet, Chef, and others. It is advised to research each to determine what is best for any given situation since each has pros and cons. Some of these also are not completely free while others are. There are also some that are specific to a particular...
Do not use AWS CloudFormation
CloudFormation being a layer of indirection makes it difficult to work with in multi-region/multi-account scenarios. With CloudFormation you have to create Stack Sets and IAM policies that allow the CloudFormation service to impersonate other roles. The prerequisite steps you have to take to use CloudFormation across multiple accounts also must be taken just to have...
Why we use Terraform and not Chef, Puppet, Ansible, SaltStack, or CloudFormation
Of course, there are downsides to declarative languages too. Without access to a full programming language, your expressive power is limited. For example, some types of infrastructure changes, such as a rolling, zero-downtime deployment, are hard to express in purely declarative terms. Similarly, without the ability to do โ€œlogicโ€ (e.g. if-statements, loops), creating...

Databricks Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Databricks notebooks are a popular tool for developing code and presenting findings in data science and machine learning. Databricks Notebooks support real-time multilingual coauthoring, automatic versioning, and built-in data visualizations.
Source: lakefs.io
7 best Colab alternatives in 2023
Databricks is a platform built around Apache Spark, an open-source, distributed computing system. The Databricks Community Edition offers a collaborative workspace where users can create Jupyter notebooks. Although it doesn't offer free GPU resources, it's an excellent tool for distributed data processing and big data analytics.
Source: deepnote.com
Top 5 Cloud Data Warehouses in 2023
Jan 11, 2023 The 5 best cloud data warehouse solutions in 2023Google BigQuerySource: https://cloud.google.com/bigqueryBest for:Top features:Pros:Cons:Pricing:SnowflakeBest for:Top features:Pros:Cons:Pricing:Amazon RedshiftSource: https://aws.amazon.com/redshift/Best for:Top features:Pros:Cons:Pricing:FireboltSource: https://www.firebolt.io/Best for:Top...
Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
Databricks is a simple, fast, and collaborative analytics platform based on Apache Spark with ETL capabilities. It accelerates innovation by bringing together data science and data science businesses. It is a fully managed open-source version of Apache Spark analytics with optimized connectors to storage platforms for the fastest data access.
Source: visual-flow.com
Top Big Data Tools For 2021
Now Azure Databricks achieves 50 times better performance thanks to a highly optimized version of Spark. Databricks also enables real-time co-authoring and automates versioning. Besides, it features runtimes optimized for machine learning that include many popular libraries, such as PyTorch, TensorFlow, Keras, etc.

Social recommendations and mentions

Based on our record, AWS CloudFormation should be more popular than Databricks. It has been mentiond 129 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.

AWS CloudFormation mentions (129)

  • Dynamic Looping Comes to AWS SAM
    AWS SAM CLI, the command-line tool for building and deploying serverless applications, now supports AWS CloudFormation Language Extensions. The one I am most excited about is Fn::ForEach, which brings dynamic looping to your YAML templates, but it's close. If you, like me, have been copy-pasting resource definitions to infinity, that stops today. - Source: dev.to / about 2 months ago
  • AWS CloudFormation Drift Detection & Remediation Guide
    AWS CloudFormation is an IaC service that helps users automate, scale, and manage their environments efficiently. On the other hand, GitOps has become one of the standard ways of ensuring the IaC configuration stored in code repositories is deployed live on the correct systems. - Source: dev.to / 6 months ago
  • Announcing AWS CDK Mixins: Composable Abstractions for AWS Resources
    The AWS Cloud Development Kit (CDK) is an open-source software development framework for defining cloud infrastructure in code and provisioning it through AWS CloudFormation. It contains pre-written modular and reusable cloud components known as constructs. Constructs are the basic building blocks representing one or more AWS CloudFormation resources and their configuration. - Source: dev.to / 8 months ago
  • Top 12 Puppet Alternatives for Automation
    Website: https://aws.amazon.com/cloudformation/. - Source: dev.to / 8 months ago
  • From Code to Cloud in Minutes: How AWS Amplify Supercharges Modern App Development
    When you deploy a cloud sandbox, Amplify creates an AWSโ€ฏCloudFormation stack following the naming convention of amplify--<$(whoami)>-sandbox in your AWS account with the resources configured in your amplify/ folder. - Source: dev.to / about 1 year ago
View more

Databricks mentions (18)

  • Platform Engineering Abstraction: How to Scale IaC for Enterprise
    Vendors like Confluent, Snowflake, Databricks, and dbt are improving the developer experience with more automation and integrations, but they often operate independently. This fragmentation makes standardizing multi-directional integrations across identity and access management, data governance, security, and cost control even more challenging. Developing a standardized, secure, and scalable solution for... - Source: dev.to / almost 2 years ago
  • dolly-v2-12b
    Dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAIโ€™s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA). Source: over 3 years ago
  • Clickstream data analysis with Databricks and Redpanda
    Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose. - Source: dev.to / almost 4 years ago
  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake. - Source: dev.to / about 4 years ago
  • A Quick Start to Databricks on AWS
    Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward. - Source: dev.to / about 4 years ago
View more

What are some alternatives?

When comparing AWS CloudFormation and Databricks, you can also consider the following products

AWS Lambda - Automatic, event-driven compute service

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Bamboo - Bamboo is a continuous integration and deployment tool that ties automated builds, tests and releases together in a single workflow.

Looker - Looker makes it easy for analysts to create and curate custom data experiencesโ€”so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.