DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

Last call! Secure your stack and shape the future! Help dev teams across the globe navigate their software supply chain security challenges.

Modernize your data layer. Learn how to design cloud-native database architectures to meet the evolving demands of AI and GenAI workloads.

Releasing software shouldn't be stressful or risky. Learn how to leverage progressive delivery techniques to ensure safer deployments.

Avoid machine learning mistakes and boost model performance! Discover key ML patterns, anti-patterns, data strategies, and more.

Related

  • Spark Job Optimization
  • All You Need to Know About Apache Spark
  • Iceberg Catalogs: A Guide for Data Engineers
  • Data Processing With Python: Choosing Between MPI and Spark

Trending

  • The Cypress Edge: Next-Level Testing Strategies for React Developers
  • Unlocking Data with Language: Real-World Applications of Text-to-SQL Interfaces
  • Comprehensive Guide to Property-Based Testing in Go: Principles and Implementation
  • Blue Skies Ahead: An AI Case Study on LLM Use for a Graph Theory Related Application
  1. DZone
  2. Data Engineering
  3. Big Data
  4. Code Analyzer for Apache Spark

Code Analyzer for Apache Spark

The new Code Analyze for Apache Spark promotes DevOps for Big Data, helping to tear down the wall between developers and operations.

By 
Tom Smith user avatar
Tom Smith
DZone Core CORE ·
May. 23, 17 · Opinion
Likes (3)
Comment
Save
Tweet
Share
7.7K Views

Join the DZone community and get the full member experience.

Join For Free

It was great talking to Chad Carson, co-founder of Pepperdata, about their release of Code Analyzer as another step in their mission to promote DevOps for big data.

The macro trends that have been taking place in Big Data over the past year include:

  • Moved from experimentation to production.

  • Hadoop MapReduce dominates production; Apache Spark gaining fast.

  • Most companies are migrating to cloud or hybrid.

  • Production Big Data is adopting standard DevOps practices.

Movement from R to Spark is consistent with what I've heard in my most recent Big Data interviews. Based on Databricks' Apache Spark survey, people are moving to Spark for the following reasons:

  • Performance (91%).

  • Advanced analytics (82%).

  •  Ease of programming (76%).

  • Ease of deployment (69%).

  • Real-time streaming (51%).

Spark is easier for non-data engineers to use. Spark is not without problems, however. Customer pain points include:

  • Hidden execution details (hard to know why performance is slow).

  • Developers can’t connect code to hardware usage.

  • Achieving acceptable performance in production is complex (for example, the “cluster weather” problem).

  • Run times are inconsistent.

  • Hardware is underused with no vision to see how to improve.

Code Analyzer addresses these problems for:

  • Developers (data engineers), who make up 41% of users.

    • Precisely correlates resource utilization with application code.

    • Provides a contextual understanding of overall cluster resource consumption.

    • Gives users the ability to compare multiple runs of the same application.

  • Operators

    • Helps developers self-solve performance problems.

    • Identifies problem apps and sends a link to the developer with details.

This helps break down the wall between development and operations so they're working together to solve problems, improve code quality, and accelerate code delivery. 

Apache Spark Big data

Opinions expressed by DZone contributors are their own.

Related

  • Spark Job Optimization
  • All You Need to Know About Apache Spark
  • Iceberg Catalogs: A Guide for Data Engineers
  • Data Processing With Python: Choosing Between MPI and Spark

Partner Resources

×

Comments

The likes didn't load as expected. Please refresh the page and try again.

ABOUT US

  • About DZone
  • Support and feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends: