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Performance & Data Eng

Performance & Data Engineering Resources

Best practices, happenings, expert tips & techniques, and more from DataOps industry experts. Browse the available content below or search the library of resources.

Performance & Data Engineering Resources

Data Observability: The Missing Link for Data Teams

Download The Unravel Guide to DataOps

How to intelligently monitor Kafka/Spark Streaming data pipelines

Why Memory Management is Causing Your Spark Apps To Be Slow or Fail

Configuration Management in Modern Data Platforms

How Shopify Fixed a $1 Million Single Query

Overcoming Friction & Harnessing the Power of Unravel: Try It for Free

Blind Spots in Your System: The Grave Risks of Overlooking Observability

Unleashing the Power of Data: How Data Engineers Can Harness AI/ML to Achieve Essential Data Quality

Healthcare leader uses AI insights to boost data pipeline efficiency

Unravel Data Recognized by SIIA as Best Data Tool & Platform at 2023 CODiE Awards

Data Observability, The Reality eBook

Three Companies Driving Better Business Outcomes from Data Analytics

3 Takeaways from the 2023 Data Teams Summit

Sneak Peek into Data Teams Summit 2023 Agenda

Get Ready for the Next Generation of DataOps Observability

Unravel Goes on the Road at These Upcoming Events

Tips to optimize Spark jobs to improve performance

Kafka best practices: Monitoring and optimizing the performance of Kafka applications

Why Legacy Observability Tools Don’t Work for Modern Data Stacks

Building vs. Buying Your Modern Data Stack: A Panel Discussion

Data Pipeline HealthCheck and Analysis

Accelerate Amazon EMR for Spark & More

Strategies To Accelerate Performance for Databricks

Effective Cost and Performance Management for Amazon EMR

How To Get the Best Performance & Reliability Out of Kafka & Spark Applications

Standardizing Business Metrics & Democratizing Experimentation at Intuit

“More than 60% of Our Pipelines Have SLAs,” Say Unravel Customers at Untold

Spark APM – What is Spark Application Performance Management

Spark Catalyst Pipeline: A Deep Dive into Spark’s Optimizer

End-to-end Monitoring of HBase Databases and Clusters

Meeting SLAs for Data Pipelines on Amazon EMR With Unravel

Case Study: Meeting SLAs for Data Pipelines on Amazon EMR

Using Machine Learning to understand Kafka runtime behavior

Using Unravel to tune Spark data skew and partitioning

Unravel Architect Solution Guide APM For Big Data Apps And Platforms

Unravel APM Solution Guide

Maximize Big Data Application Performance and ROI

Monitor and optimize data pipelines and workflows end-to-end

Intelligent Application Performance Management (APM) Platform for the Modern Data Stack

Skills Developers Need To Optimize Performance

Additional Considerations Regarding Performance And Monitoring

Opportunities to Improve Performance and Monitoring

Common Issues With Performance and Monitoring

Real-World Problems Solved By Performance Monitoring and Optimization

Most Frequently Used Performance Tools

2017 Application Performance Management Predictions – Part 4

2017 Application Performance Management Predictions – Part 2

Organizations Turn Focus to Data Reliability, Security and Governance

Is The Promise of Big Data Hampered by Skills Shortages and Poor Performance?

Architecting an Automated Performance Management (APM) Solution for Modern Data Applications — Part 1

Architecting APM Solution for Modern Data Applications — Part 2

Why is APM a Must-Have for Big Data Self-Service Analytics?

Fighting for precious resources on a multi-tenant big data cluster

How to resolve performance issues of big data applications

Organizations Need APM to fully Harness Big Data

Common Modern Data Stack Application and Operation Challenges

To Cache or Not to Cache RDDs in Spark

Auto-Tuning Tez Applications

The Operations Guide To Big Data Application Performance