New Relic

In the increasingly complex world of modern data infrastructure, visibility isn’t just a nice-to-have—it’s a necessity. As data pipelines grow more sophisticated, spanning multiple clouds, services, and technologies, keeping track of what’s happening across your stack becomes increasingly challenging. Enter New Relic, a comprehensive observability platform designed to provide end-to-end visibility into your entire technical ecosystem.

Founded in 2008 by Lew Cirne (whose name is the anagram of “New Relic”), the platform began as an Application Performance Monitoring (APM) solution. Over the years, it has evolved into a full-spectrum observability platform that helps organizations monitor, troubleshoot, and optimize their entire digital ecosystem.

What started as a way to monitor application performance has expanded to cover infrastructure monitoring, log management, browser and mobile applications, synthetic monitoring, and more—all unified under a single platform with a consistent data model called the New Relic One platform.

At the heart of New Relic is its Telemetry Data Platform, a unified repository for all your operational data. Unlike traditional monitoring tools that silo different types of telemetry data, New Relic brings together:

• Metrics: Numeric measurements of system performance
• Events: Discrete actions or occurrences
• Logs: Detailed records of system and application activities
• Traces: End-to-end journeys of requests across distributed systems

This unified approach allows data engineers to correlate issues across different components of their stack, making troubleshooting faster and more effective.

Powering the Telemetry Data Platform is NRDB (New Relic Database), a powerful time-series database optimized for high-cardinality data and sub-second queries. NRDB can ingest trillions of data points per day and allows data teams to query across all their telemetry data using NRQL (New Relic Query Language), a SQL-like query language.

Example NRQL query to analyze database performance:

SELECT average(duration), percentile(duration, 95, 99) 
FROM Transaction 
WHERE appName = 'DataWarehouse' AND name LIKE 'DatabaseQuery/%' 
FACET name 
TIMESERIES 5 minutes SINCE 3 hours ago

New Relic’s approach to observability spans the entire stack, providing visibility into:

• Applications: Performance metrics, error rates, and transaction tracing
• Infrastructure: Host metrics, cloud services, and containers
• Digital Customer Experience: Browser performance, mobile apps, and synthetic monitoring
• Serverless Functions: Monitoring for AWS Lambda, Azure Functions, and more
• Network Performance: Network telemetry and topology mapping

For data engineering teams, this means you can track a data pipeline from the moment data is ingested through processing, transformation, storage, and finally to consumption—all within a single platform.

While New Relic serves various technical disciplines, it offers specific value for data engineering teams:

Data pipelines are the lifeblood of data-driven organizations, and New Relic provides the tools to ensure they’re healthy and efficient:

• End-to-end visibility: Track data as it moves through your pipeline
• SLO monitoring: Ensure data freshness and availability meet business requirements
• Error detection: Quickly identify and resolve issues in ETL processes
• Performance optimization: Find bottlenecks in data processing flows

For database administrators and data engineers, New Relic offers specialized monitoring for popular databases including:

• Relational databases: MySQL, PostgreSQL, SQL Server, Oracle
• NoSQL databases: MongoDB, Redis, Cassandra
• Time-series databases: InfluxDB, TimescaleDB
• Data warehouses: Snowflake, Redshift, BigQuery

Key metrics tracked include:

• Query performance and slow query analysis
• Connection pool utilization
• Cache hit ratios
• Replication lag
• Resource utilization (CPU, memory, I/O)

Modern data stacks often include big data technologies, which New Relic can monitor through its infrastructure agents and integrations:

• Apache Kafka: Consumer lag, broker performance, topic throughput
• Apache Hadoop: HDFS metrics, YARN resource utilization, MapReduce job statistics
• Apache Spark: Executor metrics, job completion rates, shuffle operations
• Elasticsearch: Cluster health, indexing performance, search latency

As more data workloads move to cloud-native services, New Relic provides visibility into:

• AWS: S3, EMR, Glue, Athena, Redshift
• GCP: BigQuery, Dataflow, Dataproc, Cloud Storage
• Azure: Synapse Analytics, HDInsight, Data Lake Storage

For data pipelines built with tools like Airflow, dbt, or custom frameworks, New Relic offers SDK and API options to create custom instrumentation, allowing you to:

• Track task execution times and success rates
• Monitor data validation results
• Measure data quality metrics
• Create custom dashboards for data pipeline health

New Relic leverages machine learning to identify unusual patterns in your data:

• Proactive detection: Find issues before they impact business operations
• Baseline establishment: Understand normal performance patterns for your data systems
• Correlation analysis: Automatically identify relationships between incidents

For a data lake or warehouse, this might mean automatically detecting:

• Unusual spikes in query latency
• Unexpected changes in data volume
• Anomalous error rates in ETL jobs
• Performance degradation after a new data model deployment

Distributed tracing is particularly valuable for data engineering teams dealing with complex data flows across multiple services:

• End-to-end visibility: Follow data as it moves through your entire architecture
• Bottleneck identification: Pinpoint exactly where data processing slows down
• Error context: See the complete path that led to a failure
• Service dependencies: Understand how different components interact

Example: A data engineer troubleshooting slow dashboard performance could use distributed tracing to determine whether the issue lies in the query execution, data retrieval, transformation logic, or visualization rendering.

New Relic’s dashboarding capabilities allow data teams to create custom views tailored to their specific needs:

• Data pipeline status dashboards: Overall health and performance of ETL processes
• Database performance dashboards: Query performance, resource utilization, and connection metrics
• Data quality dashboards: Error rates, validation results, and data freshness
• Business impact dashboards: Connecting technical metrics to business outcomes

Effective alerting is crucial for maintaining reliable data systems:

• Smart alerts: Trigger notifications based on deviation from normal patterns
• Incident correlation: Group related issues to reduce alert fatigue
• Contextual notifications: Include relevant information needed for troubleshooting
• Multi-channel delivery: Receive alerts via email, Slack, PagerDuty, or custom webhooks

Common alert scenarios for data engineering include:

• Data pipeline failures or delays
• Excessive database load
• Storage capacity thresholds
• Data quality failures
• Service dependencies becoming unavailable

Getting started with New Relic involves:

1. Creating an account: Sign up for New Relic One
2. Installing agents: Deploy the appropriate agents for your technology stack
3. Configuring integrations: Connect to your databases, cloud services, and data tools
4. Setting up custom instrumentation: Instrument your data pipelines
5. Creating dashboards: Build views that provide visibility into your data infrastructure

New Relic offers multiple ways to integrate with your data stack:

• Infrastructure agents: Monitor hosts, VMs, and containers
• APM agents: Track application performance for code-based data processing
• Integration plugins: Connect to common databases and data technologies
• APIs and SDKs: Create custom instrumentation for proprietary systems
• Open source telemetry: Ingest data from OpenTelemetry and other standards

To get the most value from New Relic, consider these data modeling practices:

• Consistent naming: Use standardized naming conventions for services and metrics
• Meaningful attributes: Add context through tags and attributes
• Service maps: Define relationships between components
• Business context: Tie technical metrics to business outcomes

Example tagging strategy for a data pipeline:

environment: production
pipeline: customer_analytics
stage: transformation
data_source: web_events
team: data_engineering

An e-commerce company implemented New Relic to monitor their data platform that powers product recommendations, inventory management, and sales analytics:

Challenges:

• Inconsistent data freshness
• Slow dashboard performance during peak shopping hours
• Difficulty diagnosing pipeline failures

New Relic Implementation:

• End-to-end tracing across ingest, processing, and storage layers
• Custom dashboards for each data domain
• SLO monitoring for data freshness

Results:

• 62% reduction in MTTR (Mean Time to Resolution)
• 99.9% data pipeline SLA achievement
• Ability to predict and prevent issues during high-traffic events

A financial institution used New Relic to monitor their regulatory reporting data lake:

Challenges:

• Complex data dependencies across hundreds of sources
• Strict compliance requirements for data completeness
• Performance bottlenecks in analytical queries

New Relic Implementation:

• Distributed tracing across ETL workflows
• Custom instrumentation for data validation checks
• Database performance monitoring for analytical engines

Results:

• 40% improvement in end-to-end data processing time
• Early detection of data quality issues
• Comprehensive audit trails for compliance

New Relic vs. Datadog:

• Both offer full-stack observability, but with different strengths
• New Relic’s pricing model (based on data ingest) vs. Datadog’s host-based pricing
• Differences in UI/UX and query capabilities

New Relic vs. Dynatrace:

• Dynatrace’s emphasis on automated discovery and AI
• New Relic’s developer-centric approach
• Different approaches to deployment and agent architecture

New Relic vs. Open Source Stack (Prometheus, Grafana, Jaeger):

• Build vs. buy considerations
• Maintenance overhead
• Feature completeness and integration efforts

New Relic is particularly well-suited for data engineering teams when:

• You have complex, distributed data pipelines
• Your stack spans multiple technologies and platforms
• You need unified visibility across infrastructure, applications, and databases
• Business stakeholders require accessible dashboards and reporting
• You value a managed solution over maintaining monitoring infrastructure

New Relic’s pricing is based primarily on data ingestion, making cost management important:

• Selective instrumentation: Focus on high-value systems and services
• Sampling strategies: Use appropriate sampling for high-volume data
• Data retention policies: Customize retention based on data importance
• Attribute filtering: Limit high-cardinality attributes

The landscape of data observability continues to evolve:

• Increased automation: More AI-driven insights and autonomous remediation
• Observability as code: Infrastructure-as-code principles applied to monitoring
• Shift left: Observability built into data pipelines from the design phase
• OpenTelemetry adoption: Standardization of telemetry data collection
• Data quality integration: Closer ties between operational and data quality monitoring

New Relic is positioning itself to address these trends through continued platform development and strategic acquisitions.

To build a culture of observability in your data team:

• Instrument by default: Make instrumentation a standard part of all data engineering work
• Define clear SLOs: Establish objective metrics for data pipeline performance
• Democratize access: Give stakeholders appropriate views into data system health
• Conduct observability reviews: Include monitoring as part of code and architecture reviews
• Document patterns: Create reusable instrumentation patterns for common scenarios

Watch out for these common issues:

• Alert fatigue: Too many notifications leads to ignored alerts
• Excessive cardinality: High-cardinality attributes causing performance and cost issues
• Missing business context: Technical metrics without connection to business impact
• Gaps in coverage: Missing instrumentation for critical components
• Siloed observability: Separate monitoring for different parts of the data stack

As data infrastructures grow increasingly complex, comprehensive observability becomes essential for maintaining reliable, efficient data operations. New Relic offers data engineering teams a unified platform to monitor, troubleshoot, and optimize their entire data stack—from ingestion to analytics.

By providing visibility into applications, infrastructure, databases, and user experience, New Relic helps data engineers ensure that data pipelines are performing optimally and meeting business requirements. The platform’s unified approach to telemetry data, powerful query capabilities, and flexible visualization options make it a strong contender for teams seeking to implement observability at scale.

While no monitoring solution is perfect for every use case, New Relic’s comprehensive feature set and focus on full-stack observability make it particularly well-suited for modern data architectures that span multiple technologies, services, and deployment environments.

By adopting New Relic and embracing observability-driven practices, data engineering teams can reduce troubleshooting time, prevent outages, optimize performance, and ultimately deliver more value to their organizations through reliable, efficient data systems.

#NewRelic #Observability #DataEngineering #DataMonitoring #APM #PerformanceMonitoring #DataPipelines #DataOps #CloudMonitoring #DistributedTracing #DatabaseMonitoring #NRQL #Telemetry #DataVisualization #ETLMonitoring #SRE #DevOps #DataInfrastructure #BigData #DataObservability