Anti-patterns for data ingestion and processing

Over-reliance on ETL Tools: Over-relying on ETL (Extract, Transform, Load) tools for data processing can lead to inflexibility and difficulties adapting to data source changes. Where possible, use tools with native integrations that allow ETL-free data processing and analysis pipelines, enabling a more flexible and scalable way to integrate data from multiple sources without introducing additional operational overhead.

Ignoring event correlation: Ignoring the correlation of multiple alerts can hide broader issues. Incorporate event correlation into the observability strategy to quickly identify and resolve problems across multiple tools and systems. Utilize distributed tracing tools to trace requests across multiple services and dependencies to identify bottlenecks or issues, centralized logs and events for security investigations, and use normalized data formats to enable correlation of telemetry from multiple sources.

Inefficient data analysis: Relying on monolithic or manual data processing methods leads to inefficient data analysis. Monolithic data processing of large volumes leads to long wait times, slow detection and reaction times, and potentially increased cost. Manual data processing, on the other hand, is error-prone and time-consuming. Overcome these inefficiencies by adopting scalable and distributed architectures like serverless computing, capable of handling large data volumes in parallel. Data processing should be automated wherever possible to ensure consistent, error-free, and efficient data analysis.

Lack of data governance: Poor data governance practices can lead to inaccurate data, poor decision-making, and compliance risks. Establish and enforce data governance policies, including data quality checks, granular access control, and data provenance tracking.