# The CI/CD moment for data analytics

DevFeed: [The CI/CD moment for data analytics](<https://devfeed.tech/articles/the-ci-cd-moment-for-data-analytics-12230.md>)

Original publisher: [Read original article](<https://platformengineering.org/blog/the-ci-cd-moment-for-data-analytics>)

Author: Gaurav Nanda

Published: 2026-07-23T05:40:01Z

Content type: article

Language: en

Sources: [Platform Engineering Blog](<https://devfeed.tech/sources/platform-engineering-blog.md>)

Topics: [data analytics](<https://devfeed.tech/topics/data-analytics.md>), [DataOps](<https://devfeed.tech/topics/dataops.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [Kafka](<https://devfeed.tech/topics/kafka.md>), [apache-flink](<https://devfeed.tech/topics/apache-flink.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [apache-flink](<https://devfeed.tech/tags/apache-flink.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [bridging](<https://devfeed.tech/tags/bridging.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data-analytics](<https://devfeed.tech/tags/data-analytics.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [event](<https://devfeed.tech/tags/event.md>), [fraud](<https://devfeed.tech/tags/fraud.md>), [google](<https://devfeed.tech/tags/google.md>), [insights](<https://devfeed.tech/tags/insights.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [latency](<https://devfeed.tech/tags/latency.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [scale](<https://devfeed.tech/tags/scale.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [systems](<https://devfeed.tech/tags/systems.md>), [tools](<https://devfeed.tech/tags/tools.md>)

## AI overview

The article argues that data analytics is approaching a CI/CD-like transition toward continuous analytics. It describes how multi-step pipelines introduce latency, operational risk, and maintenance burden, and explains why real-time insight is becoming a baseline platform capability for applications such as personalization, fraud detection, reliability, and AI-driven features.

## Source excerpt

How converging OLTP and OLAP architectures are driving 'Continuous Analytics', the CI/CD moment for data to deliver real-time, unified insights and operational simplicity.