# analytics stack Published articles for analytics stack. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Beyond the Dashboard: Accelerating Real-Time Intelligence in the Age of AI DevFeed: [Beyond the Dashboard: Accelerating Real-Time Intelligence in the Age of AI]() Original publisher: [Read original article]() Author: Kostiantyn Okhrimenko Published: 2026-08-27T11:15:58Z Content type: article Language: en Sources: [Booking.com Development - Medium]() Topics: [semantic-layer](), [genai](), [analytics stack](), [Large Language Model](), [Artificial Intelligence](), [data](), [dashboards](), [Query (disambiguation)]() Tags: [agent](), [ai](), [ai-tools](), [analytics-stack](), [bi-tools](), [dashboards](), [data](), [genai](), [real-time](), [self-service](), [self-serving-analytics](), [semantic-layer](), [sql]() ### AI overview The article examines how GenAI-driven natural-language interfaces can help stakeholders obtain trusted data answers without repeatedly interrupting data and engineering teams. It argues that a robust semantic layer is necessary to make self-service analytics reliable and precise. ### Source excerpt When an urgent request for a report or dashboard arrives, often just before an executive meeting, data and engineering teams must drop planned work to respond. One request may be reasonable, but repeated interruptions come at a cost: important work, such as scaling infrastructure, improving reliability, models optimization, gets pushed back, while quick, one-off dashboards become more technical debt to maintain. For managers and other decision-makers, the need is real: they require reliable data to make decisions quickly. But getting an answer often depends on someone who knows SQL, understands the data structure, and has time to help. When those people are already busy, the question waits, even when the answer is sitting in the data warehouse. By the time the report is ready, the decision window may have passed. This is not just a prioritization issue. We need a better way for people to get trusted answers quickly without constantly pulling teams away from building and improving the data platform. All of the above can be illustrated by the image: Image 1: Typical reporting circleWhat we will talk about The explosion of GenAI over the last few years has shifted the focus for the modern analytics stack. We are evolving beyond traditional Data Democratization, which often gave teams access to complex pre-AI tools without clear governance, toward natural language data interaction: asking questions in plain English -- Talk to your data concept. In the traditional stack, the "interface" to data was either a dashboard or a SQL editor. This created a high barrier to entry that caused the friction. By properly architecting and utilizing GenAI-driven tools, we can finally bridge the gap between intent and insight. Talk to your data is a self-serve ecosystem where any stakeholder can bypass the traditional ticketing queue and, instead of waiting for an engineer to interpret a requirement and translate it into a query, the user engages with a specialised agent. The challenge, h ## The forbidden index: Building privacy-preserving search with OpenSearch DevFeed: [The forbidden index: Building privacy-preserving search with OpenSearch]() Original publisher: [Read original article]() Author: Kylie Wagar-Dirks Published: 2026-07-24T15:00:22Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Open Source](), [Access Control](), [pii](), [dashboards](), [tokenization](), [data](), [elasticsearch](), [Framework]() Tags: [analytics](), [analytics-stack](), [blog](), [compliance](), [dashboards](), [data](), [dynamic-data](), [open-source](), [opensearch](), [pii](), [plugin](), [privacy](), [search](), [tokenization]() ### AI overview The article describes a talk by Unnati Mishra and Akshat Khanna on building privacy-preserving search and analytics with OpenSearch. Their approach moves privacy controls into the ingest layer, using a custom plugin to redact PII at index time through tokenization and dynamic data masking. It also applies differential privacy to OpenSearch Dashboards by adding calibrated noise to aggregate query results. ### Source excerpt At OpenSearchCon Europe 2026, Unnati Mishra and Akshat Khanna (Angel One) argued that privacy in search must be handled during the ingest phase rather than retroactively through access controls. The post The forbidden index: Building privacy-preserving search with OpenSearch appeared first on OpenSearch. ## Building a Modern Analytics Stack Around ClickHouse DevFeed: [Building a Modern Analytics Stack Around ClickHouse]() Original publisher: [Read original article]() Author: Sebastian Insausti Published: 2026-07-01T10:35:21Z Content type: article Language: en Sources: [SeveralNines]() Topics: [clickhouse](), [Databases](), [Open Source](), [MySQL](), [PostgreSQL](), [real-time](), [NVMe](), [Replication](), [IO]() Tags: [analytics](), [analytics-stack](), [clickhouse](), [cta](), [database-general](), [databases](), [distributed](), [mysql](), [nvme](), [olap](), [open-source](), [postgresql](), [real-time]() ### AI overview This article explains how ClickHouse fits into a modern analytics stack alongside relational databases, Redis, and Prometheus. It describes ClickHouse as an open-source column-oriented DBMS for analytical workloads and discusses columnar storage, distributed processing, replication, and near-real-time ingestion. ### Source excerpt Historically, relational databases did double duty. The same PostgreSQL / MySQL instance that handled your application's writes also answered your business questions. A well-indexed schema, a few GROUP BY reports, done. And that works, right up until it doesn't: the reports get slower, the dashboards start eating the same I/O budget as the application; or, [...] The post Building a Modern Analytics Stack Around ClickHouse appeared first on Severalnines. ## Why we moved our growth analytics back into Tinybird DevFeed: [Why we moved our growth analytics back into Tinybird]() Original publisher: [Read original article]() Author: Pablo Abella Published: 2026-06-01T10:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [data]() Tags: [ai-x-data](), [analytics](), [analytics-stack](), [data](), [growth](), [scalable-analytics-architecture]() ### AI overview Tinybird describes Birdwatcher, its internal growth analytics stack, and explains why it moved more of its analytics workflow onto its own data and platform. ### Source excerpt The story of Birdwatcher, Tinybird's internal growth analytics stack, and why we moved more of our analytics loop onto our own data. ## 10x speed, 10x savings: Constant Contact's journey from Pinot to ClickHouse DevFeed: [10x speed, 10x savings: Constant Contact's journey from Pinot to ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-11-04T07:39:45Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [data](), [dashboards](), [Databases]() Tags: [amazon](), [analytics](), [analytics-stack](), [apache](), [clickhouse](), [dashboards](), [olap](), [pinot](), [real-time](), [streaming]() ### AI overview Constant Contact rebuilt its real-time analytics stack by migrating from Apache Pinot to ClickHouse. The article describes the scaling and maintenance problems caused by Pinot's handling of upserts and growing data volumes, and explains how ClickHouse fit the team's OLAP and reporting requirements. ### Source excerpt Constant Contact rebuilt its real-time analytics stack with ClickHouse. In this blog post, they share their lessons doing so.