# Time Series Time series is an ordered sequence of measurements of a variable at equally spaced time intervals, used in analysis and forecasting. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine DevFeed: [ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine]() Original publisher: [Read original article]() Author: James Cunningham Published: 2026-09-15T14:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Prometheus](), [Time Series](), [Cloud](), [Grafana](), [telemetry](), [Traces]() Tags: [clickhouse](), [cloud](), [grafana](), [logs](), [metrics](), [prometheus](), [time-series](), [traces]() ### AI overview ClickHouse announces a private preview of PromQL support and a time-series table engine in ClickHouse Cloud, allowing Prometheus metrics to be stored in ClickHouse and queried with existing PromQL. ### Source excerpt ClickHouse PromQL support lets you store Prometheus metrics in ClickHouse Cloud, query them using familiar PromQL, and bring metrics together with your logs and traces without rewriting queries in SQL. ## TimesFM-3: A zero-shot foundation model for multivariate forecasting DevFeed: [TimesFM-3: A zero-shot foundation model for multivariate forecasting]() Original publisher: [Read original article]() Published: 2026-08-31T17:19:40Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [Time Series](), [Google](), [datasets](), [Transformer architecture](), [Fine-tuning](), [generalization in machine learning]() Tags: [data-management](), [features](), [fine-tuning](), [foundation-models](), [google](), [machine-intelligence](), [model](), [product](), [time-series](), [transformer-architecture](), [zero-shot]() ### AI overview Google Research introduces TimesFM-3, a 330-million-parameter time-series foundation model designed for accurate multivariate forecasting in a single forward pass. Pre-trained on more than one trillion real-world and synthetic time points, it jointly models coevolving series and external covariates in zero-shot settings without task-specific fine-tuning. ### Source excerpt Data Management ## pg\_statviz 1.2 released with PostgreSQL 19 support and new features DevFeed: [pg\_statviz 1.2 released with PostgreSQL 19 support and new features]() Original publisher: [Read original article]() Author: Pg Statviz Project Published: 2026-08-25T00:00:00Z Content type: release Language: en Sources: [PostgreSQL news]() Topics: [PostgreSQL](), [Monitoring](), [Time Series](), [Artificial Intelligence](), [API](), [OpenAI](), [Claude]() Tags: [ai](), [ai-models](), [api](), [claude](), [features](), [monitoring](), [openai](), [postgresql](), [release](), [series]() ### AI overview pg_statviz 1.2 adds PostgreSQL 19 support, a blocking-locks analysis module, OpenAI API integration, and updated default Claude and Gemini models. It remains a lightweight PostgreSQL extension and utility for analyzing and visualizing internal statistics, with optional AI features. ### Source excerpt Just in time for the PostgreSQL 19 betas, I'm excited to announce release 1.2 of pg_statviz, the minimalist extension and utility pair for time series analysis and visualization of PostgreSQL internal statistics. This release adds support for the upcoming PostgreSQL 19: pg_statviz now captures the new wal_fpi_bytes counter from pg_stat_wal. The PG18/19 I/O worker, effective WAL level, and autovacuum scoring settings are captured in snapshot_conf. The release has been tested against 19 beta3, and across the whole PostgreSQL 13 to 19 range. It also introduces a new blocking locks analysis module: Each snapshot now records the number of blocked and blocking sessions, along with a breakdown by lock type (relation, transactionid, tuple, and so on). Detection is built on pg_blocking_pids(), so even soft blocks (sessions that are just ahead in the lock wait queue) are counted, not just hard conflicts. Storage stays lightweight: table size is independent of how many sessions were involved in the blocking. The module produces charts and AI verdicts like every other module, and the deterministic severity floor applies here too: sustained blocking can never be reported as healthy. Also new is the openai AI provider: --ai openai uses the OpenAI API, so the same flag works with OpenAI itself and with any other service or local server that implements that API. You can select the endpoint and model with the OPENAI_BASE_URL and OPENAI_MODEL environment variables. The openai package has been added to the [ai] extras, and zero-dependency installs remain unchanged. Finally, this release also updates the default AI models to claude-sonnet-5 for Claude and gemini-3.7-flash for Gemini. pg_statviz takes the view that everything should be light and minimal. Unlike commercial monitoring platforms, it doesn't require invasive agents or open connections to the database: it all lives inside your database. The extension is plain SQL and PL/pgSQL and doesn't require modules to be loaded, the vis ## An AI tool for prioritizing candidate biomarkers from wearable sensor data DevFeed: [An AI tool for prioritizing candidate biomarkers from wearable sensor data]() Original publisher: [Read original article]() Published: 2026-08-21T17:02:24Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [data](), [Time Series](), [datasets](), [Temporal data](), [Training AI Models](), [Model Development]() Tags: [agents](), [ai](), [analysis](), [data](), [datasets](), [generative-ai](), [health-bioscience](), [memory](), [reasoning](), [research](), [series](), [time-series](), [training](), [validation](), [workflow]() ### AI overview The Biomarker Discovery Framework is a supervised multi-agent system for prioritizing biomarker candidates from wearable-sensor data. It combines hypothesis generation, statistical analysis, model training, adversarial validation, and literature-grounded reasoning in a traceable six-phase workflow. Across three cohorts, it recovered known clinical signals, found convergent biomarkers across independent datasets, and improved downstream prediction when demographic features were included. ### Source excerpt Generative AI ## Two Providers, a Stubborn Plateau and a Very Long Tail: Email in the Tranco Top-1M DevFeed: [Two Providers, a Stubborn Plateau and a Very Long Tail: Email in the Tranco Top-1M]() Original publisher: [Read original article]() Author: Artem Berezin Published: 2026-07-30T11:49:26Z Content type: article Language: en Sources: [RIPE Labs]() Topics: [Time Series](), [microsoft 365](), [Resilience](), [Google](), [Microsoft](), [migration](), [Server](), [Internet](), [data](), [Software as a service](), [proofpoint]() Tags: [2026](), [authentication](), [competition](), [data](), [dns](), [google](), [measurements](), [microsoft](), [microsoft-365](), [migration](), [popular](), [resilience](), [saas](), [server](), [time-series]() ### AI overview A decade of DNS measurements across the Tranco top 1 million domains shows that email infrastructure is consolidating around Google Workspace and Microsoft 365. Self-hosted mail servers declined from 44.6% in 2016 to 22.4% in July 2026, while the two leading providers together handled 38.6% of measured domains. DMARC enforcement has plateaued, and a large long tail of infrastructure remains difficult to classify. The article frames this concentration as a resilience concern because outages, filtering changes, or policy decisions could affect a substantial part of the email ecosystem. ### Source excerpt Ten years of DNS measurements reveal three trends across the Internet's most popular domains: email continues to consolidate around two providers, DMARC enforcement has hit a plateau, and a surprisingly large long tail of infrastructure defies easy classification. ## Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making DevFeed: [Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making]() Original publisher: [Read original article]() Author: David Chandler | Laboratory for Information and Decision Systems Published: 2026-07-14T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [decision-making](), [Structured-data](), [AI Models](), [data](), [Time Series](), [Computer science](), [Electrical engineering and computer science (EECS)](), [Temporal data](), [systems]() Tags: [ai](), [ai-and-decision-making](), [ai-in-business-planning](), [ai-models](), [algorithms](), [artificial-intelligence](), [business-and-management](), [business-modeling](), [celonis-context-model](), [computer-science-and-technology](), [data](), [data-systems](), [decision-making](), [devavrat-shah](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [forecasting](), [idss](), [ikigailabs](), [laboratory-for-information-and-decision-systems-lids](), [mit-eecs](), [mit-idss](), [mit-intellectual-property](), [mit-lids](), [mit-schwarzman-college-of-computing](), [predictions](), [profile](), [real-time](), [research](), [school-of-engineering](), [startups](), [structured](), [tabular-data](), [time-series-data]() ### AI overview MIT Professor Devavrat Shah's research led to a foundation model for tabular and time-series enterprise data. Developed with Ikigai Labs, the system continuously tests predictions against real outcomes to support large-scale forecasting and decision-making. ### Source excerpt Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. ## Building a Custom Metrics Exporter for Kubernetes DevFeed: [Building a Custom Metrics Exporter for Kubernetes]() Original publisher: [Read original article]() Author: Victor David Effiok Published: 2026-07-14T18:00:00Z Content type: tutorial Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [autoscaling](), [Grafana Cloud Metrics](), [Time Series](), [Go Language](), [WebSocket]() Tags: [autoscaling](), [building](), [code](), [go](), [http](), [kubernetes](), [metrics](), [prometheus](), [server](), [time-series]() ### AI overview A tutorial on building a custom Kubernetes metrics exporter that exposes application or external state for Prometheus, enabling queries, alerts, and HorizontalPodAutoscaler scaling. It covers exporter architecture, direct instrumentation, metric formatting, and choosing counters, gauges, or histograms. ### Source excerpt Kubernetes ships with built-in awareness of CPU and memory, but most real-world scaling decisions depend on signals that live entirely outside that narrow window: how many messages are waiting in a queue, how long the last batch job took, how many active WebSocket connections a pod is holding. When the built-in metrics are not enough, a metrics exporter bridges that gap. This post walks through writing one from scratch, packaging it as a container, and wiring it into a cluster so that Prometheus -- and ultimately the HorizontalPodAutoscaler -- can consume it. What a metrics exporter actually does An exporter is a small HTTP server with a single responsibility: expose application state as text on a /metrics endpoint. Prometheus scrapes that endpoint on a regular interval, stores the time-series data, and makes it available for queries, alerts, and autoscaling rules. In some cases you can instrument your application directly -- embedding the Prometheus client library and exposing /metrics from within the same process -- rather than running a separate exporter. A standalone exporter makes more sense when the data source is external to your application or when you do not control the application code. The format Prometheus expects is plain text -- one metric per line, with a name, optional labels, and a numeric value. Client libraries handle the serialization for you, so in practice you only need to decide what to measure and call the right function when that value changes. Choosing what to measure Before writing any code, it helps to decide what kind of signal you are dealing with. The Prometheus data model has three main types: Counters only ever increase. They are the right tool for totals: requests served, jobs processed, errors encountered. Never use a counter for a value that can go down. Gauges represent a current snapshot of a value that can rise and fall freely. Queue depth, active connections, and cache size are all gauges. Histograms record the distribution of obse ## ScyllaDB vs Aerospike, Wide-Column vs. Key/Value DevFeed: [ScyllaDB vs Aerospike, Wide-Column vs. Key/Value]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-07-06T13:13:23Z Content type: comparison Language: en Sources: [ScyllaDB]() Topics: [NoSQL](), [Database](), [Benchmark](), [Time Series](), [Availability](), [Replication](), [Encryption](), [Cloud](), [Latency]() Tags: [article](), [availability](), [benchmark](), [database](), [encryption](), [latency](), [leadership-team](), [performance](), [product](), [replication](), [security](), [time-series]() ### AI overview This comparison explains how wide-column databases differ from key-value stores, using ScyllaDB and Aerospike as examples. It argues that ScyllaDB's broader data model, clustering keys, replication, availability, and operational features can support more complex access patterns while maintaining strong performance and low latency. ### Source excerpt Wide-column flexibility doesn't have to come at the expense of performance -- see where the two models differ, where each one wins, and why you no longer have to choose ## Dogfooding the Billable Actions metric: How granular observability improved our metering validation DevFeed: [Dogfooding the Billable Actions metric: How granular observability improved our metering validation]() Original publisher: [Read original article]() Author: Eric Chen Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [observability](), [dashboards](), [Cloud](), [API](), [Server](), [Time Series](), [benchmarking]() Tags: [account](), [api](), [cloud](), [cost](), [count](), [dashboard](), [dashboards](), [dogfooding](), [metric](), [observability](), [optimization](), [query](), [raw](), [server](), [temporal-voices](), [time-series](), [validation](), [visibility]() ### AI overview Temporal describes how its Billable Actions metric and OpenMetrics per-type breakdowns provide more granular cost visibility. Internally, the company uses the metric with a canary account and validation dashboards to verify metering accuracy, troubleshoot discrepancies, and identify optimization opportunities. ### Source excerpt See how Temporal uses the Billable Actions metric internally to validate metering, troubleshoot discrepancies, and improve cost observability. ## Introducing ClickStack Cloud: Serverless observability powered by ClickHouse DevFeed: [Introducing ClickStack Cloud: Serverless observability powered by ClickHouse]() Original publisher: [Read original article]() Author: Mike Shi Published: 2026-05-27T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [Cloud](), [OpenTelemetry](), [Serverless](), [telemetry](), [log management](), [Traces](), [Time Series]() Tags: [clickhouse](), [cloud](), [complexity](), [cost](), [data](), [infrastructure](), [logs](), [metrics](), [observability](), [opentelemetry](), [performance](), [platform](), [scale](), [serverless](), [telemetry](), [time-series](), [traces]() ### AI overview ClickStack Cloud is a fully managed, serverless observability service built on ClickHouse. It accepts OpenTelemetry data through a managed OTLP endpoint and lets teams explore logs, metrics, and traces while the platform handles ingestion, buffering, scaling, and storage. ### Source excerpt Introducing ClickStack Cloud: a fully managed, serverless observability platform built on ClickHouse where you send OpenTelemetry data to a managed endpoint and immediately explore logs, metrics, and traces without operating any infrastructure. ## How the D. E. Shaw group powers high-cardinality observability at scale with ClickHouse DevFeed: [How the D. E. Shaw group powers high-cardinality observability at scale with ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-15T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [High-Performance Computing](), [Prometheus](), [Time Series](), [benchmarking](), [Compression](), [tracing]() Tags: [clickhouse](), [compression](), [compute](), [high-performance-computing](), [infrastructure](), [metrics](), [observability](), [performance](), [production](), [prometheus](), [scale](), [site-reliability](), [systems](), [time-series](), [tracing]() ### AI overview The D. E. Shaw group uses ClickHouse for high-cardinality observability across millions of compute workloads on its internal grid. The article describes evaluation results showing approximately 7x better performance than alternatives, production ingestion exceeding 500,000 records per second, and improved long-term capacity planning and tracing analysis. ### Source excerpt How the D. E. Shaw group replaced its previous observability platform with ClickHouse to handle high-cardinality metrics at scale, achieving 7x better query performance and enabling multi-year capacity planning across millions of compute workloads. ## ClickHouse vs Prometheus for High Cardinality, Part 1: Understanding the Problem DevFeed: [ClickHouse vs Prometheus for High Cardinality, Part 1: Understanding the Problem]() Original publisher: [Read original article]() Author: Rory Crispin; Dale McDiarmid Published: 2026-05-14T15:13:45Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Prometheus](), [observability](), [Time Series](), [telemetry]() Tags: [architecture](), [churn](), [clickhouse](), [metrics](), [observability](), [prometheus](), [time-series]() ### AI overview This article explains why high cardinality creates challenges for Prometheus and other series-oriented observability systems. It examines the effects on series creation, memory use, querying, and churn, and introduces a comparison with how ClickHouse handles these workloads. ### Source excerpt Why does high cardinality break Prometheus but not ClickHouse? In Part 1, we explore the architectural tradeoffs of Prometheus and other series-based systems, showing how cardinality impacts memory, ingestion, querying, and operational stability at scale. ## LABScon25 Replay | Breach Alpha: Trading on Cyber Fallout DevFeed: [LABScon25 Replay | Breach Alpha: Trading on Cyber Fallout]() Original publisher: [Read original article]() Author: LABScon Published: 2026-05-14T13:00:44Z Content type: article Language: en Sources: [SentinelLabs - We are hunters, reversers, exploit developers, and tinkerers shedding light on the world of malware, exploits, APTs, and cybercrime across all platforms.]() Topics: [Cybersecurity](), [LABScon](), [incident](), [Artificial Intelligence](), [Time Series](), [dataset](), [ransomware]() Tags: [ai](), [breach](), [cybersecurity](), [data](), [labscon](), [labscon25](), [model](), [ransomware](), [time-series](), [trading]() ### AI overview Mick Baccio and Scott Roberts examine whether public breach signals can anticipate stock-market reactions before formal disclosure. Using AI-assisted data collection, a public-disclosure dataset, an intuition-led model, and Hidden Markov Model time-series analysis, they test a "15/30" cyber-event trading hypothesis and find highly mixed results. ### Source excerpt Mick Baccio and Scott Roberts examine whether public breach signals and market timing models can turn cyber incidents into actionable trading opportunities. ## How We Built Time Series: Configuration-Driven Visualization in Tinybird Forward DevFeed: [How We Built Time Series: Configuration-Driven Visualization in Tinybird Forward]() Original publisher: [Read original article]() Author: Julia Vallina Published: 2026-02-09T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [Time Series](), [clickhouse](), [SQL](), [configuration](), [debug]() Tags: [clickhouse](), [configuration](), [debug](), [engineering-excellence](), [sql](), [time-series](), [visualization]() ### AI overview An account of how Tinybird rebuilt its Time Series feature, focusing on the SQL problems encountered, the ClickHouse patterns used, and its use for debugging alerts. ### Source excerpt How we rebuilt Time Series: the SQL problems we hit, the ClickHouse patterns we used, and how we use it to debug our own alerts. ## New and improved Time Series Charts & Playgrounds in Forward DevFeed: [New and improved Time Series Charts & Playgrounds in Forward]() Original publisher: [Read original article]() Author: Jorge Sancha Published: 2026-01-29T00:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [Time Series](), [browser](), [SQL](), [Artificial Intelligence]() Tags: [ai](), [browser](), [product-updates](), [sql](), [time-series]() ### AI overview Tinybird Forward adds improved Time Series charts and Playgrounds, allowing users to explore with AI, write SQL, or visualize data in the browser. ### Source excerpt Whether you want to Explore with AI, Write SQL directly or visualize data, you can now do it from the browser in Tinybird Forward. Time Series and Playgrounds bring back the flexibility of Classic, improved for Forward ## Behind the music: How Chartmetric is scaling music analytics with ClickHouse DevFeed: [Behind the music: How Chartmetric is scaling music analytics with ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-01-08T14:17:18Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data](), [Time Series](), [real-time](), [Streaming](), [API](), [dashboards](), [Web app](), [airflow](), [elasticsearch](), [Web]() Tags: [airflow](), [analytics](), [api](), [app](), [apple](), [cache](), [clickhouse](), [cloud](), [dashboards](), [data](), [elasticsearch](), [music](), [scale](), [search](), [series](), [speed](), [streaming](), [time](), [time-series](), [web]() ### AI overview Chartmetric uses ClickHouse Cloud to scale real-time music analytics across billions of rows from streaming services, social media, and music charts. The migration from Postgres and Snowflake improved query speed and reduced storage costs, while ClickHouse now supports LLM-facing queries and a playlist cache ingesting more than 15 million rows daily. ### Source excerpt "ClickHouse works very well as part of our multi-system data stack. It's excellent for time-series data, and the VersionedCollapsingMergeTree engine was a game-changer for us, speeding up queries from 20 seconds in Snowflake to 1.5 seconds in ClickHouse." ## Book Review - Just Use Postgres! DevFeed: [Book Review - Just Use Postgres!]() Original publisher: [Read original article]() Author: vladmihalcea Published: 2025-11-24T09:00:34Z Content type: opinion Language: en Sources: [Vlad Mihalcea]() Topics: [PostgreSQL](), [Databases](), [SQL](), [integrity](), [JSON](), [Time Series](), [Transactions](), [Embeddings](), [genai]() Tags: [book](), [book-review](), [embeddings](), [genai](), [integrity](), [json](), [postgres](), [postgresql](), [sql](), [time-series](), [transactions]() ### AI overview A review of Denis Magda's book Just Use Postgres!, describing its audience, structure, and coverage of PostgreSQL querying, data integrity, transactions, indexes, JSON, full-text search, extensions, embeddings, and time-series workloads. ### Source excerpt Introduction My friend, Denis Magda, wrote a wonderful book called Just Use Postgres!, and I'm glad that I got the chance to read it. Audience This book is useful for any software developer who's either using PostgreSQL or plans on using it because it covers a lot of features that are very useful for modern applications. No matter your level of seniority, you are definitely going to learn from Denis' book. Content Just Use Postgres! has 402 pages, 3 parts, and 11 chapters. The first part is an introduction to PostgreSQL and... Read More The post Book Review - Just Use Postgres! appeared first on Vlad Mihalcea. ## Building StockHouse: Real-time market analytics with ClickHouse DevFeed: [Building StockHouse: Real-time market analytics with ClickHouse]() Original publisher: [Read original article]() Author: Lionel Palacin Published: 2025-11-13T16:21:22Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [Streaming](), [Time Series](), [Go Language](), [WebSocket](), [Node.js](), [Vue.js](), [dashboards](), [Back end](), [Front end]() Tags: [architecture](), [backend](), [clickhouse](), [engineering](), [financial](), [go](), [js](), [low-latency](), [node](), [observability](), [real-time](), [series](), [trading](), [vue](), [vue-js](), [web]() ### AI overview This article presents StockHouse, a complete demo for building a real-time market analytics application. It uses Massive WebSocket APIs for live stock and crypto data, ClickHouse for efficient storage and low-latency analysis, and Perspective for interactive visualization. ### Source excerpt Learn more how we built StockHouse, a real-time financial analytics application with Massive, ClickHouse and Perspective that scales to thousands of events per second. ## How we are building the personal health coach DevFeed: [How we are building the personal health coach]() Original publisher: [Read original article]() Published: 2025-10-27T22:36:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [Google](), [Time Series](), [data](), [Statistics](), [Android](), [iOS]() Tags: [2025](), [ai](), [android](), [gemini](), [generative-ai](), [google](), [health-bioscience](), [ios](), [preview](), [science](), [statistics](), [time-series]() ### AI overview Google describes a Gemini-powered personal health coach designed to provide personalized, adaptive coaching grounded in science and expert oversight. The optional public preview uses Fitbit data to generate health insights and applies numerical reasoning to physiological time-series data such as sleep and activity. ### Source excerpt Generative AI ## TimescaleDB to ClickHouse replication: Use cases, features, and how we built it DevFeed: [TimescaleDB to ClickHouse replication: Use cases, features, and how we built it]() Original publisher: [Read original article]() Author: The ClickPipes Team Published: 2025-09-09T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Replication](), [migration](), [Time Series](), [Database](), [Open Source]() Tags: [analytics](), [clickhouse](), [data](), [data-migrations](), [database](), [migration](), [migrations](), [open-source](), [performance](), [postgres](), [real-time](), [replication](), [sync](), [time-series]() ### AI overview This article explains how ClickHouse's Postgres CDC connector in ClickPipes supports one-time migrations and continuous replication from TimescaleDB to ClickHouse Cloud. It describes online and iterative migration scenarios, synchronization with low lag, and co-existence of TimescaleDB and ClickHouse for real-time workloads and analytics. ### Source excerpt The Postgres CDC connector in ClickPipes now supports one-time migrations and continuous replication from TimescaleDB. ## IoT for fun and Prophet: Scaling IoT and predicting the future DevFeed: [IoT for fun and Prophet: Scaling IoT and predicting the future]() Original publisher: [Read original article]() Author: Bryan Wood Published: 2025-07-22T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [Internet of things](), [Redpanda-Connect](), [Streaming](), [Time Series](), [ESP32](), [Amazon Web Services](), [Amazon S3](), [schema-evolution](), [event driven](), [Hardware](), [Kafka]() Tags: [apache-iceberg](), [apache-iceberg-for-scalable-storage](), [aws](), [core](), [devices](), [esp32-mqtt-iot-example](), [event-driven](), [hardware](), [integrating-iceberg-and-redpanda](), [iot](), [iot-data-pipeline-architecture](), [iot-predictive-analytics](), [iot-sensor-data-forecasting](), [predictive-analysis-with-iot](), [prophet-forecasting-for-iot](), [real-time-iot-data-streaming](), [redpanda-connect](), [redpanda-connect-for-iot](), [s3](), [scaling-iot-with-prophet](), [schema-evolution](), [schema-management-in-iot](), [sensor](), [streaming](), [thought-leadership]() ### AI overview This tutorial presents a scalable IoT data pipeline for real-time streaming and forecasting. It combines Redpanda and Redpanda Connect for ingestion and messaging, Apache Iceberg for scalable time-series data storage, AWS IoT for device-to-cloud communication, and ESP32 hardware for affordable connected-device experiments and deployments. ### Source excerpt Check out this real-world example of scaling IoT for predictive analysis with Redpanda, Iceberg, and Prophet--without high costs or complexity. ## How Chartmetric uses ClickHouse to turn artist data into music intelligence DevFeed: [How Chartmetric uses ClickHouse to turn artist data into music intelligence]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-05-21T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Time Series](), [data](), [real-time](), [elasticsearch](), [Amazon EC2](), [Cloud]() Tags: [2025](), [analytics](), [api](), [clickhouse](), [cloud](), [data](), [elasticsearch](), [instagram](), [latency](), [music](), [open](), [open-source](), [performance](), [real-time](), [search](), [self-hosting](), [streaming](), [tiktok](), [time-series](), [youtube]() ### AI overview Chartmetric uses ClickHouse Cloud to analyze large-scale music data, including streaming counts, social signals, playlist placements, and chart rankings. The article describes how ClickHouse helped improve time-series query performance, reduce storage costs, and support API responsiveness as the platform scaled. ### Source excerpt "ClickHouse has been excellent, delightful, wonderful--I can't think of enough words to describe how nice it is for time series data and especially reducing storage costs and improving API performance." Peter Gomez, Lead Engineer ## Essential Monitoring Queries: Creating a Dashboard in ClickHouse Cloud DevFeed: [Essential Monitoring Queries: Creating a Dashboard in ClickHouse Cloud]() Original publisher: [Read original article]() Author: Mihir Gokhale Published: 2025-05-20T12:11:18Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [dashboards](), [Monitoring](), [Cloud](), [real-time](), [SQL](), [Time Series]() Tags: [building](), [clickhouse](), [cloud](), [dashboards](), [monitoring](), [real-time](), [sql](), [time-series](), [visualization]() ### AI overview This tutorial shows how to turn essential ClickHouse monitoring queries into a centralized, reusable dashboard in ClickHouse Cloud. It demonstrates tables, time-series visualizations, query parameters, interactive time filters, and dashboard sharing. ### Source excerpt A short, easy-to-follow guide to building your own monitoring dashboard in ClickHouse Cloud. Real-time visibility. Zero setup. Just a few clicks. ## Anomaly Detection in Time Series Using Statistical Analysis DevFeed: [Anomaly Detection in Time Series Using Statistical Analysis]() Original publisher: [Read original article]() Author: Ivan Shubin Published: 2025-04-15T18:45:36Z Content type: tutorial Language: en Sources: [Booking.com Development - Medium]() Topics: [Time Series](), [Statistics](), [Monitoring](), [data](), [Website]() Tags: [analysis](), [anomaly-detection](), [article](), [behavior](), [data](), [grafana](), [metrics](), [monitoring](), [outlier-detection](), [sre](), [statistics](), [time-series](), [web]() ### AI overview This article explains how to build a statistical anomaly detection system for time series data. It describes why static thresholds and comparisons with the same point one week earlier can miss recurring or gradual problems, and introduces standard deviation as a foundational statistical measure. ### Source excerpt Setting up alerts for metrics isn't always straightforward. In some cases, a simple threshold works just fine -- for example, monitoring disk space on a device. You can just set an alert at 10% remaining, and you're covered. The same goes for tracking available memory on a server. But what if we need to monitor something like user behavior on a website? Imagine running a web store where you sell products. One approach might be to set a minimum threshold for daily sales and check it once a day. But what if something goes wrong, and you need to catch the issue much sooner -- within hours or even minutes? In that case, a static threshold won't cut it because user activity fluctuates throughout the day. This is where anomaly detection comes in. What exactly is anomaly detection? Instead of relying on simple rules, it involves analyzing historical data to spot unusual patterns. There are various ways to implement anomaly detection, including machine learning and statistical analysis. In this article, we'll focus on the statistical approach and walk through how we built our own anomaly detection system for time series data from scratch at Booking. The Naïve Approach One common mistake I've seen across different companies and teams is trying to detect anomalies by simply comparing a business metric to its value exactly one week ago. This week vs previous week At first glance, this approach isn't entirely useless -- you can catch some anomalies, as shown in the image above. But is it a reliable long-term solution? Not really. The big flaw is that today's anomaly becomes next week's baseline. That means if the same issue occurs again at the same time next week, it may go completely unnoticed because we're now comparing against a flawed reference point. Outage in previous week That doesn't look right, our simplistic approach doesn't know that last week's data was compromised. Another limitation of this method is that it only considers a single week at a time. But what if perform [Next page]()