# Time-series Published articles for Time-series. 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. ## Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more DevFeed: [Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more]() Original publisher: [Read original article]() Author: Bosmat Tuvel Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Redis](), [Data structures](), [Data Management](), [Open Source]() Tags: [data-management](), [improvements](), [memory](), [new-features](), [operations](), [performance](), [redis](), [streams](), [tech](), [time-series]() ### AI overview Redis 8.10 in Redis Open Source introduces compact hashes, incremental backup and restore, JSONPath syntax extensions, more flexible Stream consumption, new Set cardinality operations, atomic movement of multiple List elements, and enhanced Time Series capabilities. The release also improves memory efficiency, throughput, and operational reliability at scale. ### Source excerpt Redis 8.10 in Redis Open Source is now available, delivering improvements that make Redis more memory efficient, expressive, and easier to operate at scale. Highlights include compact hashes with up to 50% lower memory usage and 2x higher hash loadin... ## Kubernetes v1.37: Native Histograms Graduates to Beta DevFeed: [Kubernetes v1.37: Native Histograms Graduates to Beta]() Original publisher: [Read original article]() Author: Richa Banker Published: 2026-09-11T18:30:00Z Content type: release Language: en Sources: [Kubernetes Blog]() Topics: [telemetry]() Tags: [kubernetes](), [latency](), [metrics](), [observability](), [prometheus](), [storage](), [telemetry](), [time-series]() ### AI overview Kubernetes v1.37 enables Prometheus native histograms by default at Beta. The article explains how dynamic exponential buckets improve metric resolution and quantile accuracy while reducing time-series, scraping, and storage overhead compared with classic histograms. ### Source excerpt I'm excited to announce that native histogram support for Kubernetes metrics is graduating to Beta and is enabled by default in Kubernetes v1.37! Native histograms (previously introduced as Alpha in Kubernetes v1.36 under KEP-5808) bring high-resolution, low-cardinality observability to Kubernetes metrics. By adopting Prometheus Native Histograms, Kubernetes components now expose latency and duration metrics with far greater accuracy while significantly reducing telemetry storage and scraping overhead. Why move beyond classic histograms? Since the early days of Kubernetes observability, duration and latency metrics (such as API server request latencies or scheduling durations) have relied on classic Prometheus histograms. Classic histograms require metric authors to define a static list of cumulative bucket boundaries (le labels), such as 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10. While familiar, this approach introduces three major challenges: The Bucket Guessing Game: If a workload's latency profile changes, for example, shifting into microsecond ranges or experiencing long-tail tail latencies beyond the highest bucket, the histogram loses visibility. Specifying bucket boundaries upfront requires knowing the distribution before observing it High Cardinality & Storage Cost: With classic histograms, each bucket boundary is exported as a separate time series (_bucket{le="..."}). A histogram with 10 buckets across multiple labels multiplies the number of time series by 10, increasing memory consumption in Prometheus and inflating time series database (TSDB) storage costs Interpolation Error in Quantiles: Calculating percentiles using histogram_quantile() relies on linear interpolation between static bucket boundaries. When bucket spans are coarse, quantile calculations can suffer from significant estimation error What are Prometheus native histograms? Prometheus Native Histograms replace static user-defined buckets with dynamic, exponential buckets. Inst ## From zero-shot forecast to purchase order with Amazon Bedrock AgentCore DevFeed: [From zero-shot forecast to purchase order with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Hyunsoo Kim, Ph.D. Published: 2026-09-11T14:08:01Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [Training AI Models](), [SDKs]() Tags: [advanced-300](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [automation](), [best-practices](), [orchestration](), [strands-agents](), [time-series](), [training](), [zero-shot]() ### AI overview An architecture article on using Amazon Chronos2 zero-shot forecasting and Bedrock AgentCore multi-agent orchestration to turn demand forecasts into validated purchase orders without per-product model training. ### Source excerpt Combine zero-shot forecasting with Amazon Chronos2 and multi-agent orchestration on Amazon Bedrock AgentCore to turn demand forecasts into validated purchase orders. No per-product model training, with business rules, auditability, and cost that scales to zero. ## IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license DevFeed: [IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license]() Original publisher: [Read original article]() Author: Roman Vaculin; Wesley M Gifford; Jiri Navratil; Chandra Reddy; Ayhan Sebin Published: 2026-09-09T15:36:24Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [foundation-models](), [Temporal data](), [releases](), [benchmarking](), [Open Source Models & Datasets](), [Inference]() Tags: [apache](), [benchmarking](), [foundation-models](), [ibm](), [inference](), [model-architecture](), [open-source](), [releases](), [time-series](), [zero-shot]() ### AI overview IBM released Granite Time Series PatchTST-FM-r2, a roughly 385M-parameter time-series foundation model for zero-shot forecasting. The article covers its architecture, probabilistic forecasting, missing-value imputation, benchmark results, licensing, and available reproducibility resources. ### Source excerpt Time-series foundation models are changing the way forecasting systems are built. Instead of training and maintaining a separate model for every dataset, users can use a pretrained model and generate forecasts zero-shot. IBM has released Granite Time Series PatchTST-FM-r2, the latest model in the Granite TSFM family (github, blog). ## 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 ## 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 ## Forking-Sequences -- Part II: Multi-Horizon Forecast Ensembling with Reduced Volatility DevFeed: [Forking-Sequences -- Part II: Multi-Horizon Forecast Ensembling with Reduced Volatility]() Original publisher: [Read original article]() Author: Willa Potosnak Published: 2026-08-10T21:52:23Z Content type: article Language: en Sources: [ML@CMU]() Topics: [Sequences](), [Time Series](), [foundation-models](), [Inference](), [Machine Learning & Artificial Intelligence]() Tags: [ai](), [artificial-intelligence](), [automated-machine-learning](), [big-data](), [computer-science](), [data-science](), [deep-learning](), [ethics-and-ai](), [forecasting](), [foundation-models](), [inference](), [machine-learning](), [ml](), [multi-horizon-forecasting](), [natural-language-processing](), [neural-networks](), [paper](), [research](), [sequences](), [time-series]() ### AI overview The article describes forecast ensembling for multi-horizon time-series models. Exponential smoothing reduces forecast volatility by about 10-13% with less than 0.1% accuracy degradation, including in zero-shot use with pretrained time-series foundation models. ### Source excerpt Based on: Potosnak, W., Wolff, M., Cao, M., Ma, R., Konstantinova, T., Efimov, D., Mahoney, M.W., Oreshkin, B., & Olivares, K.G. "Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility." Transactions on Machine Learning Research, 2026. (Disclaimer: Code implementation not used in the paper; not affiliated with Amazon -- provided as a reference for forking-sequences and forecast ensembling) TL;DR Ensembling, nearly for free. Forking-sequences already produces overlapping forecasts for every target date across FCDs in a single forward pass, so ensembling them at inference adds no extra encoder computation compared with window-sampling. Two new forecast volatility metrics. scaled Forecast Percentage Change (sFPC) measures raw revision size in real time (no ground truth needed); Excess Volatility (EV) goes further, rewarding accuracy-improving revisions and only penalizing the ones that move forecasts away from the truth or overshoot it. Reduced volatility without sacrificing accuracy. Exponential-smoothing forecast ensembling (α = 0.9) reduces sEV by 10-13% across all encoder types, with less than 0.1% accuracy degradation. Works zero-shot on models pretrained with window-sampling. Forecast ensembling applied to pretrained Time Series Foundation Models (TSFMs) -- Chronos-2, Toto 2.0, TimesFM, PatchTST, N-BEATS -- cuts volatility by ~10% with negligible accuracy cost (less than 0.1%). In Part [...] ## 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. ## 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. ## Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery DevFeed: [Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery]() Original publisher: [Read original article]() Published: 2026-05-19T17:52:00Z Content type: news Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [coding](), [Google](), [Benchmark](), [Data Science](), [Software](), [Time Series](), [Mathematics]() Tags: [ai](), [benchmark](), [benchmarks](), [code](), [coding](), [drive](), [general-science](), [google](), [machine-intelligence](), [natural-language-processing](), [open-source-models-datasets](), [time-series]() ### AI overview Google Research introduces Empirical Research Assistance (ERA), an AI tool that uses Gemini to write and optimize scientific code. ERA searches literature, explores solutions, evaluates results, and uses tree search to optimize code for computational experiments. Tests across genomics, public health, satellite imagery, neuroscience, time-series forecasting, and mathematics show expert-level benchmark performance. ERA also helped build the Computational Discovery prototype, which is being made available through Google Labs and Gemini for Science. ### Source excerpt General Science ## 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 2: Cardinality in ClickHouse DevFeed: [ClickHouse vs Prometheus for High Cardinality, Part 2: Cardinality in ClickHouse]() Original publisher: [Read original article]() Author: Rory Crispin; Dale McDiarmid Published: 2026-05-15T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Prometheus](), [Databases](), [telemetry](), [Temporal data](), [data](), [systems]() Tags: [clickhouse](), [data](), [databases](), [logs](), [metrics](), [observability](), [prometheus](), [systems](), [telemetry](), [time-series]() ### AI overview This article examines how ClickHouse and other column-oriented databases handle high-cardinality observability data differently from Prometheus and other series-oriented time-series databases. It explains that ClickHouse shifts much of the cost from ingestion to query time and models telemetry as wide, timestamped events with dimensions, attributes, and measurements that can be aggregated later. The article also notes that ClickHouse is not a drop-in replacement for Prometheus and requires a different approach to telemetry, instrumentation, and aggregation. ### Source excerpt In this blog, we explore why high cardinality behaves fundamentally differently in column-oriented databases like ClickHouse, and why the costs appear in very different places than in traditional series-based systems like Prometheus. ## 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." ## ClickHouse achieves AWS Financial Services Competency DevFeed: [ClickHouse achieves AWS Financial Services Competency]() Original publisher: [Read original article]() Author: Aditya Chidurala Published: 2025-12-12T00:00:00Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [data](), [Temporal data](), [Security, Privacy and Abuse Prevention](), [AI, ML & Data Engineering]() Tags: [analytics](), [aws](), [clickhouse](), [data-analytics](), [financial-services](), [fraud](), [machine-learning](), [partners](), [real-time](), [scalability](), [time-series]() ### AI overview ClickHouse announces its AWS Financial Services Competency and describes how financial organizations use its analytics platform for high-volume, low-latency workloads. ### Source excerpt ClickHouse has achieved AWS Financial Services Competency, joining a select group of AWS Partners recognized for deep expertise in serving financial services organizations. ## Introducing Analytics Buckets DevFeed: [Introducing Analytics Buckets]() Original publisher: [Read original article]() Author: Fabrizio Fenoglio Published: 2025-12-02T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [Apache Iceberg](), [Amazon S3]() Tags: [amazon-s3](), [analytics](), [apache-iceberg](), [parquet](), [postgres](), [s3](), [scale](), [schema-evolution](), [storage](), [time-series]() ### AI overview Supabase introduces Analytics Buckets for storing large analytical datasets in Supabase Storage. The service uses Apache Iceberg, Amazon S3, and columnar Parquet files, while Postgres remains suited to transactional application data. ### Source excerpt Use Analytics Buckets to store huge datasets in Supabase Storage with Apache Iceberg and columnar Parquet format, optimized for analytical workloads. ## 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. [Next page]()