# Temporal data Data whose values, availability, or validity depend on time, commonly represented as time series, event sequences, or timestamped records. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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). ## 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 ## Postgres 19: How Our Advice Has Changed Since We Wrote It DevFeed: [Postgres 19: How Our Advice Has Changed Since We Wrote It]() Original publisher: [Read original article]() Author: Christopher Winslett Published: 2026-08-18T19:00:00Z Content type: article Language: en Sources: [CrunchyData Blog]() Topics: [data](), [JIT](), [Latency](), [Temporal data](), [Linux]() Tags: [advice](), [async](), [benchmarks](), [index](), [jit](), [latency](), [linux](), [maintenance](), [parallel](), [partitioning](), [postgres](), [postgres-19](), [production-postgres](), [release](), [storage](), [upgrade]() ### AI overview This article revisits earlier Crunchy Data guidance on loading, storage, indexes, and partitioning for the upcoming Postgres 19 release. It explains which changes in Postgres 18 and 19 affect that advice, including asynchronous I/O, parallel maintenance, BRIN and skip-scan behavior, partition operations, and JIT being disabled by default. The details are based on current betas and may change before general availability. ### Source excerpt Revisiting Crunchy posts on COPY, TOAST, BRIN, covering indexes, and partitioning: what we said then, which Postgres versions changed the story, and what we recommend on Postgres 19. ## From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking DevFeed: [From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking]() Original publisher: [Read original article]() Author: Steven De Gryze; Parshva Doshi; Sean O'Byrne; Arnold Overwijk; Dinesh Ramasamy; Lee Xiong Published: 2026-08-05T19:20:20Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta ML Applications]() Topics: [recommendation systems](), [Temporal data]() Tags: [ads](), [architecture](), [data-infrastructure](), [meta](), [ml-applications](), [production-engineering](), [recommendation-systems](), [scaling-laws](), [tokenization]() ### AI overview Meta describes a multi-stage sequence-model architecture for ads ranking that separates offline user modeling from lightweight online ranking. It also uses dense tokenization and target-aware attention to learn feature interactions, with reported conversion and ad-click lifts across Instagram and Facebook. ### Source excerpt Every day, Meta's recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...] Read More... The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking appeared first on Engineering at Meta. ## Personalizing Airbnb search by learning from the guest journey DevFeed: [Personalizing Airbnb search by learning from the guest journey]() Original publisher: [Read original article]() Author: Daochen Zha Published: 2026-07-21T17:01:04Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Transformer](), [recommendation systems](), [Temporal data](), [Data Science]() Tags: [ai](), [blog](), [conversion](), [engineering](), [machine-learning](), [model](), [recommendation-system](), [research](), [scale](), [search](), [technology](), [training]() ### AI overview Airbnb describes a Transformer-based sequence model for personalizing search by encoding years of guest behavior, including listing views, bookings, reviews, and cancellations. The system learns richer representations of guest preferences to improve listing relevance and booking conversion while addressing very long, noisy event sequences and the cost of training on hundreds of millions of search-label pairs. ### Source excerpt How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time. By: Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Jun Shi, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Michael Kinoti, Stephanie Moyerman, and Sanjeev Katariya Introduction Planning a trip on Airbnb rarely happens in a single session. A guest searching for a place to stay in San Francisco might browse dozens of listings over several days, leaving behind a trail of views. Typically, over a period of years, that same guest will have accumulated many previous bookings, reviews, and the occasional cancellation. Taken together, these events reveal a great deal about what that guest values in a stay. For years, Airbnb's search ranking captured this through hand-crafted features: aggregated statistics such as total past bookings or average listing price. These worked well, but as the feature count grew into the hundreds, the approach became harder to scale and increasingly limited in expressiveness. In this blog post, we describe how we built a sequence modeling system that encodes the full guest journey using a Transformer, learning richer representations of guest preferences to deliver more personalized search results. An example of a guest journey, which is typically long, exploratory, and complex.Challenges Event sequences per guest present three core challenges. First, they are dominated by listing views, which account for the vast majority of all events -- some guests accumulate hundreds of thousands of them -- making raw sequences computationally intractable to model directly. The distribution of event types, with the majority being listing views. Second, unlike social media platforms, which optimize for engagement, Airbnb optimizes for booking conversion. Bookings are rare, compared to events, and deliberate, whereas a listing view could reflect genuine intent or simply idle browsing. Building a model that generalizes ## 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. ## 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. ## Using group theory to explore the space of positional encodings for attention DevFeed: [Using group theory to explore the space of positional encodings for attention]() Original publisher: [Read original article]() Author: Alok Puranik Published: 2026-04-22T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [Language models](), [Machine learning](), [Temporal data]() Tags: [analysis](), [blog-post](), [language-models](), [machine-learning](), [ml]() ### AI overview The article examines positional encodings for attention using group theory. It argues that formalizing desirable properties leaves only a few valid families, most of which are already used in real systems, while also identifying a technically valid but apparently unexplored class. ### Source excerpt Attention is a computational primitive at the core of modern language models, allowing internal representations to reference and influence each other. It's how these models handle sequential data in the first place. ## The generative recommender behind Shopify's commerce engine DevFeed: [The generative recommender behind Shopify's commerce engine]() Original publisher: [Read original article]() Author: Yang Liu Published: 2026-02-25T16:04:54Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [Temporal data](), [model-deployment](), [real-time](), [Feature Engineering]() Tags: [architecture](), [engineering](), [feature-engineering](), [generative](), [latency](), [model](), [production](), [real-time](), [scale](), [shopify](), [train](), [training]() ### AI overview Shopify describes a generative recommender that treats buyer journeys as raw event sequences. An autoregressive, causally masked model predicts next products or ads while meeting real-time production constraints at Shopify's scale. ### Source excerpt Treating buyer journeys as sequences instead of simplified signals, and building a model fast enough to serve at scale. ## 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. ## Building Real-Time AI: Highlights from the AWS MCP Hackathon in San Francisco DevFeed: [Building Real-Time AI: Highlights from the AWS MCP Hackathon in San Francisco]() Original publisher: [Read original article]() Author: Zoe Steinkamp Published: 2025-11-20T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI Bots](), [Kafka](), [incident](), [Orchestration](), [Temporal data]() Tags: [agents](), [ai](), [analytics](), [aws](), [bedrock](), [clickhouse](), [hackathon](), [incident](), [kafka](), [latency](), [mcp](), [monitoring](), [observability](), [orchestration](), [real-time](), [streaming](), [time-series]() ### AI overview A ClickHouse recap of three AWS MCP Hackathon projects that use live and streaming data for ad bidding, incident response, and glucose monitoring. ### Source excerpt How teams used ClickHouse to power agents with streaming data, sub-second analytics, and production-ready dashboards at the AWS MCP Hackathon. ## Calendar and Time-Zone Complexities in Software DevFeed: [Calendar and Time-Zone Complexities in Software]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2024-12-20T14:20:00Z Content type: opinion Language: en Sources: [Vanilla Java]() Topics: [Temporal data](), [Data analysis](), [Time Series]() Tags: [exercise](), [info](), [pitfalls](), [time](), [time-series]() ### AI overview This commentary explains why date and time handling is complex in software. It discusses time zones, calendar systems, cultural conventions, and historical changes that can cause incorrect date arithmetic and other defects. ### Source excerpt I find time fascinating and surprisingly complex. Time zones and calendars change from place to place over time. There are a number of interesting websites on the subject. Time is one of those concepts that appears deceptively simple on the surface yet becomes increasingly intricate the more we examine it. As software developers, we often face scenarios where we must handle dates and times and their myriad associated rules--time zones, calendar systems, cultural conventions, and historical irregularities. Working with time can lead us into subtle pitfalls that affect everything from straightforward user interfaces to global financial systems. Time is an illusion. Lunchtime doubly so. -- Douglas Adams The Hitchhiker's Guide to the Galaxy The Surprising Complexity Behind Time Time is not uniform. Humans have invented calendar systems and measurement techniques, each influenced by politics, religion, and culture. As a result, how we record and interpret dates has repeatedly changed over the centuries. These shifts mean that historical dates do not map cleanly onto modern calendars. The Julian and Gregorian calendars, the French Revolutionary calendar, and the attempts by certain countries to gradually or abruptly adjust to the Gregorian standard all introduce tricky discontinuities. We also have complex local customs, such as the Korean age-counting system, or unique historical anomalies like Sweden's February 30th in 1712. Understanding these anomalies is crucial when dealing with historical data sets, genealogical records, financial time series that stretch back centuries, or any domain that involves retrospective data analysis. From a technical perspective, these complexities translate into potential defects. Off-by-one errors, incorrect conversions, or failure to account for historical shifts can lead to incorrect date arithmetic. Even if you never work with centuries-old data, these quirks are cautionary tales: time is never as straightforward as it first appears. H ## How Supergood unlocked their Postgres developer productivity DevFeed: [How Supergood unlocked their Postgres developer productivity]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2024-02-20T18:01:16Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Database](), [Temporal data](), [telemetry]() Tags: [api](), [case-studies](), [database](), [developer-productivity](), [observability](), [postgres](), [redis](), [time-series]() ### AI overview A case study of Supergood's use of Neon and database branching to improve Postgres development productivity while processing API metrics and time-series data. ### Source excerpt "Neon allows us to develop much faster than we've even been used to. In traditional setups, you're just trying to get the data that is representative of real customers--but now, when we build a feature, we're actually testing it on real data in a matter of minutes. We can get feat... ## Real-world Insights: Anomaly Detection in Internet Traffic DevFeed: [Real-world Insights: Anomaly Detection in Internet Traffic]() Original publisher: [Read original article]() Author: Peter Brejcak Senior Data Scientist Published: 2024-02-13T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Internet Traffic](), [Temporal data](), [Data Science]() Tags: [analytics](), [anomaly-detection](), [data-science](), [internet-traffic](), [monitoring](), [quality](), [time-series](), [user-experience]() ### AI overview This article explains how trivago approaches anomaly detection in partner-level internet traffic. It focuses on practical business implementation, emphasizing that abrupt changes in time series may result from dynamic input parameters and expected traffic shifts rather than genuine anomalies. ### Source excerpt Anomaly detection for time series is like finding unusual events in a sequence of data over time. It helps identify outliers or deviations from the expected pattern, signaling potential issues or anomalies in the dataset. This is the theory, but how does it translate into practical implementation for real business needs? ## Pre-processing temporal data made easier with TensorFlow Decision Forests and Temporian DevFeed: [Pre-processing temporal data made easier with TensorFlow Decision Forests and Temporian]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-09-11T20:14:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [Temporal data](), [Temporian](), [TensorFlow Decision Forests](), [Feature Engineering](), [Python](), [datasets]() Tags: [blog](), [data](), [dataset](), [feature-engineering](), [logs](), [machine-learning](), [python](), [sales](), [temporal-data](), [temporian](), [tensorflow-decision-forests](), [time-sequences](), [time-series](), [transactions](), [user-event]() ### AI overview This article demonstrates preprocessing temporal sales data with Temporian and forecasting weekly sales with TensorFlow Decision Forests. It explains event sets, aggregation, moving sums, indexing, and the trade-off between transactional detail and uniformly sampled time series. ### Source excerpt Posted by Google: Mathieu Guillame-Bert, Richard Stotz, Robert Crowe, Luiz GUStavo Martins (Gus), Ashley Oldacre, Kris Tonthat, Glenn Cameron, and Tryolabs: Ian Spektor, Braulio Rios, Guillermo Etchebarne, Diego Marvid, Lucas Micol, Gonzalo Marín, Alan Descoins, Agustina Pizarro, Lucía Aguilar, Martin Alcala Rubi Temporal data is omnipresent in applied machine learning applications. Data often changes over time or is only available or valuable at a certain point in time. For example, market prices and weather conditions change constantly. Temporal data is also often highly discriminative in decision-making tasks. For example, the rate of change and interval between two consecutive heartbeats provides valuable insights into a person's physical health, and temporal patterns of network logs are used to detect configuration issues and intrusions. Hence, it is essential to incorporate temporal data and temporal information in ML applications. INFO: Temporian is a new open-source Python library for preprocessing and feature engineering temporal data for machine learning applications. It is developed in collaboration between Google and Tryolabs. Check the sister blog post for more details. This blog post demonstrates how to train a forecasting model on transactional data. Specifically, we will show how to forecast the total weekly sales from individual sales records. For the modeling part, we will use TensorFlow Decision Forests as they are well suited to handle temporal data. To feed the transaction data to our model, and to compute temporal specific features, we will use Temporian, a newly released library designed for ingesting and aggregating transactional data from multiple non-synchronized sources. Time series are the most commonly used representation for temporal data. They consist of uniformly sampled values, which can be useful for representing aggregate signals. However, time series are sometimes not sufficient to represent the richness of available data. Instead ## Announcing OpenTSDB 2.4.0: Rollup and Pre-Aggregation Storage, Histograms, Sketches, and More DevFeed: [Announcing OpenTSDB 2.4.0: Rollup and Pre-Aggregation Storage, Histograms, Sketches, and More]() Original publisher: [Read original article]() Author: amberwilsonla-blog Published: 2018-12-27T17:01:17Z Content type: release Language: en Sources: [Yahoo]() Topics: [Databases](), [Time Series](), [Hadoop](), [Monitoring](), [Data analysis](), [Open Source](), [Temporal data]() Tags: [data-analysis](), [databases](), [hadoop](), [monitoring](), [open-source](), [release](), [salesforce](), [time-series](), [workflows](), [yahoo-engineering]() ### AI overview OpenTSDB 2.4.0 is released with rollup and pre-aggregation storage for time series data, along with histograms and sketches. The release focuses on retaining lower-resolution data for longer periods, reducing query workloads, and supporting more accurate percentile analysis across multiple series. ### Source excerpt yahoodevelopers: By Chris Larsen, Architect OpenTSDB is one of the first dedicated open source time series databases built on top of Apache HBase and the Hadoop Distributed File System. Today, we are proud to share that version 2.4.0 is now available and has many new features developed in-house and with contributions from the open source community. This release would not have been possible without support from our monitoring team, the Hadoop and HBase developers, as well as contributors from other companies like Salesforce, Alibaba, JD.com, Arista and more. Thank you to everyone who contributed to this release! A few of the exciting new features include: Rollup and Pre-Aggregation Storage As time series data grows, storing the original measurements becomes expensive. Particularly in the case of monitoring workflows, users rarely care about last years' high fidelity data. It's more efficient to store lower resolution "rollups" for longer periods, discarding the original high-resolution data. OpenTSDB now supports storing and querying such data so that the raw data can expire from HBase or Bigtable, and the rollups can stick around longer. Querying for long time ranges will read from the lower resolution data, fetching fewer data points and speeding up queries. Likewise, when a user wants to query tens of thousands of time series grouped by, for example, data centers, the TSD will have to fetch and process a significant amount of data, making queries painfully slow. To improve query speed, pre-aggregated data can be stored and queried to fetch much less data at query time, while still retaining the raw data. We have an Apache Storm pipeline that computes these rollups and pre-aggregates, and we intend to open source that code in 2019. For more details, please visit http://opentsdb.net/docs/build/html/user_guide/rollups.html. Histograms and Sketches When monitoring or performing data analysis, users often like to explore percentiles of their measurements, such as the 9 ## Redis on Raspberry Pi for Embedded and IoT Applications DevFeed: [Redis on Raspberry Pi for Embedded and IoT Applications]() Original publisher: [Read original article]() Published: 2017-02-24T09:52:30Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Raspberry Pi](), [Redis](), [Internet of things](), [Arm](), [Linux](), [Temporal data](), [Data structures](), [Debian]() Tags: [arm](), [data-type](), [debian](), [iot](), [linux](), [raspberry-pi](), [redis](), [streams](), [time-series]() ### AI overview The article discusses adapting Redis to run on Raspberry Pi and its potential use in embedded and IoT devices. It highlights Redis Streams for storing sensor data and time series, along with Redis's small memory footprint and suitability for small ARM devices. ### Source excerpt After 10 million of units sold, and practically an endless set of different applications and auxiliary devices, like sensors and displays, I think it's deserved to say that the Raspberry Pi is not just a success, it also became one of the preferred platforms for programmers to experiment in the embedded space. Probably with things like the Pi zero, it is also becoming the platform in order to create hardware products, without incurring all the risks and costs of designing, building, and writing software for vertical devices. Well, I love to think that also Redis is a platform that programmers like to use when to hack, experiment, build new things. Moreover devices that can be used for embedded / IoT applications, often have the problem of temporarily or permanently storing data, for example received by sensors, on the device, to perform on-device computations or to send them to remote servers. Redis is adding a "Stream" data type that is specifically suited for streams of data and time series storage, at this point the specification is near complete and work to implement it will start in the next weeks. Redis existing data structures, and the new streams, together with the small memory footprint, the decent performances it can provide even while running on small hardware (and resulting low energy usage), looked like a good match for Raspberry Pi potential applications, and in general for small ARM devices. The missing piece was the obvious one: to run well on the Pi. One of the many cool things about the Pi is that its development environment does not look like the embedded development environments of a few years ago... It just runs Linux, with all the Debian-alike tooling you expect to find. Basically adapting Redis to work on the Pi was not a huge task. The most fundamental mismatch a Linux system program and the Pi could have, is a performance / footprint mismatch, but this a non issue because of the Redis design itself: an empty instance consumes a total of 1MB of ## Apache Cassandra in a Microservices Enterprise Platform DevFeed: [Apache Cassandra in a Microservices Enterprise Platform]() Original publisher: [Read original article]() Author: mick Published: 2015-04-28T14:00:00Z Content type: article Language: en Sources: [Finn.no]() Topics: [Apache Cassandra](), [Microservice](), [Architecture & Design](), [Temporal data]() Tags: [apis](), [architecture](), [cassandra](), [coupling](), [enterprise](), [event-driven](), [microservices](), [rest](), [time-series]() ### AI overview The article discusses Apache Cassandra as a persistence layer for microservices platforms, particularly for systems that need scalability and time-series data models. It also outlines microservices practices involving isolated runtime APIs, reduced coupling, REST, and producer-defined schemas for event-driven designs. ### Source excerpt In this article we'll explore how Apache Cassandra, the world's most popular wide column store and 8th most popular database overall, will only grow as a cornerstone technology in a microservices platform. With a little theory to microservices, to some examples of microservices and underlying required infrastructure, we'll show that any solution both capable of scaling and dealing with time-series data-models is going to need to depend upon Apache Cassandra as a persistence layer, despite having a polyglot persistence model at large. microservices Microservices is a term that's come out of ThoughtWorks' Martin Fowler and James Lewis. It's a bit of a buzzword, basically a fresh revival of the parts of service orientated architecture that you should be focusing on and getting right. A lot of it hopefully is obvious to you already. If you've been doing service orientated architecture or even generally just unix programming properly over the years it might well be frustrating just how buzz "microservices" has become. But it's worth keeping in mind how much garbage we've collected and how many aspects of service orientated architecture that we've gotten badly wrong over the years. Younger programmers certainly deserve the clarity that ThoughtWorks is giving us here. Microservices, following the tips and guidelines from Sam Newman, can basically be broken down into four groups. interfaces Ensure that you standardise the systems architecture at large and especially the gaps or what we know as the APIs between services. Standardise upon practices and protocols that minimise coupling. Move from tightly coupled systems with many compile time dependencies and distributed published client libraries, to clearly defined and isolated runtime APIs. Take advantage of REST, especially level 3 in richardson's maturity model, for the synchronous domain driven designed parts of your system. When it comes to event driven design use producer defined schemas, like that offered by Apache Th ## Real-Time Counts with Stitch DevFeed: [Real-Time Counts with Stitch]() Original publisher: [Read original article]() Published: 2014-07-03T00:00:00Z Content type: article Language: en Sources: [SoundCloud Backstage Blog]() Topics: [Temporal data](), [web applications](), [incident]() Tags: [api](), [architecture](), [batch](), [cassandra](), [data](), [hadoop](), [race-condition](), [real-time](), [time-series]() ### AI overview SoundCloud describes Stitch, a Cassandra-backed system for real-time count time series. It combines stream processing with batch MapReduce/Hadoop processing and evolves toward a Lambda Architecture-style design to address non-idempotent counter updates. ### Source excerpt Here at SoundCloud, in order to provide counts and a time series of counts in real time, we created something called Stitch. Stitch was... ## MySQL for Statistics - Old Faithful DevFeed: [MySQL for Statistics - Old Faithful]() Original publisher: [Read original article]() Published: 2011-07-05T00:00:00Z Content type: article Language: en Sources: [SoundCloud Backstage Blog]() Topics: [data](), [Temporal data]() Tags: [data](), [database](), [events](), [mysql](), [persistence](), [statistics](), [storage](), [time-series]() ### AI overview The article describes using MySQL with InnoDB for statistics persistence after MongoDB disk thrashing. It examines read-optimized storage, indexed play logs, aggregation requirements, time ranges, and query I/O behavior at multi-billion-row scale. ### Source excerpt MySQL turns out to be a good Swiss Army Knife for persistence, if used wisely. Understanding disk access patterns driven by your storage...