# Scalable Analytics Architecture Published articles for Scalable Analytics Architecture. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why we moved our growth analytics back into Tinybird DevFeed: [Why we moved our growth analytics back into Tinybird]() Original publisher: [Read original article]() Author: Pablo Abella Published: 2026-06-01T10:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [data]() Tags: [ai-x-data](), [analytics](), [analytics-stack](), [data](), [growth](), [scalable-analytics-architecture]() ### AI overview Tinybird describes Birdwatcher, its internal growth analytics stack, and explains why it moved more of its analytics workflow onto its own data and platform. ### Source excerpt The story of Birdwatcher, Tinybird's internal growth analytics stack, and why we moved more of our analytics loop onto our own data. ## How We Built Tinybird Wrapped: A Year in Data DevFeed: [How We Built Tinybird Wrapped: A Year in Data]() Original publisher: [Read original article]() Author: Alberto Romeu, Pablo Abella Published: 2026-01-15T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [data]() Tags: [customer](), [data](), [feature](), [learn](), [metrics](), [scalable-analytics-architecture](), [time]() ### AI overview This article explains how Tinybird built Tinybird Wrapped, a yearly recap feature that aggregates and visualizes customer usage metrics across regions, time periods, and data sources. ### Source excerpt Learn how we built Tinybird Wrapped, a yearly recap feature that aggregates and visualizes customer usage metrics across multiple regions, time periods, and data sources using... Tinybird! ## Best Practices to Backfill Materialized Views in ClickHouse® Safely DevFeed: [Best Practices to Backfill Materialized Views in ClickHouse® Safely]() Original publisher: [Read original article]() Author: Jesús Botella Published: 2026-01-09T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [clickhouse]() Tags: [best-practices](), [clickhouse](), [how-to](), [operational](), [reduce](), [scalable-analytics-architecture]() ### AI overview This article explains how to safely backfill materialized views in ClickHouse and discusses how Tinybird can reduce the operational burden. ### Source excerpt Backfilling materialized views in ClickHouse®, how to do it safely and how Tinybird helps to reduce operational burden. ## ClickHouse® Kafka Engine vs Tinybird Kafka Connector DevFeed: [ClickHouse® Kafka Engine vs Tinybird Kafka Connector]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-12-31T00:00:00Z Content type: comparison Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Kafka]() Tags: [clickhouse](), [compare](), [kafka](), [oss](), [pipeline](), [scalable-analytics-architecture]() ### AI overview A comparison of the ClickHouse OSS Kafka Engine and Tinybird's Kafka connector, covering tradeoffs, failure modes, and when to choose each solution for a Kafka-to-ClickHouse pipeline. ### Source excerpt Compare ClickHouse OSS Kafka Engine and Tinybird's Kafka connector. Understand tradeoffs, failure modes and when to choose each solution for your Kafka to ClickHouse pipeline. ## How to Fix Kafka to ClickHouse® Performance Bottlenecks DevFeed: [How to Fix Kafka to ClickHouse® Performance Bottlenecks]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-12-30T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Kafka](), [Optimization](), [Structured-data]() Tags: [best-practices](), [clickhouse](), [distribution](), [how-to](), [kafka](), [optimization](), [partition](), [performance](), [pipeline](), [scalable-analytics-architecture](), [schema](), [view]() ### AI overview A tutorial on optimizing performance in Kafka-to-ClickHouse pipelines through schema optimization, Materialized View tuning, partition distribution strategies, and throughput practices. ### Source excerpt Learn how to optimize your Kafka to ClickHouse pipeline performance with schema optimization, Materialized View tuning, partition distribution strategies and throughput best practices. ## Why Kafka pipelines fail (and how to fix them) DevFeed: [Why Kafka pipelines fail (and how to fix them)]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-12-23T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Kafka](), [Streaming](), [data]() Tags: [data](), [how-to](), [kafka](), [mistakes](), [pipelines](), [scalable-analytics-architecture](), [streaming]() ### AI overview The article discusses predictable mistakes that cause Kafka pipelines to fail and ways to prevent them before they occur. ### Source excerpt Kafka pipelines fail in predictable ways. These are the mistakes that break streaming data and how to prevent them before they happen. ## Build a Real-Time E-Commerce Analytics API from Kafka - II DevFeed: [Build a Real-Time E-Commerce Analytics API from Kafka - II]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-12-17T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [API](), [Kafka](), [real-time](), [dashboards]() Tags: [analytics](), [api](), [build](), [dashboards](), [kafka](), [real-time](), [scalable-analytics-architecture]() ### AI overview A tutorial on building a real-time ecommerce analytics API with Kafka, focusing on adding dashboards, alerts, and user-facing metrics to an online store. ### Source excerpt Build a real-time ecommerce analytics API with Kafka part 2: add dashboards, alerts, and user-facing metrics to your store. ## Tutorial: Build a Real-Time E-Commerce Analytics API with Kafka DevFeed: [Tutorial: Build a Real-Time E-Commerce Analytics API with Kafka]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-12-17T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Kafka](), [API](), [real-time](), [Tutorial]() Tags: [analytics](), [api](), [kafka](), [real-time](), [scalable-analytics-architecture](), [tutorial]() ### AI overview A tutorial covering the architecture for building a real-time ecommerce analytics API with Kafka. ### Source excerpt Build a real-time ecommerce analytics API with Kafka in hours, not weeks. This tutorial covers the complete architecture. ## Why Flink may be unnecessarily complex for most streaming data processing users DevFeed: [Why Flink may be unnecessarily complex for most streaming data processing users]() Original publisher: [Read original article]() Author: Javi Santana Published: 2025-10-21T00:00:00Z Content type: opinion Language: en Sources: [Tinybird]() Topics: [streaming-data-processing](), [Streaming]() Tags: [flink](), [scalable-analytics-architecture](), [streaming](), [streaming-data-processing]() ### AI overview The article argues that Flink's complexity may make it unnecessary for most people who need streaming data processing. ### Source excerpt Flink might sound like the holy grail of streaming data processing, but for 95% of us, it's just a complex headache we don't need. ## CI/CD with Tinybird Forward: automating real-time deployments DevFeed: [CI/CD with Tinybird Forward: automating real-time deployments]() Original publisher: [Read original article]() Author: Iago Enríquez Published: 2025-07-11T10:01:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [CI/CD](), [real-time]() Tags: [ci-cd](), [how-to](), [real-time](), [scalable-analytics-architecture](), [workflow]() ### AI overview An example based on the Electric Project demonstrates how to create a CI/CD workflow using Tinybird Forward for real-time data deployments. ### Source excerpt An example based on the Electric Project created in a previous post to show how to create a CI/CD workflow using Tinybird Forward. ## Scaling ClickHouse® ingestion with multi-writer architecture DevFeed: [Scaling ClickHouse® ingestion with multi-writer architecture]() Original publisher: [Read original article]() Author: Jordi Orihuela Published: 2025-06-27T10:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [clickhouse](), [real-time](), [data]() Tags: [architectures](), [clickhouse](), [data](), [real-time](), [scalable-analytics-architecture]() ### AI overview The article presents a multi-writer architecture for scaling real-time ClickHouse ingestion and claims that the design prevents data loss caused by conflicts between writers. ### Source excerpt Scaling real-time ingestion with multiple writers creates conflicts most architectures can't handle. This design prevents data loss. ## Cut AWS costs 40% while scaling faster with EKS, Karpenter, and Spot Instances DevFeed: [Cut AWS costs 40% while scaling faster with EKS, Karpenter, and Spot Instances]() Original publisher: [Read original article]() Author: Luis Rodríguez Published: 2025-06-27T10:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [Amazon EKS](), [Amazon Web Services]() Tags: [aws](), [scalable-analytics-architecture]() ### AI overview The article explains changes that cut AWS costs by 40% while improving scaling speed with EKS, Karpenter, and Spot Instances. ### Source excerpt We cut AWS costs 40% while scaling faster using EKS, Karpenter, and Spot Instances. Here's exactly what we changed. ## How Tinybird Uses KEDA and Real-Time Analytics to Autoscale Kafka Workloads DevFeed: [How Tinybird Uses KEDA and Real-Time Analytics to Autoscale Kafka Workloads]() Original publisher: [Read original article]() Author: Víctor M. Fernández Published: 2025-06-27T10:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [autoscaling](), [Kafka](), [real-time]() Tags: [autoscaling](), [kafka](), [real-time](), [scalable-analytics-architecture]() ### AI overview Tinybird describes using KEDA and its real-time analytics platform to autoscale Kafka workloads. ### Source excerpt Tinybird uses KEDA and its own real-time analytics platform to autoscale Kafka workloads. Learn how we made it work. ## How we automatically handle ClickHouse® schema migrations DevFeed: [How we automatically handle ClickHouse® schema migrations]() Original publisher: [Read original article]() Author: Raquel Barbadillo Published: 2025-06-27T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Structured-data](), [data]() Tags: [clickhouse](), [data](), [data-lineage](), [data-migrations](), [migrations](), [scalable-analytics-architecture](), [schema]() ### AI overview The article explains how data lineage is used to optimize ClickHouse table deployments and avoid unnecessary, expensive data migrations. ### Source excerpt How we use data lineage to optimize ClickHouse® table deployments and avoid unnecessary and expensive data migrations. ## Optimizing Apache Iceberg tables for real-time analytics DevFeed: [Optimizing Apache Iceberg tables for real-time analytics]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-06-03T10:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Apache Iceberg](), [Apache Iceberg tables](), [real-time](), [Sorting](), [systems]() Tags: [apache-iceberg](), [apache-iceberg-tables](), [high-performance](), [how-to](), [partitioning](), [performance](), [real-time](), [scalable-analytics-architecture](), [sorting](), [systems]() ### AI overview A tutorial on using Apache Iceberg partitioning, sorting, and compaction features to build high-performance real-time analytics systems. ### Source excerpt Learn how to use Iceberg's partitioning, sorting, and compaction features to build high-performance real-time analytics systems ## Building real-time analytics with Redpanda, Iceberg, and Tinybird DevFeed: [Building real-time analytics with Redpanda, Iceberg, and Tinybird]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-05-23T10:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Apache Iceberg](), [real-time]() Tags: [analytics](), [apps](), [architecture](), [building](), [real-time](), [scalable-analytics-architecture]() ### AI overview The article presents a modern stack for building real-time analytical apps with Redpanda, Iceberg, and Tinybird, and indicates that it includes the complete architecture. ### Source excerpt Building real-time analytical apps with Redpanda, Iceberg, and Tinybird creates a modern stack that scales. Complete architecture inside. ## Real-Time Analytics on Apache Iceberg with Tinybird DevFeed: [Real-Time Analytics on Apache Iceberg with Tinybird]() Original publisher: [Read original article]() Author: Alberto Romeu, Víctor Ramírez Published: 2025-05-20T10:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Apache Iceberg](), [real-time](), [Framework]() Tags: [analytics](), [apache-iceberg](), [apis](), [build](), [how-to](), [real-time](), [scalable-analytics-architecture](), [scale]() ### AI overview A tutorial on building scalable real-time analytics APIs over Apache Iceberg tables using Tinybird. ### Source excerpt Learn how to build real-time analytics APIs that scale over your Iceberg tables ## dbt in real-time DevFeed: [dbt in real-time]() Original publisher: [Read original article]() Author: Javi Santana Published: 2025-04-24T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [real-time](), [Streaming](), [SQL]() Tags: [batch](), [performance](), [pipelines](), [real-time](), [scalable-analytics-architecture](), [sql](), [streaming]() ### AI overview The article describes how dbt can transform batch models into streaming pipelines while retaining the same SQL, resulting in a different performance profile. ### Source excerpt dbt in real-time transforms your batch models into streaming pipelines. Same SQL, completely different performance profile. ## Best practices for downsampling billions of rows of data DevFeed: [Best practices for downsampling billions of rows of data]() Original publisher: [Read original article]() Author: Paco González Published: 2025-04-08T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [data]() Tags: [best-practices](), [compute](), [data](), [effective](), [reduce](), [scalable-analytics-architecture]() ### AI overview The article presents best practices for downsampling billions of rows of data. It states that downsampling can reduce compute resource use, while noting that this approach involves tradeoffs. ### Source excerpt Data downsampling can be an effective way to reduce compute resources, but it comes with tradeoffs. ## How to run load tests in real-time data systems DevFeed: [How to run load tests in real-time data systems]() Original publisher: [Read original article]() Author: Ana Guerrero, Iago Enríquez Published: 2025-03-07T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [real-time](), [systems](), [data]() Tags: [how-to](), [load](), [production](), [real-time](), [scalable-analytics-architecture](), [systems]() ### AI overview A tutorial on running load tests for real-time data systems, noting that they fail differently from traditional applications and discussing how to avoid disrupting production. ### Source excerpt Load tests on real-time data systems fail differently than traditional apps. Here's how to run them without breaking production. ## Real-Time Logs Analytics Architectures: Choosing an Approach for Scale DevFeed: [Real-Time Logs Analytics Architectures: Choosing an Approach for Scale]() Original publisher: [Read original article]() Author: Paco González Published: 2025-02-19T00:00:00Z Content type: opinion Language: en Sources: [Tinybird]() Topics: [real-time]() Tags: [analysis](), [architectures](), [logs](), [real-time](), [scalable-analytics-architecture](), [scale]() ### AI overview The article discusses real-time logs analytics architectures, which range from simple to complex, and emphasizes choosing an appropriate architecture to avoid rebuilding it later. ### Source excerpt Real-time logs analytics architectures range from simple to complex. Choose the wrong one and you'll rebuild it later. Choose wisely. ## Building Real-Time Live Sports Viewer Analytics with Tinybird and AWS DevFeed: [Building Real-Time Live Sports Viewer Analytics with Tinybird and AWS]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2024-12-03T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [real-time](), [Amazon Web Services](), [Sports]() Tags: [architecture](), [aws](), [real-time](), [scalable-analytics-architecture](), [sports]() ### AI overview The article describes building real-time live sports viewer analytics with Tinybird and AWS, including an architecture intended to support millions of viewers without crashing. ### Source excerpt Showing real-time stats to millions of viewers without crashing? It's possible. Here's the architecture that won't drain your budget. ## Application Architecture: Combining DynamoDB and Tinybird DevFeed: [Application Architecture: Combining DynamoDB and Tinybird]() Original publisher: [Read original article]() Author: Alasdair Brown Published: 2024-11-06T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [App](), [Transactions]() Tags: [architecture](), [dynamodb](), [scalable-analytics-architecture]() ### AI overview This article explains how to combine DynamoDB for transactions with Tinybird for analytics, addressing their differing performance characteristics. ### Source excerpt DynamoDB is fast for transactions but slow for analytics. Here's how to combine it with Tinybird and get the best of both worlds. ## Simple patterns for aggregating on DynamoDB DevFeed: [Simple patterns for aggregating on DynamoDB]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-10-15T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [DynamoDB](), [data]() Tags: [dynamodb](), [patterns](), [scalable-analytics-architecture]() ### AI overview The article presents four approaches for aggregating data in DynamoDB tables, addressing the fact that DynamoDB does not natively support aggregations. ### Source excerpt DynamoDB doesn't natively support aggregations, so here are four different approaches to aggregate data in DynamoDB tables. [Next page]()