# The Data Base Published articles for The Data Base. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Tinybird vs. ClickHouse®: What's the difference? DevFeed: [Tinybird vs. ClickHouse®: What's the difference?]() Original publisher: [Read original article]() Author: Gonzalo Gómez Published: 2026-04-17T00:00:00Z Content type: comparison Language: en Sources: [Tinybird]() Topics: [clickhouse](), [developer tooling](), [SDKs](), [real-time]() Tags: [analytics](), [clickhouse](), [developer](), [developer-tooling](), [open-source](), [real-time](), [sdks](), [the-data-base]() ### AI overview The article compares ClickHouse with Tinybird, describing ClickHouse as an open source columnar database and Tinybird as a data platform built on ClickHouse with developer tooling, SDKs, and infrastructure abstractions for building real-time analytics APIs. ### Source excerpt ClickHouse® is an open source columnar database. Tinybird is a data platform built on ClickHouse® with developer tooling, SDKs, and infrastructure abstractions for building real-time analytics APIs. ## IBM Buying Confluent Reflects Enterprise Preference for Safety DevFeed: [IBM Buying Confluent Reflects Enterprise Preference for Safety]() Original publisher: [Read original article]() Author: Javi Santana Published: 2026-02-02T00:00:00Z Content type: opinion Language: en Sources: [Tinybird]() Topics: [ibm]() Tags: [confluent](), [enterprise](), [ibm](), [the-data-base]() ### AI overview The article argues that IBM buying Confluent is a move to provide enterprises with a safe, established choice. ### Source excerpt IBM buying Confluent is just another move to secure a safe place for enterprise. Nobody ever got fired for buying IBM--or Confluent. ## How to Achieve Real-Time Exactly-Once Ingestion from Kafka to ClickHouse DevFeed: [How to Achieve Real-Time Exactly-Once Ingestion from Kafka to ClickHouse]() Original publisher: [Read original article]() Author: Gonzalo Gómez Published: 2025-12-16T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Kafka](), [real-time]() Tags: [architecture](), [clickhouse](), [how-to](), [kafka](), [real-time](), [the-data-base]() ### AI overview A tutorial on an architectural pattern for achieving exactly-once ingestion from Kafka to ClickHouse in real time. The supplied text states that the pattern guarantees no duplicates. ### Source excerpt Exactly-once delivery from Kafka to ClickHouse isn't magic. It's architecture. Here's the pattern that guarantees no duplicates. ## How to Monitor Kafka with ClickHouse® Kafka Engine DevFeed: [How to Monitor Kafka with ClickHouse® Kafka Engine]() Original publisher: [Read original article]() Author: Alberto Romeu Published: 2025-12-10T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Kafka](), [Monitoring]() Tags: [clickhouse](), [how-to](), [kafka](), [monitoring](), [the-data-base]() ### AI overview A tutorial about monitoring Kafka using ClickHouse Kafka Engine, including setup guidance and discussion of ways the configuration can fail. ### Source excerpt Kafka monitoring with ClickHouse Kafka Engine breaks in ways nobody documents. Here's how to set it up properly. ## Tinybird vs ClickHouse® Cloud cost comparison explained DevFeed: [Tinybird vs ClickHouse® Cloud cost comparison explained]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2025-08-26T10:00:00Z Content type: comparison Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Cloud]() Tags: [architecture](), [comparison](), [cost](), [pricing](), [the-data-base](), [vs]() ### AI overview A comparison of Tinybird and ClickHouse Cloud pricing at real production scale, including how pricing differences may affect architecture decisions. ### Source excerpt Tinybird vs ClickHouse Cloud cost comparison at real production scale. The pricing difference might change your architecture decision. ## How to install ClickHouse® on your own servers with Tinybird DevFeed: [How to install ClickHouse® on your own servers with Tinybird]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2025-08-24T10:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Cloud](), [Server]() Tags: [clickhouse](), [cloud](), [how-to](), [infrastructure](), [install](), [servers](), [the-data-base]() ### AI overview The article introduces installing ClickHouse on your own servers with Tinybird, highlighting self-managed regions as a way to reduce infrastructure-related work. ### Source excerpt Want ClickHouse in your own cloud without the infrastructure headaches? Tinybird's self-managed regions handle the hard parts. ## How to ingest 1 billion rows per second in ClickHouse® DevFeed: [How to ingest 1 billion rows per second in ClickHouse®]() Original publisher: [Read original article]() Author: Javi Santana Published: 2025-08-18T10:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [clickhouse]() Tags: [clickhouse](), [how-to](), [the-data-base]() ### AI overview The article explores whether it is possible to ingest 1 billion rows per second in ClickHouse, motivated by a report about Tesla engineers achieving that rate. The supplied text does not state the outcome. ### Source excerpt After seeing the engineers at Tesla talk about 1B row/s ClickHouse® ingestion, I wanted to see if I could do it myself. ## Supabase for User-Facing Analytics: Limitations and Alternatives DevFeed: [Supabase for User-Facing Analytics: Limitations and Alternatives]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2025-05-26T00:00:00Z Content type: comparison Language: en Sources: [Tinybird]() Topics: [Supabase]() Tags: [analytics](), [the-data-base]() ### AI overview The article discusses using Supabase for user-facing analytics, stating that Supabase struggles in this use case and considering what works better. ### Source excerpt Supabase is great until you need user-facing analytics. Then it struggles. Here's what works better for that use case. ## A Developer's Guide to Data Engineering DevFeed: [A Developer's Guide to Data Engineering]() Original publisher: [Read original article]() Author: Javi Santana Published: 2025-05-15T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [data-engineering](), [Code](), [data]() Tags: [code](), [data](), [data-engineering](), [developer](), [guide](), [the-data-base]() ### AI overview A guide explaining the main differences between developers, who work with code and some data, and data engineers, who work with a lot of data and some code. ### Source excerpt Developers work with code and some data, data engineers work with a lot of data and some code. We explain the main differences. ## Lessons learned from 5 years with ClickHouse® clusters: Part II DevFeed: [Lessons learned from 5 years with ClickHouse® clusters: Part II]() Original publisher: [Read original article]() Author: Javi Santana Published: 2025-04-16T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [clickhouse]() Tags: [clickhouse](), [clusters](), [insights](), [production](), [the-data-base]() ### AI overview An article presenting lessons from five years of operating ClickHouse clusters, including production lessons, failures, and insights. ### Source excerpt What I learned operating ClickHouse part II: more production lessons, more failures, more insights you won't find in docs. ## I've operated petabyte-scale ClickHouse® clusters for 5 years DevFeed: [I've operated petabyte-scale ClickHouse® clusters for 5 years]() Original publisher: [Read original article]() Author: Javi Santana Published: 2025-04-01T00:00:00Z Content type: opinion Language: en Sources: [Tinybird]() Topics: [clickhouse]() Tags: [clickhouse](), [clusters](), [experience](), [production](), [scale](), [the-data-base]() ### AI overview A practitioner shares lessons from operating ClickHouse at scale, including wins, failures, and production experience. ### Source excerpt What I learned operating ClickHouse at scale: the wins, the failures, and the lessons that only come from production experience. ## We added the Backup Database engine to ClickHouse® DevFeed: [We added the Backup Database engine to ClickHouse®]() Original publisher: [Read original article]() Author: Maksim Kita Published: 2025-03-03T00:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Database]() Tags: [backup](), [clickhouse](), [database](), [the-data-base]() ### AI overview The article announces a new Backup engine for ClickHouse and explains why it was added, how it was implemented, and how to use it. ### Source excerpt We added a new ClickHouse® engine: Backup. Here's why we did it, how it's implemented, and example usage. ## Outgrowing Postgres: optimize and integrate OLTP + OLAP stack DevFeed: [Outgrowing Postgres: optimize and integrate OLTP + OLAP stack]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-02-27T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [olap](), [Transactions]() Tags: [analytics](), [how-to](), [olap](), [postgres](), [scale](), [the-data-base]() ### AI overview This tutorial explains how to optimize Postgres for analytics and when to integrate an OLAP system alongside its transaction-processing workload. ### Source excerpt Postgres handles transactions fine but chokes on analytics at scale. Here's how to optimize it and when to add OLAP. ## Outgrowing Postgres: how to evaluate the right OLAP solution DevFeed: [Outgrowing Postgres: how to evaluate the right OLAP solution]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-02-17T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [olap]() Tags: [analytics](), [olap](), [postgres](), [the-data-base]() ### AI overview This article discusses how to evaluate an OLAP solution when outgrowing Postgres for analytics, emphasizing the importance of understanding query patterns. ### Source excerpt Outgrowing Postgres for analytics? Evaluating the right OLAP solution requires understanding your query patterns. Start here. ## Outgrowing Postgres: When to move OLAP workloads off Postgres DevFeed: [Outgrowing Postgres: When to move OLAP workloads off Postgres]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-02-07T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [olap]() Tags: [analytics](), [olap](), [postgres](), [the-data-base]() ### AI overview The article explains when to move analytics workloads off Postgres by identifying technical and team health warning signs before a crisis. ### Source excerpt Learn when to move analytics off Postgres by watching for technical and team health warning signs before crisis hits. ## Outgrowing Postgres: How to run OLAP workloads on Postgres DevFeed: [Outgrowing Postgres: How to run OLAP workloads on Postgres]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-01-31T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [olap]() Tags: [analytics](), [deep-dive](), [olap](), [postgres](), [techniques](), [the-data-base]() ### AI overview A deep dive into running OLAP workloads on Postgres, covering basic optimizations, advanced techniques, and when to consider moving away from Postgres. ### Source excerpt A deep dive into running analytics on Postgres, from basic optimizations to advanced techniques and knowing when to quit. ## Outgrowing Postgres: Handling growing data volumes DevFeed: [Outgrowing Postgres: Handling growing data volumes]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-01-17T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [data](), [Optimization]() Tags: [data](), [optimization](), [scale](), [techniques](), [the-data-base](), [time]() ### AI overview The article discusses how to handle growing Postgres data volumes at terabyte scale, suggesting optimization techniques before migration and considering when to move to another solution. ### Source excerpt Postgres slowing down at terabyte scale? Before you migrate, try these optimization techniques. And know when it's time to move on. ## Query DynamoDB tables with SQL DevFeed: [Query DynamoDB tables with SQL]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-10-30T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [DynamoDB](), [SQL](), [NoSQL]() Tags: [dynamodb](), [nosql](), [sql](), [the-data-base]() ### AI overview The article presents querying DynamoDB tables with SQL and says Tinybird makes NoSQL data accessible to people who know SQL, avoiding PartiQL. ### Source excerpt Query DynamoDB with SQL instead of fighting with PartiQL. Tinybird makes your NoSQL data accessible to anyone who knows SQL. ## 5 Best Use Cases for DynamoDB in 2026 | Tinybird DevFeed: [5 Best Use Cases for DynamoDB in 2026 | Tinybird]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-09-18T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [DynamoDB]() Tags: [2026](), [dynamodb](), [the-data-base](), [use-cases]() ### AI overview The article examines five use cases for DynamoDB in 2026, including situations where it performs well and where it may struggle. It emphasizes choosing the right tool before migration becomes necessary. ### Source excerpt DynamoDB use cases where it excels and where it struggles. Pick the right tool before you're stuck migrating later. ## Managed ClickHouse options compared in 2026 DevFeed: [Managed ClickHouse options compared in 2026]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-08-20T00:00:00Z Content type: comparison Language: en Sources: [Tinybird]() Topics: [clickhouse](), [hosting]() Tags: [2026](), [clickhouse](), [comparison](), [hosting](), [platforms](), [the-data-base]() ### AI overview A comparison of managed ClickHouse options available in 2026, ranging from basic hosting services to full platforms. ### Source excerpt Managed ClickHouse options in 2026 range from basic hosting to full platforms. This comparison shows what you actually get. ## ClickHouse JOIN performance improvements from two pull requests DevFeed: [ClickHouse JOIN performance improvements from two pull requests]() Original publisher: [Read original article]() Author: Maksim Kita Published: 2024-04-30T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [clickhouse](), [pull-requests]() Tags: [clickhouse](), [performance](), [pull-requests](), [the-data-base]() ### AI overview Two pull requests introduced to ClickHouse improve JOIN performance in common scenarios. ### Source excerpt We recently introduced two pull requests to ClickHouse® that significantly improve JOIN performance in common scenarios. ## Best database for real time analytics in 2026 and how to choose DevFeed: [Best database for real time analytics in 2026 and how to choose]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-03-26T00:00:00Z Content type: comparison Language: en Sources: [Tinybird]() Topics: [Database](), [real-time]() Tags: [2026](), [analytics](), [comparison](), [database](), [real-time](), [the-data-base]() ### AI overview A comparison of database options for real-time analytics in 2026, emphasizing that the best choice depends on the workload. ### Source excerpt The best database for real-time analytics in 2026 depends on your workload. This comparison covers the options that actually matter. ## Columnar Databases: Architecture and Analytical Query Performance DevFeed: [Columnar Databases: Architecture and Analytical Query Performance]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2023-11-03T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [Database]() Tags: [database](), [the-data-base]() ### AI overview An introduction to columnar database architecture and its relationship to analytical query performance, including a stated claim that analytical queries can run 100x faster. ### Source excerpt What is a columnar database and why does it matter? Understanding the architecture explains why analytical queries run 100x faster. ## A practical guide to real-time CDC with MySQL DevFeed: [A practical guide to real-time CDC with MySQL]() Original publisher: [Read original article]() Author: Jim Moffitt Published: 2023-08-23T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [MySQL](), [Confluent Cloud](), [real-time](), [data]() Tags: [confluent-cloud](), [data](), [guide](), [mysql](), [real-time](), [the-data-base]() ### AI overview A step-by-step guide to setting up Change Data Capture with MySQL, Confluent Cloud, and Tinybird. ### Source excerpt A step-by-step guide to setting up Change Data Capture (CDC) with MySQL, Confluent Cloud, and Tinybird. [Next page]()