# olap Online Analytical Processing (OLAP) is a technology that uses multidimensional structures to organize and analyze data, supporting complex queries and reporting. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Beyond Inline Values: Evolving Strata's Storage Engine DevFeed: [Beyond Inline Values: Evolving Strata's Storage Engine]() Original publisher: [Read original article]() Author: Nevin Zheng Published: 2026-06-03T00:00:00Z Content type: article Language: en Sources: [nevzheng]() Topics: [Rust](), [abstraction](), [SQL](), [olap]() Tags: [abstraction](), [block](), [bootstrapping](), [layout](), [migration](), [performance](), [repo](), [rust](), [sql](), [storage](), [storage-engine](), [write-amplification]() ### AI overview This architectural decision record proposes replacing Strata's inline value storage with a block-based abstraction. It describes the current Rust LSM storage engine and SQL layer, identifies flexibility and I/O amplification problems, compares options, and records the proposed direction. ### Source excerpt An architectural decision record: why Strata is moving from inline values to block-based storage. ## What I shipped in my first 60 days at Mintlify DevFeed: [What I shipped in my first 60 days at Mintlify]() Original publisher: [Read original article]() Author: Dens Sumesh Published: 2025-11-06T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [systems](), [clickhouse](), [Kafka](), [olap](), [AI Agent](), [Artificial Intelligence]() Tags: [ai](), [ai-agent](), [analytics](), [clickhouse](), [engineering](), [infrastructure](), [kafka](), [olap]() ### AI overview An engineer describes work shipped during the first 60 days at Mintlify, including a custom analytics platform built with Kafka and ClickHouse, an AI agent for querying analytics data, and an abuse checker for filtering customer feedback. ### Source excerpt Building analytics infrastructure, abuse detection systems, and caching solutions. ## Why HTAP Databases Are Giving Way to Disaggregated Architectures DevFeed: [Why HTAP Databases Are Giving Way to Disaggregated Architectures]() Original publisher: [Read original article]() Author: Zhou Sun Published: 2025-05-04T10:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [olap](), [Cloud](), [Latency](), [consistency](), [NoSQL](), [MongoDB](), [Hadoop](), [hdfs](), [CockroachDB](), [Amazon Redshift](), [vitess]() Tags: [cloud](), [cloud-data](), [cockroachdb](), [consistency](), [data-lake](), [databases](), [engineering](), [hadoop](), [hdfs](), [latency](), [mongodb](), [olap](), [redshift](), [sql](), [vitess]() ### AI overview The article traces the separation of transactional and analytical database workloads, explaining how differing storage and scaling requirements led to specialized OLTP and OLAP systems. It argues that HTAP as a single database architecture is declining while its underlying ideas persist in today's disaggregated data stack. ### Source excerpt This blog is inspired by Jordan Tigani's "Big Data is Dead." Jordan and I actually spent some time building an HTAP database at SingleStore. From the one database that did everything in the '80s, to the great divide, to HTAP, to today's disaggregated stack--here's why HTAP as a database is dead, but its spirit lives on. ## 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. ## Implementing ETL on GCP DevFeed: [Implementing ETL on GCP]() Original publisher: [Read original article]() Author: Deepmala Published: 2020-07-15T20:53:17Z Content type: tutorial Language: en Sources: [Brave New Geek]() Topics: [DataOps](), [Cloud](), [BigQuery](), [data loss prevention](), [Low code](), [No-code](), [olap]() Tags: [analytics](), [analytics-pipeline](), [bi-tools](), [bigquery](), [cdap](), [cloud-data-loss-prevention](), [cloud-dataflow](), [cloud-dataprep](), [cloud-dataproc](), [cloud-pub-sub](), [cloud-storage](), [cloud-tasks](), [data-analytics](), [data-fusion](), [data-lake](), [data-loss-prevention](), [elt](), [etl](), [gcp](), [no-code](), [serverless](), [sql]() ### AI overview A practical guide to building ETL pipelines on Google Cloud Platform using Google-managed services. It explains a two-phase architecture with Cloud Storage as a data lake, Cloud Data Loss Prevention for sensitive-data detection or redaction, and BigQuery as the curated data warehouse, with attention to low-code and no-code approaches. ### Source excerpt ETL (Extract-Transform-Load) processes are an essential component of any data analytics program. This typically involves loading data from disparate sources, transforming or enriching it, and storing the curated data in a data warehouse for consumption by different users or systems. An example of this would be taking customer data from operational databases, joining it with data from Salesforce and Google Analytics, and writing it to an OLAP database or BI engine.