Email: 30 Years of Postgres 🐘, Spotify Skips Bayesian 🎧, Kestra 2.0 Ships 🚀

Subject:
30 Years of Postgres 🐘, Spotify Skips Bayesian 🎧, Kestra 2.0 Ships 🚀
Received:
9/10/2026, 10:12:25 AM
From:
"TLDR Data" <dan@tldrnewsletter.com>
To:
silk.theater.56@fwdnl.com
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&lt;div style="display: none; max-height: 0px; overflow: hidden;"&gt;Tom Lane, a committer for 25 of Postgres&#039; 30 years, walks through the architectural bets that held: process-per-connection for code simplicity&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;‌&nbsp;&lt;/div&gt;

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&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fclickhouse.com%2Fbenchmarks%2Fcostbench%3Futm_source=tldr%26utm_medium=paid-newsletter%26utm_campaign=2026-09-10_Primary_Clickhouse%26utm_content=header_costbench_100_billion%26utm_id=tldr1/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/ledsI2LJF-9bZXgifiCULd6Ft8RaIoJ5OhjiUmCkQjk=452"&gt;&lt;img src="https://images.tldr.tech/clickhouse.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="ClickHouse"&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;


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&lt;h1&gt;&lt;strong&gt;TLDR Data &lt;span id="date"&gt;2026-09-10&lt;/span&gt;&lt;/strong&gt;&lt;/h1&gt;
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                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fclickhouse.com%2Fbenchmarks%2Fcostbench%3Futm_source=tldr%26utm_medium=paid-newsletter%26utm_campaign=2026-09-10_Primary_Clickhouse%26utm_content=header_costbench_100_billion%26utm_id=tldr1/2/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/cF3rbr0llhasRXTJnj-TRrhjKqH8CKPf3uSYd9JcYKs=452"&gt;
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                                        &lt;strong&gt;CostBench: 100 billion rows. Up to 83% lower read costs. (Sponsor)&lt;/strong&gt;
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                                    Fast queries are only part of your analytics bill. Ingesting and preparing fresh data costs money too.&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fclickhouse.com%2Fbenchmarks%2Fcostbench%3Futm_source=tldr%26utm_medium=paid-newsletter%26utm_campaign=2026-09-10_Primary_Clickhouse%26utm_content=Body_costbench%26utm_id=tldr1/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/4u6A0aKheepGEK6U1ddTYh6xRmMn2RtL5tQ0A0ie5dM=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt;CostBench&lt;/span&gt;&lt;/a&gt; measures ingest, preparation, and reads. We streamed &lt;strong&gt;1 million stock-market quotes per second&lt;/strong&gt;, reaching &lt;strong&gt;100 billion rows&lt;/strong&gt;, with dashboard and drill-down queries running throughout.&lt;/p&gt;

&lt;p&gt;Against traditional cloud data warehouses we tested, ClickHouse delivered:&lt;/p&gt;

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&lt;li&gt;Up to &lt;strong&gt;82% lower ingest costs&lt;/strong&gt;&lt;/li&gt;
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&lt;li&gt;Up to &lt;strong&gt;83% lower read costs&lt;/strong&gt;&lt;/li&gt;
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&lt;p&gt;&lt;strong&gt;Open-source code. Full methodology. Public results.&lt;/strong&gt;&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fclickhouse.com%2Fbenchmarks%2Fcostbench%3Futm_source=tldr%26utm_medium=paid-newsletter%26utm_campaign=2026-09-10_Primary_Clickhouse%26utm_content=Body_inspect_numbers_reproduce_benchmark%26utm_id=tldr1/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/ORQezMFLQMfd5cTMZ5VAuIsIQ9w1pcnCB5g2PLBP2-g=452" rel="noopener noreferrer nofollow" target="_blank"&gt;&lt;span&gt; Inspect the numbers and reproduce the benchmark.&lt;/span&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Try ClickHouse Cloud on your own data. Start with &lt;strong&gt;$300 in free trial credits&lt;/strong&gt;.&lt;/p&gt;

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&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;📱&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
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&lt;h1&gt;&lt;strong&gt;Deep Dives&lt;/strong&gt;&lt;/h1&gt;
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                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.snowflake.com%2Fen%2Fblog%2Fengineering%2F30-years-of-postgres-architecture-tom-lane%2F%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/QpiraWKNGCHWshoYvu5meBm1_pqQDH2ZAg38qG8jyQo=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Tom Lane on the Architectural Decisions That Shaped 30 Years of Postgres (41 minute read)&lt;/strong&gt;
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&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Tom Lane, a committer for 25 of Postgres' 30 years, walks through the architectural bets that held: process-per-connection for code simplicity, write-ahead logging in 8.0 that turned it from a plaything into a serious database, and MVCC pushing maintenance work into background vacuum. He credits Berkeley's liberal license for the project surviving where its contemporaries did not, names version 13 the buggiest release, and says he would delete partitioning given the chance.
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                                    &lt;span&gt;
                                        &lt;strong&gt;Built for reliability: How American Express processes payments at scale (12 minute read)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    American Express uses cells as complete payment-processing failure domains: microservices, databases, DNS, and reference data are local, while dynamic transactions are routed to the cell that owns their state. The design trades global coordination for controlled failure scope and deliberately rejects transactions when consistency cannot be established.
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                                    &lt;span&gt;
                                        &lt;strong&gt;Cloudfloe's query engine in a celld cell (9 minute read)&lt;/strong&gt;
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&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Cloudfloe is a web interface for querying Iceberg data on S3 with DuckDB. This experiment tested whether a stateful, serverless-style cell could replace a per-query container: DuckDB WebAssembly read a 37,537-row Iceberg table in 148 ms, but cold or woken cells took 250–320 ms and retained most of their memory. Keeping the connection warm cut repeat queries to about 3 ms, making lifecycle policy, not raw startup, the core design tradeoff.
                                &lt;/span&gt;
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&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;🚀&lt;/span&gt;&lt;/div&gt;
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&lt;h1&gt;&lt;strong&gt;Opinions &amp; Advice&lt;/strong&gt;&lt;/h1&gt;
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&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Non-technical staff now write billions of lines of analytics code. The problem is where it lives: random context, porous permissions, and output scattered across one-off chats, Slack pastes, and someone's laptop. With no record of which table an agent grabbed or which filter it dropped, the only feedback loop left is people disagreeing.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.theseattledataguy.com%2F5-cdc-tools-and-the-tradeoffs-you-should-know%2F%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/dKXdZQ_2QywqW3S4Aix2ICiSLmmUCqac2QSpPBekXNM=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;5 CDC tools and the tradeoffs you should know (14 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    CDC evaluation should start with failure behavior, not connector counts. Compare capture method, initial snapshots and backfills, schema evolution, delivery semantics, observability, deployment model, and who owns recovery. A production proof needs representative data and a broken-destination test, especially for type fidelity and duplicate handling.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fankit-rana.com%2Flogs%2F49-jitter-synchronised-clients%2F%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/KFEXN7v9pbGOFlLdoUtvsHP0en_SxDrWUlWaINMB944=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Jitter is the cheapest reliability fix you are not using (5 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Distributed systems synchronise themselves without being told to: shared events such as deploys, cache flushes, or connection loss turn independent workloads into recurring load spikes. Add jitter to initial retries, TTL refreshes, heartbeats, token renewals, and scheduled work. This preserves average load while preventing retry storms from repeatedly hitting a recovering dependency in lockstep.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;💻&lt;/span&gt;&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding-top: 0px; padding-bottom: 0px;"&gt;
&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Launches &amp; Tools&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table style="table-layout: fixed; width: 100%;" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top"&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fkestra.io%2Fblogs%2Frelease-2-0%2F%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/Ut2OGuX8UdA6KcZaPeXavPJWdFHyK92IjHWwBZ6lcCY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Kestra 2.0: A new engine, workers anywhere, and flows as agent tools (16 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Kestra is an open-source orchestration platform for scheduling and operating data, infrastructure, and business workflows. Its 2.0 LTS release separates control and data planes so workers can run across regions, clouds, or private networks over outbound-only connections. It also exposes flows as MCP tools for agents, decouples queue and storage choices, and includes migration tooling for 1.x users.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Fgalaxy-io%2Ffilament%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/DsnoSVRKwasZtjW1XvQHZDkZOAcR6sDM0mFy_CIScZc=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Filament (GitHub Repo)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Filament replicates data from sources to sinks with full, incremental, or CDC modes, verifying every bounded batch on both sides of the write and advancing checkpoints only after success. Sources, sinks, state storage, and event transport are all swappable.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.jupyter.org%2Fjupytergis-0-16-new-visualization-capabilities-collaborative-story-maps-and-more-03e6b78bacc0%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/7MLADLqeRkp9AC36-94nBUnsbKHOdnVxhXcg2kPic6g=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;JupyterGIS 0.16: Collaborative story maps and remote geospatial workflows (3 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    JupyterGIS 0.16 adds real-time collaborative story maps, lazy tile rendering for remote openEO workflows, and native GeoZarr and GeoPackage support. Declarative symbology and Xarray tiling keep visualizations reproducible while allowing large geospatial datasets to stay remote instead of being materialized locally.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;🎁&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;strong&gt;&lt;h1&gt;Miscellaneous&lt;/h1&gt;&lt;/strong&gt;&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fengineering.atspotify.com%2F2026%2F9%2Fwhy-spotify-is-not-using-bayesian-a-b-testing%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/MQ3Z6pXL5wqU-gT2Uh8hyKv0ltnds9n2BMpaflEkHaY=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Why Spotify is not using Bayesian A/B testing (12 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Bayesian experimentation is not one method but a choice of prior, likelihood, and stopping rule. Spotify shows that common flat-prior posterior thresholds can reproduce frequentist peeking, while Bayes factors can control false positives under optional stopping. Choose guarantees for the experiment program first, then the inference configuration.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.dwarkesh.com%2Fp%2Fpretraining-progress-is-mostly-data%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/MOHE3LhNumPLYC70KZwvt-QmcJdLNEiCnElCm4E9Lzg=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;Pretraining progress is mostly coming from data (11 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    A controlled comparison of 2019-2025 open-model recipes and data corpora attributes 12.0x compute-efficiency gains to data improvements versus 3.7x to model recipes at a 1e19-FLOP budget. The result frames dataset extraction, curation, and filtering as a major systems lever, while cautioning that larger-scale and synthetic-data effects remain untested.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;&lt;span style="font-size: 36px;"&gt;⚡&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;div class="text-block"&gt;
&lt;div style="text-align: center;"&gt;

&lt;h1&gt;&lt;strong&gt;Quick Links&lt;/strong&gt;&lt;/h1&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

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&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2FRusselWebber%2FxlDuckDb%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/zfCdy0RUmuGcRKXLswpZR1sbN1kCKg5aTpg0TfUPhhI=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;xlDuckDb (GitHub Repo)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    xlDuckDb adds a DuckDbQuery() function to Excel that runs DuckDB SQL and spills results into cells, querying worksheet ranges, tables, and local or remote JSON, CSV, and Parquet in one statement.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td class="container" style="padding: 15px 15px;"&gt;
&lt;div class="text-block"&gt;
&lt;span&gt;
                                &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fdatabase-doctor.com%2Fposts%2Ftpch-q15%3Futm_source=tldrdata/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/gIx3TPfDGvyqAHFQx5PA0WNTAuLaAeIcDKo1AK1J1Ik=452"&gt;
                                    &lt;span&gt;
                                        &lt;strong&gt;TPC-H Query 15 - Scalar Constants and Materialisation (5 minute read)&lt;/strong&gt;
                                    &lt;/span&gt;
&lt;/a&gt;
&lt;br&gt;
&lt;br&gt;
&lt;span style="font-family: &quot;Helvetica Neue&quot;, Helvetica, Arial, Verdana, sans-serif;"&gt;
                                    Referencing the same CTE twice tests whether the optimiser materialises the result.
                                &lt;/span&gt;
&lt;/span&gt;
&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;






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&lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fin%2Fjoelvanveluwen%2F/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/QGYPGAGugUiggRD7jdgLNoc2KiTAZnqma2aqXiVZU7A=452"&gt;&lt;span&gt;Joel Van Veluwen&lt;/span&gt;&lt;/a&gt;, &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fin%2Fjennytzurueyching%2F/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/EaiqaFqxq2uEQEfVyhE3oDzHEyijArvzU-JEzYNicyA=452"&gt;&lt;span&gt;Tzu-Ruey Ching&lt;/span&gt;&lt;/a&gt; &amp; &lt;a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fin%2Fremi-turpaud%2F/1/010001a08acde915-61d7c400-e0f5-40b5-97f1-158f7d51a2e8-000000/ZYXfa-YuuAaVyLdlgPbZiX82wBoiZqC2zsQ7XcVsbp4=452"&gt;&lt;span&gt;Remi Turpaud&lt;/span&gt;&lt;/a&gt;
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