Been spending some cycles rebuilding our analytics / cms stack passively with ai agents. Costly and bloated…these SaaS packages we have had to use can run $5k/mo or more. Started with a pencil drawing and sent that to the agent, and said “hey build this with placeholder data”. Mind blown. I’d spend an hour here and there prodding it with our data structures, it’s now running hourly materializedview building to collate all our data from multiple sources into our OWN database. Next step will be querying and ingesting all the external ad campaigns like Apple / Google / meta, and include those with manually created influencer or referral code campaigns as well Side effect it’s already done things like notice outperforming cohorts that I didn’t even know about. Anyway all that to say if you’re in SaaS you might need to plan on joining the rest of us devs in LaaS (landscaping as a service)

11/11. To stay up to date, follow our main repository, `paradedb/paradedb`. And to get a truly fresh read on the data infra space, read @materializedview. Chris' writing distills products and market dynamics into their core principles, and those are how teams and products win.

3/11. Reading MaterializedView has been an incredible source of pragmatic analysis on the data infra space. In a space where cargo culting is prevalent, Chris keeps it real. I attribute a lot of our learning progress to Chris' advices. Seriously, read it.

1/11. It's a new week, it's new @materializedview newsletter. If you're into data infra I *highly* encourage reading this one. It's on DuckDB and data warehouses. As always @criccomini shines with a clear, thoughtful and pragmatic analysis. On Chris' writing, DWHs, and Postgres:

Continuing our 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 series, let's discuss how you can work around operations or functions that are not easily available in 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜. Let's consider an example for this, consider you want to use 𝗟𝗔𝗚(), 𝗟𝗘𝗔𝗗() window function in Power BI but actually you don't have any direct 𝗗𝗔𝗫 𝗳𝗼𝗿𝗺𝘂𝗹𝗮 to implement this. You can use multiple 𝗗𝗔𝗫 𝗳𝗼𝗿𝗺𝘂𝗹𝗮𝘀 to get your way around but I personally thing that it is not efficient specially when you are connected to a database and you can write SQL queries in your database. Also, this solution is effective only when you have access to the database and can write query to it. 𝗦o, 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝗶𝘀: • Create a materialized view in SQL using LAG(), LEAD() or whatever function you don't have in Power BI. • Now, load the data using direct query and you will find the view that you created there like table. • Then you can use that view as table and visualize the data that you want. I attached some screenshots where I created view in SQL and then loaded the data into Power BI and visualized it using a table. #PowerBI #SQL #DAX #WindowFunctions #LAGFunction #LEADFunction #MaterializedView #DatabaseIntegration #DirectQuery #PowerBIWorkaround #DataAnalytics #BusinessIntelligence #DataVisualization #TechSolutions #DataModeling #PowerBIExpert #AdvancedAnalytics #DataEfficiency #TechUS #TechUK #TechEurope #USBusiness #UKBusiness #EuropeTech #DigitalTransformation #BIInnovation #SQLQueries #DataAnalyst #PowerBIDesktop #DatabaseManagement #BigData #BusinessDecisions #DataScienceUK #DataScienceUS #EuropeanTech #DataDrivenUK #DigitalEconomyEU #DataInsightsEurope #DataAnalysisUS #TechCommunityUS #TechInnovationUK #DataScienceEurope

Kafkaを利用したストリーム処理にすれば、リアルタイムに差分集計しながらデータを格納する事ができるので、バッチ集計が不要にできる意味です。 RDBでもMaterializedView使えば同じようなことも出来ますが、RDBはACID特性を捨てられないのでロックするところが高速化のボトルネックになるんです。

Process a #RealTime stream of driver ratings, ensuring you have the most current average rating for each driver.🚗⭐️Then materialize the results into a #materializedview, allowing downstream applications and teams to act on the results. Take a look👀 youtube.com/watch?v=JxBRVmbx…

(18/100) #100diasdecodigo @He4rtDevs Hoje mudei a contagem do twokei.com de músicas ouvidas para minutos ouvidos Tambem estudei sobre MaterializedView do Postgres (pra não ficar fazendo select o tempo todo), inclusive pretendo fazer um artigo sobre. Imagem WIP:

No doubt @ApacheFlink is the industry standard for stream processing and the backbone of our platform. We took an already great tool and built upon it to provide an even more powerful way to build stream processing apps. deltastream.io/building-upon… #streamprocessing #materializedview

Les MaterializedView de #Clickhouse sont à présent gérés dans @SQLFluff à partir de sa version 2.0.3

Last week I wrote about the newest feature of @TimescaleDB to have Continuous Aggregates on other Continuous Aggregates. Long time due, finally here 😊🎉 #TimeScaleDB #ContinuousAggregate #Incremental #MaterializedView tsdb.co/new-cont-aggs

Check out how you can build and use always up to date materialized views with simple SQL statements in DeltaStream. If you use Kafka or Kinesis, join our private beta! #streamprocessing #materializedview #serverless #eventstreaming #realtimedata #KAFKA deltastream.io/post/deltastr…

Boost your query performance with materialized views. This short video will show you how to create them. Learn more in the tutorial by @gamesbrainiac 👉 jb.gg/0jxpy8 #MaterializedView #SQL #QueryPerformance

・Stream処理とSQLが相いれないところで苦手な処理 　・JOIN 　　・制約が大きい 　・GROUP BY 　　・重い 　・MaterializedView 　　・Pullで制約アリ #CloudNativeDB

AutoMaterializedView おなかの中にデータを持っているView Redshiftはもともと持っている 定期的に発行されて自動でMaterializedViewを 生成するというとんでもないサービス #awsbasics

Creating my first #materializedview today in @postgis At some point I should really pull a blog together about schema, tables, views and materialized views! #GIS #GEO

Day 4 of #AdventOfFlink @snntrable @sjwiesman shows how INSERT INTO makes it simple to write data into external systems and maintain #materializedview github.com/ververica/flink-s…

Difference Between #View and #MaterializedView: Views are not physically stored on the disk and so, each time it gets updated when accessed while on the other hand, Materialized Views are directly stored on the disc. bit.ly/2Qeavks. #BesantTechnologies #sql #java #api

So - for that I used the new version of Postgres. I like Postgres. Ain't no view like a MaterializedView because MaterializedViews are fast!

Moving the Travelling-Salesman-Problem to a #MaterializedView in #Oracle. :) @KaiGlittenberg at #ITTage 2018