Podcasts about Trino

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TESTO DELL'ARTICOLO ➜ https://www.bastabugie.it/8144I VERI PATRONI D'EUROPA, ALTRO CHE URSULA VON DER LEYEN di Cristina Siccardi I patroni d'Europa non sono Ursula von der Leyen, Roberta Metsola, António Costa, Kaja Kallas e neppure Macron e Steinmeier, bensì i santi Benedetto da Norcia, Cirillo e Metodio, Brigida di Svezia, Caterina da Siena e Teresa Benedetta della Croce, sui quali il Senato della Repubblica italiana scriveva nel 2017 in una pubblicazione dal titolo Patroni d'Europa. Percorsi di unità, di pace, di cultura: «In modi speciali essi sono stati tutti profondamente europei [...]. Se pace, cultura, dialogo, difesa dei diritti umani sono oggi imperativi morali per tutti i cittadini d'Europa, e non solo per chi si professa credente, dobbiamo riconoscere il merito a straordinari precursori. La loro voce, a distanza di secoli, ancora ha molto da dirci e da insegnarci». Leggendo queste considerazioni, occorre fare alcuni doverosi distinguo. L'allora presidente del Senato, Pietro Grasso, aveva riconosciuto il patronato dell'Europa dei santi menzionati; tuttavia, ha compiuto un'operazione conforme a tutti coloro che da molti anni cercano di assorbire le figure dei santi nell'agone del liberalismo laicista politico e religioso, strumentalizzando i loro insegnamenti.I santi patroni d'Europa hanno operato nella pace di Cristo e non del mondo; hanno tessuto le loro relazioni non in un vacuo dialogo, ma sulle linee costruttive del Vangelo; non hanno pensato e agito in modalità antropocentrica, ma evangelica e con spirito soprannaturale alla luce della Grazia di Dio; hanno dato priorità alla Gloria di Dio e non del mondo, concentrandosi sulla salvezza delle anime, considerando lesive le proposte e tentazioni mondane. Essi non sono stati «straordinari precursori» dell'ideologia europeista anticristiana, bensì Maestri nell'instaurare il Regno di Dio attraverso Cristo Re.San Benedetto da Norcia (480-547) è stato dichiarato «Santo patrono di tutta l'Europa» da papa Paolo VI il 24 ottobre 1964 con la lettera apostolica Pacis Nuntius. Cirillo e Metodio sono stati proclamati compatroni da papa Giovanni Paolo II il 31 dicembre 1980 con la lettera apostolica Egregiae virtutis; lo stesso Papa ha inoltre proclamato compatrone d'Europa santa Brigida di Svezia, santa Caterina da Siena e santa Teresa Benedetta della Croce il 1º ottobre 1999.SAN BENEDETTO, SANTI CIRILLO E METODIOLa statura umana e cristiana di san Benedetto resta nella Storia un luminoso punto di riferimento in un'epoca di profondi mutamenti (come la nostra), quando l'antico ordinamento romano stava ormai crollando e stava per nascere una nuova era sotto l'impulso di nuovi popoli emergenti all'orizzonte dell'Europa. Attraverso la fondazione delle abbazie e dei monasteri nel continente, san Benedetto risanò le anime, bonificò i villaggi, promuovendo la coltivazione razionale delle terre, offrendo lavoro alle famiglie che vivevano e lavoravano intorno ai centri benedettini; salvò l'antico patrimonio culturale e letterario greco-romano, influì sulla trasformazione dei costumi dei barbari. La Regola benedettina portò ordine e civilizzazione grazie a due parole profondamente applicate «Ora et labora», che instillarono il senso del dovere, stando attenti alla propria coscienza e allo sguardo di Dio (ciò implicava, conseguentemente, il rispetto per i legittimi diritti altrui) e che promossero responsabilizzazione, coraggio, determinazione, tutto ciò, disse Giovanni Paolo II durante la sua visita pastorale a Norcia il 23 marzo 1980, «sulla base e in forza di una vita spirituale di fede e di preghiera assolutamente intensa ed esemplare».La missione dei fratelli Cirillo (826/827-869) e Metodio (815/825-885), evangelizzatori bizantini dei popoli slavi in Moravia e Pannonia (antica regione compresa tra i fiumi Danubio e Sava, che comprendeva la parte occidentale dell'attuale Ungheria, il Burgenland oggi Land austriaco, fino a Vienna, la parte nord della Croazia e parte della Slovenia), produsse nel IX secolo l'invenzione dell'alfabeto glagolitico, noto come «cirillico», dal nome del suo inventore e nato dal geniale sforzo di conciliare le lingue latina, greca e slava. Come san Benedetto aveva posto le basi dell'Europa latina, i due fratelli di Tessalonica innestarono nel continente la tradizione greca e bizantina, come riconobbe papa Pio XI con la Lettera Apostolica Quod Sanctum Cyrillum del 1927, definendoli «figli dell'Oriente, di patria bizantini, d'origine greci, per missione romani, per i frutti apostolici slavi».Le nazioni europee, con le loro lingue, le loro culture, i loro usi e costumi furono unite sotto il Sacro Romano Impero, che si instaurò sotto l'egida e il faro del Cristianesimo, un credo non rivoluzionario, non distruttivo, ma forte nei suoi principi e nei suoi valori del Dio Uno e Trino, di patria, di famiglia e proprietà privata. È di tutta evidenza che il collante di tante diversità fu la Fede religiosa, che rispettava ogni identità, a differenza della surrettizia Unione Europea che vuole imporre, senza rispetto di quelle identità, il livellante pensiero unico alle genti europee.Aver eliminato il Cristianesimo dalla linfa europea, come ben vediamo, ha trasportato il continente nel baratro del pensiero neonietzschiano, che nega verità oggettive, imponendo una pluralità di prospettive opinabili, in cui le “verità soggettive” e i presunti diritti sono legati all'ideologia schizofrenica di chi domina con politiche sovranazionali, tiranniche e schiavizzanti, che vanno contro le Leggi di Dio, ma anche contronatura, riproponendo in definitiva il «non serviam» di matrice luciferina. Se l'Europa era stata ferita e incrinata dalla rivoluzione protestante, oggi la presunta Unione Europea, claudicante e persa in un labirinto di confusione, è il frutto del suo tradimento a se stessa.SANTA BRIGIDA DI SVEZIA ED EDITH STEINSanta Brigida di Svezia (1303-1373) fu sposa, madre, monaca, mistica, donna di grande carità e coordinatrice di ordine e di pace dentro e fuori la Chiesa. Si recò a Roma per celebrare l'Anno Santo del 1350 e qui trovò una situazione drammatica: il Papa si era trasferito ad Avignone e il popolo romano era come un gregge senza pastore. C'era la peste e in Europa infuriava il conflitto tra Francia e Inghilterra. Nelle stanze di Palazzo Farnese e nelle chiese romane ricevette rivelazioni divine, intanto parlava direttamente al Papa, ai cardinali, ai governanti europei, anche per intercedere per la pace in Europa al fine di porre termine alla guerra dei Cent'anni. Si prodigò per il ritorno del Pontefice a Roma, come fece anche vigorosamente la mistica domenicana e sua contemporanea santa Caterina da Siena (1347-1380), la quale, sopravvivendole, sarà testimone del ritorno definitivo a Roma di Gregorio XI nel 1377. Particolarmente devota della Passione di Cristo, giunse il tempo dei pellegrinaggi brigidini: da Assisi al Gargano, arrivando poi in Terra Santa, quando aveva quasi settant'anni.Cinque santi medioevali come patroni d'Europa ed una dell'età moderna, l'ebrea Edith Stein (1891-1942), atea convertita al Cattolicesimo, che divenne carmelitana scalza. Dalla brillante intelligenza, scelse il ramo universitario della filosofia e dopo essere stata allieva di Edmund Husserl, divenne membro della facoltà di Friburgo. Un giorno rimase folgorata quando vide una donna con i sacchetti della spesa entrare in una chiesa per pregare... un atto semplicissimo, ma che a Edith rivelò che Dio può essere pregato in qualsiasi momento e quindi apprese, grazie a quella donna, che il punto centrale del Credo cristiano è lo stabilire un rapporto personale fra l'anima e il Padre Creatore. Nel 1921, durante una vacanza, lesse l'autobiografia della mistica carmelitana Teresa d'Avila e da allora abbracciò Santa Romana Chiesa, ricevendo il battesimo il 1° gennaio 1922. Dopo un periodo di discernimento spirituale, entrò nel monastero carmelitano di Colonia nel 1934, prendendo il nome di Teresa Benedetta della Croce e qui scrisse il libro metafisico Endliches und ewiges Sein (Essere finito ed Essere eterno) con l'obiettivo di conciliare le filosofie di san Tommaso d'Aquino e di Husserl.Per proteggerla dalle leggi razziali, l'Ordine delle Carmelitane scalze la trasferì nei Paesi Bassi, ma non fu sufficiente: il 26 luglio 1942 entrò in vigore l'ordine di Hitler che anche gli ebrei convertiti dovevano essere catturati e internati. Fu così che Edith e sua sorella Rosa, anche lei divenuta cattolica, furono deportate nel campo di concentramento di Auschwitz, dove vennero uccise nelle camere a gas il 9 agosto 1942 e i loro corpi furono bruciati nei forni crematori.ROBERTO BENIGNI ESALTA IL MANIFESTO DI VENTOTENEAlcuni giorni fa Roberto Benigni ha teatralmente declamato e inneggiato con lo spettacolo intitolato «Il Sogno» il Manifesto di Ventotene per un'Europa libera e unita, manifesto che è stato protagonista di una ormai nota manifestazione progressista a Roma, ma anche di molteplici polemiche politiche e mediatiche. Nel decantare l'Europa culturale e l'indiscutibile suo primeggiare nel mondo, Benigni si è però completamente “scordato” di far presente che è stata la religione cristiana ad aver dato vita ad uno straordinario sviluppo dell'arte, della letteratura, della musica nel segno della bellezza; ma ha anche “scordato” di dire che è stato il Cristianesimo ad avviare lo studio scientifico degli esseri animati e inanimati, si pensi alle realtà monastiche che si sono occupate della catalogazione del mondo vegetale e animale, nonché dello studio medico delle erbe officinali; ma si pensi anc

Siguen apareciendo audios y trinos que ponen en jaque la relación entre la Fiscalía y Armando Benedetti 

Marco Ansaldo"La morte di Papa Francesco"L'annuncio della morte di Papa Francesco è stato dato nella Cappella di Casa Santa Marta dal camerlengo, il cardinale Kevin Farrell. Accanto a lui il cardinale Segretario di Stato Pietro Parolin, il Sostituto mons. Edgar Pena Parra e il Maestro delle Cerimonie mons. Diego Ravelli: “Carissimi fratelli e sorelle - le parole di Farrell - con profondo dolore devo annunciare la morte di nostro Santo Padre Francesco. Alle ore 7:35 di questa mattina il Vescovo di Roma, Francesco, è tornato alla casa del Padre. La sua vita tutta intera è stata dedicata al servizio del Signore e della Sua chiesa. Ci ha insegnato a vivere i valori del Vangelo con fedeltà, coraggio ed amore universale, in modo particolare a favore dei più poveri e emarginati. Con immensa gratitudine per il suo esempio di vero discepolo del Signore Gesù, raccomandiamo l'anima di Papa Francesco all'infinito amore misericordioso di Dio Uno e Trino.” Emorragia cerebrale possibile causa della morte. Ieri, a Pasqua, l'ultima apparizione davanti a 35mila fedeli. Bandiere a mezz'asta a Palazzo Chigi, Camera, Senato e Quirinale. Ma anche ambasciate e caserme. Migliaia in preghiera a piazza San Pietro.Marco Ansaldo è considerato uno dei più autorevoli esperti di Turchia, paese dove da più di trent'anni viaggia, lavorando e abitando a Istanbul. Si è occupato di politica e cultura scrivendo centinaia di reportage e una lunga serie di interviste con i maggiori protagonisti, dal presidente Recep Tayyip Erdogan al premio Nobel per la letteratura Orhan Pamuk. Ha redatto le voci dell'Enciclopedia Treccani e del Dizionario Utet, e inventato il Foro di dialogo intergovernativo Italia-Turchia. Da Istanbul ha collaborato anche con La7 al programma Atlantide.Per sette anni, dal 2010 al 2016, è stato vaticanista di Repubblica, dove ha seguito i pontificati di Papa Benedetto XVI e di Papa Francesco. Ha seguito due Conclavi, decine di viaggi papali, il caso dei Corvi in Vaticano e gli scandali Vatileaks 1 e 2. Da più di dieci anni scrive di Vaticano per il prestigioso settimanale tedesco Die Zeit.Genovese, è ambasciatore all'estero dell'U.C. Sampdoria e ha scritto tre testi sul calcio; per anni disc-jockey e conduttore radiofonico, allievo di tre Conservatori di Stato si occupa anche di musica collaborando oggi con Rai Radio 3.È curatore di cicli e convegni culturali e tiene conferenze in organismi e istituzioni sui vari temi di cui è esperto. Ha scritto una quindicina di libri e oggi vive fra Istanbul, Genova e Roma. È probiviro della Federazione Nazionale della Stampa Italiana.IL POSTO DELLE PAROLEascoltare fa pensarewww.ilpostodelleparole.itDiventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.

#RosieRivera REACCIONA a liberación de #TrinoMarín, exesposo de #JenniRivera: Quien 4BUSÓ de ella y de #ChiquisRivera, #Shakira hizo HISTORIA con el público en la CDMX, #AislinnDerbez DESMIENTE pelea con #JoséEduardo por NO asistir al BAUTIZO de Tessa. Esto y más en el programa completo De Primera ManoSee omnystudio.com/listener for privacy information.

#RosieRivera REACCIONA a liberación de #TrinoMarín, exesposo de #JenniRivera y que estuvo 18 años en la cárcel por el abuso hacia ella y a #ChiquisRivera.See omnystudio.com/listener for privacy information.

EXCLUSIVA #TrinoMarín, exesposo de #JenniRivera quedó en LIBERTAD tras acusaciones por presunto 4BUS0 SEXU4l por #RosieRivera y sus hijas, salió el 26 de noviembre 2024, #AddisTuñón nos da toda la informaciónSee omnystudio.com/listener for privacy information.

Tiktok/Internet sensations, @TrinoxAdam1026 stopped by the After Mess Podcast to talk about coming out, crying during "Pretty Woman," parenting styles, meeting Katy Perry and so so much more. Amazing convo w/ our guys on this one be sure to go show them some love on IG & Tiktok @trinoxadam

El trino de Trump a Zelensky ha preocupado a todo el mundo. Sin embargo, Putin celebró lo dicho en este, ¿qué significa esto en el ámbito internacional?

-#EEUU y #Rusia comienzan las negociaciones de paz en Ucrania SIN #Ucrania ni #Europa. -#Brasil se adhiere a la #OPEP+-#DonaldTrump le da dos semanas a las Escuelas y Universidades de su País para que terminen todas sus "iniciativas de diversidad" ó perderán su financiamiento federal.

Resumen informativo con las noticias más destacadas de Colombia del sábado 15 de febrero de 2025 a las siete de la noche.

Manfred Moser is joined by Peter Kosztolanyi to talk about the origins, current status, and future of the new Preview Web UI for Trino, before we play around with it in a demo.More info at https://trino.io/episodes/70

La falta de claridad sobre el motivo del trino ha dejado espacio para especulaciones, lo que Matamoros considera "irresponsable" en el contexto de la seguridad nacional.See omnystudio.com/listener for privacy information.

Resumen informativo con las noticias más destacadas de Colombia del lunes 03 de febrero de 2025 a las nueve de la noche.

In this episode, we explore the cutting-edge world of data infrastructure with Justin Borgman, CEO of Starburst — a company transforming data analytics through its open-source project, Trino, and empowering industry giants like Netflix, Airbnb, and LinkedIn. Justin takes us through Starburst's journey from a Yale University spin-out to a leading force in data innovation, discussing the shift from data lakes to lakehouses, the rise of open formats like Iceberg as the future of data storage, and the role of AI in modern data applications. We also dive into how Starburst is staying ahead by balancing on-prem and cloud offerings while emphasizing the value of optionality in a rapidly evolving, data-driven landscape. Starburst Data Website - https://www.starburst.io X/Twitter - https://x.com/starburstdata Justin Borgman LinkedIn - https://www.linkedin.com/in/justinborgman X/Twitter - https://x.com/justinborgman FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck (00:00) Intro (01:32) What is Starburst? (02:32) Understanding the data layer (05:06) Justin Borgman's story before Starburst (10:41) The evolution of Presto into Trino (13:20) Lakehouse vs. data lake vs. data warehouse (22:06) Why Starburst backed the lakehouse from the start (23:20) Starburst Enterprise (27:31) Cloud vs. on-prem (29:10) Starburst Galaxy (31:23) Dell Data Lakehouse (32:13) Starburst's data architecture explained (38:30) The rise of data apps (38:54) Starburst AML (40:41) “We actually built the Galaxy twice” (43:13) Managing multiple products at scale (45:14) “We founded the company on the idea of optionality” (47:20) Iceberg (48:01) How open-source acquisitions work (51:39) Why Snowflake embraced Iceberg (53:15) Data mesh (55:31) AI at Starburst (57:16) Key takeaways from go-to-market strategies (01:01:18) Lessons from the Dell partnership (01:04:40) Predictions for 2025

29th January, 2025 – In this episode of “Saint of the Week,” host Sabrina looks at Blessed Archangela Girlani who was born in Trino, in northern Italy, in 1460, and baptized Eleanor. Though planning to become a Benedictine nun she was thwarted in her desire by her horse – the animal refused to carry her […] L'articolo E173 | Saint of the Week – Sabrina McKiernan – Blessed Archangela Girlani  proviene da Radio Maria.

With his soul-stirring tunes and spellbinding stories woven into his lyrics, Trino has a magical way of connecting with listeners on a deep level. His social media is like a backstage pass to his creative escapades, packed with behind-the-scenes peeks, live jams, and artist collaborations. Fans can look forward to a steady stream of updates and exclusive goodies that showcase his ever-evolving sound and artistic flair. Keep your ears perked for upcoming drops that promise to be both groundbreaking and oh-so-personal, as Trino fearlessly ventures into new musical territories!

Valora el hecho de que tengamos una fe clara y viva en el misterio de Dios Uno y Trino porque su costo fue la sangre de Nuestro Señor Jesucristo; no permitas que se pierda, al contrario que sea vida en tu vida.

Unstructured data is the next frontier for AI: think video, audio, and more. David Nicholson is joined by Dell Technologies' Vice President of Product Management for Artificial Intelligence and Data Management Chad Dunn for a conversation on the strategic importance of high-quality data and the dynamic capabilities of the Dell Data Lakehouse in facilitating effective AI workloads. Highlights include ⤵️ Data quality is paramount: "Garbage in, expensive garbage out" applies more than ever in the age of generative AI Dell's Data Lakehouse: This intelligent platform helps organizations extract, prepare, and analyze data for AI workloads, including both structured and unstructured data with tools like Apache Spark and Trino Customer experiences: The evolving landscape of data challenges in large enterprises Pushing the boundaries: Dell's approach to managing unstructured data and integrating AI Factory visions into Lakehouse functionalities  

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En este episodio conversamos con el arqueólogo Trino Escalante sobre su trabajo en los parques arqueológico en Mérida, Yucatán. Así como una hoja en el viento, estas son ideas transmitidas a la memoria. #CultivamosMemorias Síguenos en nuestras redes sociales Libreta Negra Mx TW: https://twitter.com/LibretaNegraMx FB: https://www.facebook.com/LibretaNegraMx/ IG: https://www.instagram.com/libretanegramx/ Apóyanos para continuar la labor de difusión y divulgación cultural. Paypal: https://www.paypal.com/donate/?hosted_button_id=NCGTRH8N57XFE Ko-Fi: https://ko-fi.com/libretanegramx Patreon: https://patreon.com/LibretaNegraMx?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=creatorshare_creator&utm_content=join_link #LaHojaSuelta #Podcast #Cultura

Lucas 1:26–38 Este relato nos muestra que la gracia soberana de Dios, la condescendencia de Cristo y el poder del Espíritu Santo no dependen de méritos humanos. La elección de María nos enseña que Dios elige a lo humilde e inmerecido para mostrar Su gloria. Como María, somos llamados a responder a la gracia de Dios con fe y obediencia, confiando en Su poder soberano para obrar en y a través de nuestras vidas.

In this engaging conversation at the All Things Open conference, Tim Spann, Principal Developer Advocate at Zilliz, discusses the importance of community collaboration in advancing AI technologies. He emphasizes the need for diverse perspectives in solving complex problems and highlights his work with the Milvus open source vector database. Tim also explains the evolving landscape of retrieval augmented generation (RAG) and its applications and shares insights into the future of AI development. The conversation concludes on a lighter note with Tim describing his creative use of Milvus in a fun Halloween project to catalog and identify ghosts. 00:00 Introduction 00:41 Meet Tim Spann: Principal Developer Advocate 01:35 The Importance of Community in AI 02:56 Advanced RAG and Multimodal Models 06:17 The Future of Agentic RAG 09:04 Challenges and Excitement in AI Development 13:35 Building AI the Right Way 17:50 Fun with AI: Capturing Ghosts 19:24 Conclusion and Final Thoughts   Guest: Tim Spann is a Principal Developer Advocate for Zilliz and Milvus. He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Principal Developer Advocate at Cloudera, Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.

Trino versus Apache Spark

Ismael Ortiz Flores –oficial de la SSC– actualiza sobre el caos vial en la México-Querétaro, tras una volcadura en Tlalpan. Transportistas deben tomar precauciones ante 10 kilómetros de fila cerca de Teoloyucan. Dalila Ramírez –corresponsal de Grupo Imagen en Toluca– reporta sobre el Operativo Enjambre, que ya capturó a directivos de seguridad y apunta a 10 alcaldes y jefes de policía mexiquense. Las autoridades intensifican la lucha contra la corrupción. Paloma Sánchez Ramos –senadora del PRI– comenta sobre la aprobación de la reforma constitucional que prohíbe el maltrato animal en México. Una victoria para los derechos de los animales. Fernando Anzures –CEO de EXMA Global– adelanta los detalles del evento del año en marketing y negocios, EXMA Global. Este 25 y 26 de noviembre, el Frontón México será el epicentro de grandes ideas. Trino Trino –ilustrador, caricaturista y monero– presenta “Las crónicas marcianas de Trino,” un libro que mezcla alienígenas y humanidad con humor absurdo. Ríe y reflexiona con este monero en su mejor momento creativo. Programa transmitido el 22 de noviembre de 2024. Escucha el Noticiero de Nacho Lozano, en vivo de lunes a viernes de 1:00 p.m. a 2:00 p.m. por el 105.3 de FM. Esta es una producción de Radio Chilango.

Trino Trino –ilustrador, caricaturista y monero– presenta “Las crónicas marcianas de Trino,” un libro que mezcla alienígenas y humanidad con humor absurdo. Ríe y reflexiona con este monero en su mejor momento creativo. Programa transmitido el 22 de noviembre de 2024. Escucha el Noticiero de Nacho Lozano, en vivo de lunes a viernes de 1:00 p.m. a 2:00 p.m. por el 105.3 de FM. Esta es una producción de Radio Chilango.

Trino, ilustrador, caricaturista y monero

   Según cuenta Eusebio, Teófilo de Antioquía (en griego, Θεόφιλου Αντιοχείας, f. 183) fue el sexto obispo de Antioquía. De sus escritos se deduce que nació en una localidad cercana al río Éufrates, de familia pagana, y que recibió educación helenística (entendiendo por helenística la mezcla de la cultura griega con otras por las conquistas de Alejandro Magno). Teófilo se convirtió al cristianismo siendo ya de edad madura. Fue el primer obispo que utilizó la palabra "Trinidad".   

  Según cuenta Eusebio, Teófilo de Antioquía (en griego, Θεόφιλου Αντιοχείας, f. 183) fue el sexto obispo de Antioquía. De sus escritos se deduce que nació en una localidad cercana al río Éufrates, de familia pagana, y que recibió educación helenística (entendiendo por helenística la mezcla de la cultura griega con otras por las conquistas de Alejandro Magno). Teófilo se convirtió al cristianismo siendo ya de edad madura. Fue el primer obispo que utilizó la palabra "Trinidad".  

More details at https://trino.io/episodes/67

 Según cuenta Eusebio, Teófilo de Antioquía (en griego, Θεόφιλου Αντιοχείας, f. 183) fue el sexto obispo de Antioquía. De sus escritos se deduce que nació en una localidad cercana al río Éufrates, de familia pagana, y que recibió educación helenística (entendiendo por helenística la mezcla de la cultura griega con otras por las conquistas de Alejandro Magno). Teófilo se convirtió al cristianismo siendo ya de edad madura. Fue el primer obispo que utilizó la palabra "Trinidad". 

Nuova puntata di Prima o Poi con Paolo Barberis e Max Ciociola

Esteban Gaviria • Selected Scriptures • Escuela Dominical

Trino & Adam Talk All: Childhood Traumas, Coming Out, Parents, Relationships, CHISME & MORE!! • Don't forget to subscribe to the podcast for free wherever you're listening or by using this link: https://bit.ly/NochedePendejadasPodcast • If you like the show, telling a friend about it would be helpful! You can text, email, Tweet, or send this link to a friend: https://bit.ly/NochedePendejadasPodcast Follow Alannized on IG Follow Alannized on TikTok Follow Alannized on Twitter  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Manfred is joined by Wren AI team members and contributors to talk about the new AI-powered, text to SQL tool and its great support for Trino.More details at https://trino.io/episodes/66

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El exalcalde de Medellín se refirió en 6AM a la reciente decisión de la Procuraduría de suspenderlo e inhabilitarlo por seis meses

Summary This episode features an insightful conversation with Petr Janda, the CEO and founder of Synq. Petr shares his journey from being an engineer to founding Synq, emphasizing the importance of treating data systems with the same rigor as engineering systems. He discusses the challenges and solutions in data reliability, including the need for transparency and ownership in data systems. Synq's platform helps data teams manage incidents, understand data dependencies, and ensure data quality by providing insights and automation capabilities. Petr emphasizes the need for a holistic approach to data reliability, integrating data systems into broader business processes. He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Petr Janda about Synq, a data reliability platform focused on leveling up data teams by supporting a culture of engineering rigor Interview Introduction How did you get involved in the area of data management? Can you describe what Synq is and the story behind it? Data observability/reliability is a category that grew rapidly over the past ~5 years and has several vendors focused on different elements of the problem. What are the capabilities that you saw as lacking in the ecosystem which you are looking to address? Operational/infrastructure engineers have spent the past decade honing their approach to incident management and uptime commitments. How do those concepts map to the responsibilities and workflows of data teams? Tooling only plays a small part in SLAs and incident management. How does Synq help to support the cultural transformation that is necessary? What does an on-call rotation for a data engineer/data platform engineer look like as compared with an application-focused team? How does the focus on data assets/data products shift your approach to observability as compared to a table/pipeline centric approach? With the focus on sharing ownership beyond the boundaries on the data team there is a strong correlation with data governance principles. How do you see organizations incorporating Synq into their approach to data governance/compliance? Can you describe how Synq is designed/implemented? How have the scope and goals of the product changed since you first started working on it? For a team who is onboarding onto Synq, what are the steps required to get it integrated into their technology stack and workflows? What are the types of incidents/errors that you are able to identify and alert on? What does a typical incident/error resolution process look like with Synq? What are the most interesting, innovative, or unexpected ways that you have seen Synq used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Synq? When is Synq the wrong choice? What do you have planned for the future of Synq? Contact Info LinkedIn (https://www.linkedin.com/in/petr-janda/?originalSubdomain=dk) Substack (https://substack.com/@petrjanda) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com) with your story. Links Synq (https://www.synq.io/) Incident Management (https://www.pagerduty.com/resources/learn/what-is-incident-management/) SLA == Service Level Agreement (https://en.wikipedia.org/wiki/Service-level_agreement) Data Governance (https://en.wikipedia.org/wiki/Data_governance) Podcast Episode (https://www.dataengineeringpodcast.com/nicola-askham-practical-data-governance-episode-428) PagerDuty (https://www.pagerduty.com/) OpsGenie (https://www.atlassian.com/software/opsgenie) Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/) SQLMesh (https://sqlmesh.readthedocs.io/en/stable/) Podcast Episode (https://www.dataengineeringpodcast.com/sqlmesh-open-source-dataops-episode-380) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou Interview Introduction How did you get involved in the area of data management? Can you describe what Microsoft Fabric is and the story behind it? Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend? Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution? What are the elements of Fabric that were engineered specifically for the service? What are the most interesting/complicated integration challenges? How has your prior experience with Ahana and Presto informed your current work at Microsoft? AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine? What are the challenges in terms of safety and reliability? What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically? When is Fabric the wrong choice? What do you have planned for the future of data lake analytics? Contact Info LinkedIn (https://www.linkedin.com/in/diptiborkar/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com) with your story. Links Microsoft Fabric (https://www.microsoft.com/microsoft-fabric) Ahana episode (https://www.dataengineeringpodcast.com/ahana-presto-cloud-data-lake-episode-217) DB2 Distributed (https://www.ibm.com/docs/en/db2/11.5?topic=managers-designing-distributed-databases) Spark (https://spark.apache.org/) Presto (https://prestodb.io/) Azure Data (https://azure.microsoft.com/en-us/products#analytics) MAD Landscape (https://mattturck.com/mad2024/) Podcast Episode (https://www.dataengineeringpodcast.com/mad-landscape-2023-data-infrastructure-episode-369) ML Podcast Episode (https://www.themachinelearningpodcast.com/mad-landscape-2023-ml-ai-episode-21) Tableau (https://www.tableau.com/) dbt (https://www.getdbt.com/) Medallion Architecture (https://dataengineering.wiki/Concepts/Medallion+Architecture) Microsoft Onelake (https://learn.microsoft.com/fabric/onelake/onelake-overview) ORC (https://orc.apache.org/) Parquet (https://parquet.incubator.apache.org) Avro (https://avro.apache.org/) Delta Lake (https://delta.io/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209) Hadoop (https://hadoop.apache.org/) PowerBI (https://www.microsoft.com/power-platform/products/power-bi) Podcast Episode (https://www.dataengineeringpodcast.com/power-bi-business-intelligence-episode-154) Velox (https://velox-lib.io/) Gluten (https://gluten.apache.org/) Apache XTable (https://xtable.apache.org/) GraphQL (https://graphql.org/) Formula 1 (https://www.formula1.com/) McLaren (https://www.mclaren.com/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Stripe is a company that relies on data to power their products and business. To support that functionality they have invested in Trino and Iceberg for their analytical workloads. In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Kevin Liu about his use of Trino and Iceberg for Stripe's data lakehouse Interview Introduction How did you get involved in the area of data management? Can you describe what role Trino and Iceberg play in Stripe's data architecture? What are the ways in which your job responsibilities intersect with Stripe's lakehouse infrastructure? What were the requirements and selection criteria that led to the selection of that combination of technologies? What are the other systems that feed into and rely on the Trino/Iceberg service? what kinds of questions are you answering with table metadata what use case/team does that support comparative utility of iceberg REST catalog What are the shortcomings of Trino and Iceberg? What are the most interesting, innovative, or unexpected ways that you have seen Iceberg/Trino used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Stripe's data infrastructure? When is a lakehouse on Trino/Iceberg the wrong choice? What do you have planned for the future of Trino and Iceberg at Stripe? Contact Info Substack (https://kevinjqliu.substack.com) LinkedIn (https://www.linkedin.com/in/kevinjqliu) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com) with your story. Links Trino (https://trino.io/) Iceberg (https://iceberg.apache.org/) Stripe (https://stripe.com/) Spark (https://spark.apache.org/) Redshift (https://aws.amazon.com/redshift/) Hive Metastore (https://cwiki.apache.org/confluence/display/hive/design#Design-Metastore) Python Iceberg (https://py.iceberg.apache.org/) Python Iceberg REST Catalog (https://github.com/kevinjqliu/iceberg-rest-catalog) Trino Metadata Table (https://trino.io/docs/current/connector/iceberg.html#metadata-tables) Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57) Tabular (https://tabular.io/) Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363) Delta Table (https://delta.io/) Podcast Episode (https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/) Databricks Unity Catalog (https://www.databricks.com/product/unity-catalog) Starburst (https://www.starburst.io/) AWS Athena (https://aws.amazon.com/athena/) Kevin Trinofest Presentation (https://trino.io/blog/2023/07/19/trino-fest-2023-stripe.html) Alluxio (https://www.alluxio.io/) Podcast Episode (https://www.dataengineeringpodcast.com/alluxio-distributed-storage-episode-70) Parquet (https://parquet.incubator.apache.org/) Hudi (https://hudi.apache.org/) Trino Project Tardigrade (https://trino.io/blog/2022/05/05/tardigrade-launch.html) Trino On Ice (https://www.starburst.io/blog/iceberg-table-partitioning/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. To address this shortcoming Datorios created an observability platform for Flink that brings visibility to the internals of this popular stream processing system. In this episode Ronen Korman and Stav Elkayam discuss how the increased understanding provided by purpose built observability improves the usefulness of Flink. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments (https://www.dataengineeringpodcast.com/codecomments) today to subscribe. My thanks to the team at Code Comments for their support. Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Ronen Korman and Stav Elkayam about pulling back the curtain on your real-time data streams by bringing intuitive observability to Flink streams Interview Introduction How did you get involved in the area of data management? Can you describe what Datorios is and the story behind it? Data observability has been gaining adoption for a number of years now, with a large focus on data warehouses. What are some of the unique challenges posed by Flink? How much of the complexity is due to the nature of streaming data vs. the architectural realities of Flink? How has the lack of visibility into the flow of data in Flink impacted the ways that teams think about where/when/how to apply it? How have the requirements of generative AI shifted the demand for streaming data systems? What role does Flink play in the architecture of generative AI systems? Can you describe how Datorios is implemented? How has the design and goals of Datorios changed since you first started working on it? How much of the Datorios architecture and functionality is specific to Flink and how are you thinking about its potential application to other streaming platforms? Can you describe how Datorios is used in a day-to-day workflow for someone building streaming applications on Flink? What are the most interesting, innovative, or unexpected ways that you have seen Datorios used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datorios? When is Datorios the wrong choice? What do you have planned for the future of Datorios? Contact Info Ronen LinkedIn (https://www.linkedin.com/in/ronen-korman/) Stav LinkedIn (https://www.linkedin.com/in/stav-elkayam-118a2795/?originalSubdomain=il) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com) with your story. Links Datorios (https://datorios.com/) Apache Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57) ChatGPT-4o (https://openai.com/index/hello-gpt-4o/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Modern businesses aspire to be data driven, and technologists enjoy working through the challenge of building data systems to support that goal. Data governance is the binding force between these two parts of the organization. Nicola Askham found her way into data governance by accident, and stayed because of the benefit that she was able to provide by serving as a bridge between the technology and business. In this episode she shares the practical steps to implementing a data governance practice in your organization, and the pitfalls to avoid. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments (https://www.dataengineeringpodcast.com/codecomments) today to subscribe. My thanks to the team at Code Comments for their support. Your host is Tobias Macey and today I'm interviewing Nicola Askham about the practical steps of building out a data governance practice in your organization Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the scope and boundaries of data governance in an organization? At what point does a lack of an explicit governance policy become a liability? What are some of the misconceptions that you encounter about data governance? What impact has the evolution of data technologies had on the implementation of governance practices? (e.g. number/scale of systems, types of data, AI) Data governance can often become an exercise in boiling the ocean. What are the concrete first steps that will increase the success rate of a governance practice? Once a data governance project is underway, what are some of the common roadblocks that might derail progress? What are the net benefits to the data team and the organization when a data governance practice is established, active, and healthy? What are the most interesting, innovative, or unexpected ways that you have seen data governance applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data governance/training/coaching? What are some of the pitfalls in data governance? What are some of the future trends in data governance that you are excited by? Are there any trends that concern you? Contact Info Website (https://www.nicolaaskham.com/) LinkedIn (https://www.linkedin.com/in/nicolaaskham/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Website (https://www.nicolaaskham.com/) Master Data Management (https://en.wikipedia.org/wiki/Master_data_management) Cartesian Join (https://www.geeksforgeeks.org/cartesian-join/) DAMA == Data Management Community (https://www.dama.org/) DMBOK == Data Management Body of Knowledge (https://www.dama.org/cpages/body-of-knowledge) DAMA DMBOK Wheel (https://www.dama.org/cpages/dmbok-2-wheel-images) CDMP (Certified Data Management Professional) Exam (https://www.dama.org/cpages/cdmp-information) Data Mesh (https://www.datamesh-architecture.com/) Data Governance First Steps Checklist (https://www.nicolaaskham.com/free-data-governance-checklist) The Never Normal (https://www.linkedin.com/newsletters/the-never-normal-6862024032934477824/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Any software system that survives long enough will require some form of migration or evolution. When that system is responsible for the data layer the process becomes more challenging. Sriram Panyam has been involved in several projects that required migration of large volumes of data in high traffic environments. In this episode he shares some of the valuable lessons that he learned about how to make those projects successful. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments (https://www.dataengineeringpodcast.com/codecomments) today to subscribe. My thanks to the team at Code Comments for their support. Your host is Tobias Macey and today I'm interviewing Sriram Panyam about his experiences conducting large scale data migrations and the useful strategies that he learned in the process Interview Introduction How did you get involved in the area of data management? Can you start by sharing some of your experiences with data migration projects? As you have gone through successive migration projects, how has that influenced the ways that you think about architecting data systems? How would you categorize the different types and motivations of migrations? How does the motivation for a migration influence the ways that you plan for and execute that work? Can you talk us through one or two specific projects that you have taken part in? Part 1: The Triggers Section 1: Technical Limitations triggering Data Migration Scaling bottlenecks: Performance issues with databases, storage, or network infrastructure Legacy compatibility: Difficulties integrating with modern tools and cloud platforms System upgrades: The need to migrate data during major software changes (e.g., SQL Server version upgrade) Section 2: Types of Migrations for Infrastructure Focus Storage migration: Moving data between systems (HDD to SSD, SAN to NAS, etc.) Data center migration: Physical relocation or consolidation of data centers Virtualization migration: Moving from physical servers to virtual machines (or vice versa) Section 3: Technical Decisions Driving Data Migrations End-of-life support: Forced migration when older software or hardware is sunsetted Security and compliance: Adopting new platforms with better security postures Cost Optimization: Potential savings of cloud vs. on-premise data centers Part 2: Challenges (and Anxieties) Section 1: Technical Challenges Data transformation challenges: Schema changes, complex data mappings Network bandwidth and latency: Transferring large datasets efficiently Performance testing and load balancing: Ensuring new systems can handle the workload Live data consistency: Maintaining data integrity while updates occur in the source system Minimizing Lag: Techniques to reduce delays in replicating changes to the new system Change data capture: Identifying and tracking changes to the source system during migration Section 2: Operational Challenges Minimizing downtime: Strategies for service continuity during migration Change management and rollback plans: Dealing with unexpected issues Technical skills and resources: In-house expertise/data teams/external help Section 3: Security & Compliance Challenges Data encryption and protection: Methods for both in-transit and at-rest data Meeting audit requirements: Documenting data lineage & the chain of custody Managing access controls: Adjusting identity and role-based access to the new systems Part 3: Patterns Section 1: Infrastructure Migration Strategies Lift and shift: Migrating as-is vs. modernization and re-architecting during the move Phased vs. big bang approaches: Tradeoffs in risk vs. disruption Tools and automation: Using specialized software to streamline the process Dual writes: Managing updates to both old and new systems for a time Change data capture (CDC) methods: Log-based vs. trigger-based approaches for tracking changes Data validation & reconciliation: Ensuring consistency between source and target Section 2: Maintaining Performance and Reliability Disaster recovery planning: Failover mechanisms for the new environment Monitoring and alerting: Proactively identifying and addressing issues Capacity planning and forecasting growth to scale the new infrastructure Section 3: Data Consistency and Replication Replication tools - strategies and specialized tooling Data synchronization techniques, eg Pros and cons of different methods (incremental vs. full) Testing/Verification Strategies for validating data correctness in a live environment Implication of large scale systems/environments Comparison of interesting strategies: DBLog, Debezium, Databus, Goldengate etc What are the most interesting, innovative, or unexpected approaches to data migrations that you have seen or participated in? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data migrations? When is a migration the wrong choice? What are the characteristics or features of data technologies and the overall ecosystem that can reduce the burden of data migration in the future? Contact Info LinkedIn (https://www.linkedin.com/in/srirampanyam/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links DagKnows (https://dagknows.com) Google Cloud Dataflow (https://cloud.google.com/dataflow) Seinfeld Risk Management (https://www.youtube.com/watch) ACL == Access Control List (https://en.wikipedia.org/wiki/Access-control_list) LinkedIn Databus - Change Data Capture (https://github.com/linkedin/databus) Espresso Storage (https://engineering.linkedin.com/data-replication/open-sourcing-databus-linkedins-low-latency-change-data-capture-system) HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html) Kafka (https://kafka.apache.org/) Postgres Replication Slots (https://www.postgresql.org/docs/current/logical-replication.html) Queueing Theory (https://en.wikipedia.org/wiki/Queueing_theory) Apache Beam (https://beam.apache.org/) Debezium (https://debezium.io/) Airbyte (https://airbyte.com/) Fivetran (fivetran.com) Designing Data Intensive Applications (https://amzn.to/4aAztR1) by Martin Kleppman (https://martin.kleppmann.com/) (affiliate link) Vector Databases (https://en.wikipedia.org/wiki/Vector_database) Pinecone (https://www.pinecone.io/) Weaviate (https://www.weveate.io/) LAMP Stack (https://en.wikipedia.org/wiki/LAMP_(software_bundle)) Netflix DBLog (https://arxiv.org/abs/2010.12597) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. Unfortunately this often turns into an exercise in frustration for everyone involved due to complex workflows and hard-to-understand dashboards. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. In this episode they share their journey through the fast-moving landscape of generative AI and unpack the difference between an AI chatbot and an AI agent. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments (https://www.dataengineeringpodcast.com/codecomments) today to subscribe. My thanks to the team at Code Comments for their support. Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Ryan Janssen and Paul Blankley about their experiences building AI powered agents for interacting with your data Interview Introduction How did you get involved in data? In AI? Can you describe what Zenlytic is and the role that AI is playing in your platform? What have been the key stages in your AI journey? What are some of the dead ends that you ran into along the path to where you are today? What are some of the persistent challenges that you are facing? So tell us more about data agents. Firstly, what are data agents and why do you think they're important? How are data agents different from chatbots? Are data agents harder to build? How do you make them work in production? What other technical architectures have you had to develop to support the use of AI in Zenlytic? How have you approached the work of customer education as you introduce this functionality? What are some of the most interesting or erroneous misconceptions that you have heard about what the AI can and can't do? How have you balanced accuracy/trustworthiness with user experience and flexibility in the conversational AI, given the potential for these models to create erroneous responses? What are the most interesting, innovative, or unexpected ways that you have seen your AI agent used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI agent for business intelligence? When is an AI agent the wrong choice? What do you have planned for the future of AI in the Zenlytic product? Contact Info Ryan LinkedIn (https://www.linkedin.com/in/janssenryan) Paul LinkedIn (https://www.linkedin.com/in/paulblankley/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Zenlytic (https://www.zenlytic.com/) Podcast Episode (https://www.dataengineeringpodcast.com/zenlytic-self-serve-business-intelligence-episode-371) Attention is all you need (https://arxiv.org/abs/1706.03762) Transformers (https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)) BERT (https://en.wikipedia.org/wiki/BERT_(language_model)) The Bitter Lesson (http://www.incompleteideas.net/IncIdeas/BitterLesson.html) Richard Sutton PID Loops (https://en.wikipedia.org/wiki/Proportional%E2%80%93integral%E2%80%93derivative_controller) AutoGPT (https://github.com/Significant-Gravitas/AutoGPT) Devin.ai (https://www.cognition.ai/introducing-devin) Google Gemini (https://gemini.google.com/) Anthropic Claude (https://www.anthropic.com/claude) OpenAI Code Interpreter (https://platform.openai.com/docs/assistants/tools/code-interpreter) Edward Tufte (https://www.edwardtufte.com/tufte/books_vdqi) Looker ActionHub (https://developers.looker.com/actions/overview/) OAuth (https://oauth.net/2/) GitHub Copilot (https://github.com/features/copilot) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Building a data platform is a substrantial engineering endeavor. Once it is running, the next challenge is figuring out how to address release management for all of the different component parts. The services and systems need to be kept up to date, but so does the code that controls their behavior. In this episode your host Tobias Macey reflects on his current challenges in this area and some of the factors that contribute to the complexity of the problem. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments (https://www.dataengineeringpodcast.com/codecomments) today to subscribe. My thanks to the team at Code Comments for their support. Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I want to talk about my experiences managing the QA and release management process of my data platform Interview Introduction As a team, our overall goal is to ensure that the production environment for our data platform is highly stable and reliable. This is the foundational element of establishing and maintaining trust with the consumers of our data. In order to support this effort, we need to ensure that only changes that have been tested and verified are promoted to production. Our current challenge is one that plagues all data teams. We want to have an environment that mirrors our production environment that is available for testing, but it's not feasible to maintain a complete duplicate of all of the production data. Compounding that challenge is the fact that each of the components of our data platform interact with data in slightly different ways and need different processes for ensuring that changes are being promoted safely. Contact Info LinkedIn () Website (https://www.dataengineeringpodcast.com) Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com) with your story. Links Data Platforms and Leaky Abstractions Episode (https://www.dataengineeringpodcast.com/abstractions-and-technical-debt-episode-374) Building A Data Platform From Scratch (https://www.dataengineeringpodcast.com/designing-a-lakehouse-from-scratch-episode-354) Airbyte (https://airbyte.com/) Podcast Episode (https://www.dataengineeringpodcast.com/airbyte-open-source-data-integration-episode-173/) Trino (https://trino.io/) dbt (https://www.getdbt.com/) Starburst Galaxy (https://www.starburst.io/platform/starburst-galaxy/) Superset (https://superset.apache.org/) Dagster (https://dagster.io/) LakeFS (https://lakefs.io/) Podcast Episode (https://www.dataengineeringpodcast.com/lakefs-data-lake-versioning-episode-157) Nessie (https://projectnessie.org/) Podcast Episode (https://www.dataengineeringpodcast.com/nessie-data-lakehouse-data-versioning-episode-416) Iceberg (https://iceberg.apache.org/) Snowflake (https://www.snowflake.com/en/) LocalStack (https://www.localstack.cloud/) DSL == Domain Specific Language (https://en.wikipedia.org/wiki/Domain-specific_language) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Summary Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human" Interview Introduction How did you get involved in machine learning? Can you start by unpacking the idea of "human-like" AI? How does that contrast with the conception of "AGI"? The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment? The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models? What are the opportunities and limitations of causal modeling techniques for generalized AI models? As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability? What are the practical/architectural methods necessary to build more cognitive AI systems? How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications? What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems? When is cognitive AI the wrong choice? What do you have planned for the future of cognitive AI applications at Aigo? Contact Info LinkedIn (https://www.linkedin.com/in/vosspeter/) Website (http://optimal.org/voss.html) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Aigo.ai (https://aigo.ai/) Artificial General Intelligence (https://aigo.ai/what-is-real-agi/) Cognitive AI (https://aigo.ai/cognitive-ai/) Knowledge Graph (https://en.wikipedia.org/wiki/Knowledge_graph) Causal Modeling (https://en.wikipedia.org/wiki/Causal_model) Bayesian Statistics (https://en.wikipedia.org/wiki/Bayesian_statistics) Thinking Fast & Slow (https://amzn.to/3UJKsmK) by Daniel Kahneman (affiliate link) Agent-Based Modeling (https://en.wikipedia.org/wiki/Agent-based_model) Reinforcement Learning (https://en.wikipedia.org/wiki/Reinforcement_learning) DARPA 3 Waves of AI (https://www.darpa.mil/about-us/darpa-perspective-on-ai) presentation Why Don't We Have AGI Yet? (https://arxiv.org/abs/2308.03598) whitepaper Concepts Is All You Need (https://arxiv.org/abs/2309.01622) Whitepaper Hellen Keller (https://en.wikipedia.org/wiki/Helen_Keller) Stephen Hawking (https://en.wikipedia.org/wiki/Stephen_Hawking) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)

Trino & Adam Return to the Everynightnights Podcast with Snow Tha Product

Trino & Adam & Jose Caballero join Snow Tha Product on the Everynightnights Podcast