Trino Community Broadcast
Trino Community
Trino Community Broadcast is a show where we cover events and happenings within the open-source Trino community and show off some cool stuff about Trino. Learn more at https://trino.io
71: Fake it real good
Jan Waś teaches us about the new Faker connector and how you can use it to emulate data that does not exist on any storage, how you can shape it as you need, and how you can then learn real SQL, build real reports, and make some real charts - all with fake data.Details at https://trino.io/episodes/71
70: Previewing a new UI
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
69: Client protocol improvments
Show notes and more details at https://trino.io/episodes/69
68: Year of the Snake - Python UDFs
Show notes with more details at https://trino.io/episodes/68
67: Extra query speed with Exasol and Trino
More details at https://trino.io/episodes/67
66: Chat with Trino and Wren AI
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
65: Performance boosts
Manfred and Cole talk about recent releases in various features enhancing performance.Details in https://trino.io/episodes/65
64: Control with Open Policy Agent
Sebastian Bernauer and Sönke Liebau from Stackable join us to talk about their experience with using Open Policy Agent for access control with Trino.More details at https://trino.io/episodes/64
63: Querying Trino with JavaScript
Emily Sunaryo, DevRel intern at Starburst, joins us to talk about her experience learning Trino and starting to write a web application with JavaScript to query data in Trino.More details at https://trino.io/episodes/63
62: A lakehouse that simply works at Prezi
More details at https://trino.io/episodes/62
61: Trino powers business intelligence
Cole and Manfred talk with our guest Patrick Pichler from CreativeData about PowerBI and his open source Trino connector.More details in https://trino.io/episodes/61
60: Trino AI functions
We chat with Isa Inalcik from BestSecret about his proof of concepts for Trino functions calling AI/LLM systems.More details at https://trino.io/episodes/60
59: Querying Trino with Java and jOOQ
More details at https://trino.io/episodes/59
58: Understanding your users with Trino and Mitzu
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57: Seeing clearly with OpenTelemetry
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56: The vast possibilities of Trino and VAST
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55: Commander Bun Bun peeks at Peaka
Timestamps:- 0:00 Intro- 1:36 Releases 437-438- 4:12 Introducing Peaka- 8:07 An overview of Peaka- 16:02 The engineering of Peaka- 20:04 Connectors- 26:51 Peaka demo- 41:34 Managing catalogs and security- 51:06 Peaka wrap-up- 53:14 PR of the episode: Filesystem caching with Alluxio- 56:16 Outro
54: Trino 2023 wrapped
Martin, Manfred and Cole look back at the year 2023 for the Trino project and the Trino community.
53: Understanding your data with Coginiti and Trino
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52: Commander Bun Bun takes a bite out of Yugabyte
Timestamps:- 0:00 Intro- 1:48 Releases 428-430- 6:30 Introducing Denis Magda from @YugabyteDB - 7:56 JDBC, Trino's JDBC driver, and the Postgres connector- 14:08 Introducing YugabyteDB- 21:33 Demo time! Trino with PostgreSQL- 29:56 Demoing Trino with YugabyteDB- 44:57 Failover and resiliency- 56:05 Upcoming events and Trino Summit soon!
51: Trino cools off with PopSQL
When you're running a world-class, highly-performant query engine like Trino and investing time and resources into maintaining it, wouldn't it be great to treat your queries as a first-class, collaborative, versioned system, too?
Enter PopSQL, a SQL client that makes it easier than ever to work together on Trino queries, collaborate with teammates, and visualize your results in a quick and easy way. We'll talk about how PopSQL integrates with Trino, what it can do for Trino users, and dive into
50: A milestone for the Trino Community Broadcast and a look back
50 episodes is a big number! With the episodes coming once a month, this has been years in the making, so we're inviting back Trino co-founder Dain Sundstrom and Trino/Iceberg Developer Advocate Brian Olsen to catch up, share some stories, and talk about what it takes to maintain an open source project for so long.
49: Trino, Ibis, and wrangling Python in the SQL ecosystem
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47: Meet the new Trino maintainers
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46: Trino heats up with Ignite
- Intro Music: 0:00- Intro: 0:31- Trino releases 408-410: 2:02- Introducing the Ignite connector to Trino: 5:30- What is Ignite?: 7:30- Contributing the Ignite connector: 10:10- PR of the episode: 52:20
45: Trino swimming with the DolphinScheduler
DolphinScheduler is a popular Apache data workflow orchestrator that enables running complex data pipelines. They recently added a Trino integration and will be demonstrating how to use DolphinScheduler to enable a series of transformations on the data lakehouse with Trino.- Intro Music: 0:00- Intro: 0:31- Trino release 407: 13:22- What is workflow orchestration?: 21:12- Why do we need a workflow orchestration tool for building a data lake?: 31:07- What is Apache DolphinScheduler?: 37:35- Does D
44: Seeing clearly with Metabase
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43: Trino saves trips with Alluxio
Let's face it, out of all those petabytes of data you've been hoarding, only a small fraction of it is creating business value for you today. When you scan the same data multiple times and transfer it over the wire, you're wasting time, compute cycles, and ultimately money. This gets worse when you're pulling data across regions or clouds from disaggregate Trino clusters. In situations like these, caching solutions like Alluxio can make a tremendous impact on the latency and cost of your queries
42: Trino Summit 2022 recap
We're going to discuss all of the awesome sessions that happened during Trino Summit this year. Manfred, Cole, and I will be joined by Martin, Dain, Brian Zhan, and Claudius for their, perspective and what they found most interesting about the summit. We also dive into stats around the summit and some exciting topics discussed off-camera.We'll also dive into some key takeaways from the Trino Contributor Congregation that took place the day after and some of the topics we went over there.- Intro
41: Trino puts on its Hudi
Trino's initial use case was around replacing the Apache Hive runtime. As data lakes grew into prominence, it became clear that having a faster query engine didn't solve all problems. The Hive model itself was a huge bottleneck and didn't provide features that companies needed akin to data warehouses and databases. Apache Hudi is a new table format created out of Uber that aims to address many of these issues and usher in a new generation of data lake.Tune in as we speak to the Trino Hudi connec
40: Trino's as cold as Iceberg
Join us for this next episode of the broadcast, where we bring back Ryan Blue, the creator of Iceberg, to discuss some of the latest happenings in the Iceberg community. We also discuss and demo a bunch of new features that have come out in the Trino Iceberg connector. We also have a new guest, Tabular Developer Advocate Sam Redai, shedding light on this incredible community as well!Since the first episodes, Iceberg has finalized the v2 spec and added a lot of new features along the way. Likewis
39: Raft floats on Trino to federate silos
In this episode we sit down with engineers, Steve Morgan and Edward Morgan, to discuss how they use Trino at Raft. Raft provides consulting services and is particularly skilled at DevSecOps. One particular challenge they face is dealing with fragmented government infrastructure. In this episode, we dive in to learn how Trino enables Raft to supply government sector clients with a data fabric solution. Raft takes a special stance on using and contributing to open source solutions that run well on
38:Trino tacks on polymorphic table functions
We'll be doing a more focused look at a specific feature that's being added to Trino: polymorphic table functions. We're excited to talk about what they do, where we are so far, where we're going, and how you can leverage them to make Trino better than ever!Show Notes: https://trino.io/episodes/38.htmlShow Page: https://trino.io/broadcast/YouTube Video: https://www.youtube.com/watch?v=90e5WxhwNas
37: Trino powers up the community support
This episode covers will introduce the benefits of having the Trino community around the Trino project. What is the purpose of communities in tech projects? Would the product be successful without a community or anyone to maintain it?We introduce some new faces that will be stewards in our journey to growing the adoption of our favorite query engine, what each of them does, and how their work impacts you as a community member! Most importantly, you can learn how to get involved and help us learn
36: Trino plans to jump to Java 17
As Trino preps to jump to Java 17, we discuss the latest features added Java 11 to Java 17, talk with Martin through a few of the potential uses of new features like the Vector API, language improvements, and G1GC speedups, and finally, we will dive into discussing some of the features that we'll be implementing in the upcoming months under a new project in Trino!- Intro song: 00:00- Intro: 00:36- Releases: 8:17- Question of the episode: Will Trino be making a vectorized C++ version of Trino wor
35: Packaging and modernizing Trino
In our Trino Community Broadcast episode 35 we are catching up on recent releases 375, 376, 377, and 378. We then talk about how Trino is packaged as tarball, rpm, and docker container, what some of the differences are, and how you can customize either of them. Beyond we also look for your feedback and input on usage of the different packages. As a next step we chat about adopting Java 17 is standard for Trino, and then we get a demo of a new feature of the web UI.- Intro song: 00:00- Intro: 00
34: A big delta for Trino
News from the Trino releases 372, 373, and 374, and an update on Project Tardigrade are the start. Then we dive into the details of the new Delta Lake connector contributed to Trino by Starburst.- Intro song: 00:00- Intro: 00:37- Releases: 2:05- Project Tardigrade update: 9:21- Concept of the episode: A new connector for Delta Lake object storage. 18:37- Pull requests of the episode: Add Delta Lake connector and documentation. 26:10- Demo of the episode: Delta Lake connector in action. 29:14- Q
33: Trino becomes highly available for high demand
Goldman Sachs uses Trino to reduce last-mile ETL and provide a unified way of accessing data through federated joins. Making a variety of data sets from different sources available in one spot for our data science team was a tall order. Data must be quickly accessible to data consumers and systems like Trino must be reliable for users to trust this singular access point for their data.Join us on this next episode as we discuss with engineers from Goldman Sachs on how they integrated Trino and ac
32: Trino Tardigrade: Try, try, and never die
While Trino has been proven to run batch analytic workloads at scale, many have avoided long-running batch jobs in fear of query failure. Join this month's broadcast discussing the project introducing granular fault-tolerance to Trino. Codenamed Project Tardigrade, it is being thoughtfully crafted to maintain the speed advantage that Trino has over other query engines while increasing the resiliency of queries. We will discuss some of the design proposals being considered with Tardigrade enginee
31: Trinites II: Trino on AWS Kubernetes Service
In the previous Trinites installation (https://trino.io/episodes/24.html), we introduced Kubernetes (k8s) and its concepts and how to use k8s with Trino. After discussing Kubernetes, we did a demo showing how to deploy Trino on k8s.This round, we're going to take the same k8s concepts and dive in a little deeper to help newbies to k8s (KuberNewbies...Kubies?) to but deploy Trino to the cloud (specifically the most common cloud provider, AWS). This takes us from proving Trino is awesome to just y
30: Trino and dbt, a hot data mesh
Trino and dbt have become a common pattern due to Trino's ability to query data over mulitple data sources using ANSI SQL and dbt's capabilities to model robust pipelines using SQL and yaml files. José Cabada from Talkdesk joins us to discuss how Talkdesk uses Trino and dbt as central elements of their data platform to realize a data mesh. We then dive into why they are doing this and discuss what impacts a data mesh strategy has on the engineer's day-to-day work life.- Intro Song: 00:00- Intro
29: What is Trino?
This is a revision on our inaugural episode of "What is Presto?" where we dive again into the question of What is Trino? We'll cover this history, the architecture, and certainly discuss a few use cases, and how to get started with the project.- Intro Song: 00:00- Intro: 00:34- News: 8:26- Concept of the week: What is Trino?: 17:03- PR of the week: PR 8821 Add HTTP/S query event logger: 58:35- Question of the week: Does the Hive connector depend on the Hive runtime?: 1:02:43Show Notes: https:/
28: Autoscaling streaming ingestion to Trino with Pravega
Concept of the week: Event Stream abstractions and Pravega: 15:15Demo of the week: Event Stream abstractions and Pravega: 1:11:00PR of the week: Pravega presto-connector PR 49: 1:20:51Question of the week: What is the point of Trino Forum and what is the relationship to Trino Slack?: 1:26:07Show Notes: https://trino.io/episodes/28.htmlShow Page: https://trino.io/broadcast/
27: Trino gits to wade in the data LakeFS
- Intro Song: 00:00- Intro: 00:34- News: 5:53- Concept of the week: LakeFS and Git on Object Storage: 9:06- Demo of the week: Running Trino on LakeFS: 40:45- PR of the week: PR 8762 Add query error info to cluster overview page in web UI: 1:11:11- Question of the week: Why are deletes so limited in Trino?: 1:14:14Show Notes: https://trino.io/episodes/27.htmlShow Page: https://trino.io/broadcast/
26: Trino Discovers Data Catalogs with Amundsen
Trino is an enabler when it comes to giving you a single source of access across data sources. But how does anyone know where to find the data that they need? Many times, multiple teams have their own view of the world when it comes to the data they need but how can teams discover data beyond their day-to-day operations? Further questions like who owns the data and how do different data sources relate to eachother all can be answered by use of data discovery tools like Amundsen. Trino gets you q
25: Trino Going Through Changes
If you know Trino, you know it allows for flexible architectures that include many systems with varying use cases they support. We've come to accept this potpourri of systems as a general modus operandi for most businesses. Many times the data is copied to different systems to accomplish varying use cases from performance and data warehousing to merge cross cutting data into a single store. When copying data between systems, how do these systems stay in sync? We discuss Change Data Capture (CDC)
24: Trinetes I: Trino on Kubernetes
- Intro Song: 00:00- Intro: 00:33- News: 8:02- Concept of the week: K8s architecture: Containers, Pods, and kubelets: 14:27- PR of the week: PR 11 Merge contributor version of k8s charts with the community version: 55:20- Demo: Running the Trino charts with kubectl: 57:42Show Notes: https://trino.io/episodes/24.htmlShow Page: https://trino.io/broadcast/
23: Trino looking for patterns
- Intro Song: 00:00- Intro: 00:34- News: 5:18- Concept of the week: Row pattern matching and MATCH_RECOGNIZE: 14:26- PR of the week: PR 8348 Document row pattern recognition in window: 52:16- Demo: Showing MATCH_RECOGNIZE functionality by example: 57:13- Question of the week: How do you tag a list of rows with custom periodic rules?: 1:12:51Show Notes: https://trino.io/episodes/23.htmlShow Page: https://trino.io/broadcast/
22: TrinkedIn: LinkedIn gets a Trino promotion
This episode will cover LinkedIn's journey to upgrade from PrestoSQL to Trino and some of the operational challenges LinkedIn's engineering team has faced at their scale.- Intro Song: 00:00- Intro: 00:36- News: 7:39- Concept of the week: Trino usage at LinkedIn: 15:55- Concept of the week: Trino hardware and operational scale: 23:23- Concept of the week: Challenges operating at scale: 44:09- Concept of the week: Open source at LinkedIn: 48:36- Concept of the week: PrestoSQL to Trino upgrade cha
21: Trino + dbt: A match made in SQL heaven?
- Intro Song: 00:00- Intro: 00:35- News: 7:42- Question of the week: Can dbt connect to different databases in the same project?: 18:18- Concept of the week: What is dbt?: 21:28- Concept of the week: dbt + Trino: 38:09- Demo: Querying Trino from a dbt project: 47:21- PR of the week: PR 8283 Externalised destination table cache expiry duration for BigQuery Connector: 1:21:13Show Notes: https://trino.io/episodes/21.htmlShow Page: https://trino.io/broadcast/
20: Trino for the Trinewbie
- Intro Song: 00:00- Intro: 00:35- News: 10:16- Concept of the week: Trino for the Trinewbie: 19:12- Concept of the week: Marius' Journey: 21:03- Concept of the week: Contributing to Trino: 54:55- PR of the week: PR 8135 Set default time zone for the current session: 1:03:36- Demo: Contributing to Trino: 1:11:49- Question of the week: How do I search nested objects in Elasticsearch from Trino?: 1:24:24We didn't have time to run through the demo. I created another video outside of the show if y
19: Data Ingestion to Iceberg and Trino
- Intro Song: 00:00- Intro: 00:37- News: 7:56- Concept of the week: Ingesting into Iceberg with Pulsar and Flink at BlueCat: 17:30- Concept of the week: BlueCat Overview: 20:31- Concept of the week: Single Tenant to Multi-Tenant: 21:33- Concept of the week: Pre-Iceberg: 26:13- Concept of the week: Iceberg: 39:29- PR of the week: PR 1905 Add format_number function: 1:01:55- Demo: Showing the format_number functionality: 1:04:38- Question of the week: How do I search nested objects in Elasticsea
18: Trino enjoying the view
- Intro Song: 00:00- Intro: 00:34- News: 1:44- Concept of the week: Trino Views, Hive Views, and Materialized Views: 4:57- PR of the week: PR 4832 Add Iceberg support for materialized views: 59:04- Demo: Showing the different views in Trino: 1:01:25- Question of the week: Are JDBC drivers backwards compatible with older Trino versions?: 1:21:02Show Notes: https://trino.io/episodes/18.htmlShow Page: https://trino.io/broadcast/
17: Trino connector resurfaces API calls
- Intro Song: 00:00- Intro: 00:34- News: 2:52- Concept of the week: Resurface and the Resurface connector: 8:58- PR of the week: PR 4022 Add Soundex function: 1:08:17- Demo: Using the soundex function: 1:10:27- Question of the week: Question of the week: How to export query results into a file (e.g. CTAS, but into a single file)?: 1:18:46Show Notes: https://trino.io/episodes/17.htmlShow Page: https://trino.io/broadcast/
16: Make data fluid with Apache Druid
- Intro Song: 00:00- Intro: 00:34- News: 7:28- Concept of the week: Apache Druid and realtime analytics: 14:51- PR of the week:PR 3522 Add Druid connector: 33:35- Demo: Using the Druid Web UI to create an ingestion spec querying via Trino: 1:01:29- Question of the week: Why doesn’t the Druid connector use the native json over http calls?: 1:10:20Show Notes: https://trino.io/episodes/16.htmlShow Page: https://trino.io/broadcast/
15: Iceberg right ahead!
- Intro Song: 00:00- Intro: 00:34- News: 6:37- Concept of the week: Apache Iceberg and the Iceberg spec: 13:32- PR of the week: PR 7233 Fix queries on tables without snapshot id: 1:07:44- Demo: Creating tables with Iceberg and reading the data in Trino: 1:10:39- Question of the week: What do I do to restart the test pipeline if it fails on me?: 1:23:35Show Notes: https://trino.io/episodes/15.htmlShow Page: https://trino.io/broadcast/
14: Iceberg: March of the Trinos
- Intro Song: 00:00- Intro: 00:34- News: 7:57- Concept of the week: Apache Iceberg and the table format: 17:14- PR of the week: PR 1067 Add Iceberg connector: 59:22- Demo: Creating tables through Iceberg and reading them through Trino: 1:04:04- Question of the week: Why do we still depend on the Hive metastore if metadata for Iceberg saves to the filesystem?: 1:18:39Show Notes: https://trino.io/episodes/14.htmlShow Page: https://trino.io/broadcast/
13: Trino takes a sip of Pinot!
- Intro Song: 00:00 - Intro: 00:34 - News: 4:23 - Concept of the week: Data cubes and Apache Pinot: 14:00 - Interview: Apache Pinot: 24:40 - PR of the week: PR 2028 Add Pinot connector: 53:51 - Question of the week: Why does my passthrough query not work in the Pinot connector?: 1:06:23 - Demo: Pinot batch insertion and query using Trino Pinot Connector: 1:12:18 - Get involved with Pinot: 1:22:00Show Notes: https://trino.io/episodes/13.htmlShow Page: https://trino.io/broadcast/
12: Trino gets super visual with Apache Superset!
- Intro Song: 00:00 - Intro: 00:34 - News : 2:57 - Concept of the week: Trino client, Python, and Apache Superset: 5:37 - Interview: Apache Superset: 19:22 - PR of the week: Superset PR 13105 feat: first step native support Trino: 46:37 - PR Demo: PR Demo: Superset PR 13105 feat: first step native support Trino: 55:13 - Get involved with Superset: 1:25:06 - Question of the Week: How do I use the Trino REST api?: 1:29:02Show Notes: https://trino.io/episodes/12.htmlShow Page: https://t
11: Dynamic Filtering and Dynamic Partition Pruning
- Intro Song: 00:00 - Intro: 00:34 - News : 3:22 - Concept of the week: Dynamic Filtering and Recap: 7:43 - PR of the week: PR 1072 Implement Dynamic Partition Pruning: 17:56 - PR Demo: PR 1072 Implement Dynamic Partition Pruning: 42:03Show Notes: https://trino.io/episodes/11.htmlShow Page: https://trino.io/broadcast/
10: Naming the bunny!
Table of Contents: - Intro Song: 00:00 - Intro: 00:32 - Where did the bunny come from? : 8:58 - Bunny names in the running...: 16:58 - And our bunny's name is..: 20:15 - Release 352 sneak peek: 23:46 - Community stats update: 34:54Show Notes: https://trino.io/episodes/10.htmlShow Page: https://trino.io/broadcast/
9: Distributed hash-joins, and how to migrate to Trino
Table of Contents: - Intro Song: 00:00 - Intro: 00:32 - Question of the week: How do I migrate to Trino from PrestoSQL? 11:55 - Concept of the week: Distributed hash-join 16:03 - Quick Discussion: Contributing Documents and Testimonials 58:05Show Notes: https://trino.io/episodes/9.htmlShow Page: https://trino.io/broadcast/
8: Trino: A Ludicrously Fast Query Engine: Past, Present, and Future
Table of Contents: - Intro Song: 00:00 - Intro: 00:32 - Martin Traverso Intro: 1:45 - Dain Sundstrom Intro: 4:30 - David Phillips Intro: 8:26 - Eric Hwang Intro: 12:24 - Past: Presto name, Facebook projects, and open source: 16:50 - Past: Where did the name Trino come from?: 23:16 - Past: Why Martin, Dain, and David left Facebook and created a Presto fork: 26:14 - Past: Why did you choose to continue using Presto instead of rebranding?: 34:51 - Past: How did the contending Presto drive us to re
7: Cost Based Optimizer, Decorrelate subqueries, and does Presto make my RDBMS faster?
Table of Contents: - Intro Song: 00:00 - Intro: 00:20 - News: 3:27 - Concept of the week: Cost Based Optimizer 16:48 - PR of the week: PR 1415 Decorrelate subqueries with Limit or TopN 43:09 - PR Demo: EXPLAIN Decorrelate subqueries with Limit or TopN 53:36 - Question of the week: Will running Presto on my relational database make processing faster? 1:02:24Show Notes: https://trino.io/episodes/7.htmlShow Page: https://trino.io/broadcast/
6: Query Planning, Remove duplicate predicates, and Memory settings
Table of Contents: - Intro Song: 00:00 - Intro: 00:20 - News: 12:32 - Concept of the week: Query Planning 20:31 - PR of the week: PR 730 Remove duplicate predicates 29:55 - PR Demo: Remove duplicate predicates demo 33:47 - Question of the week: How should I allocate memory properties? 54:49Show Notes: https://trino.io/episodes/6.htmlShow Page: https://trino.io/broadcast/
5: Hive Partitions, sync_partition_metadata, and Query Exceeded Max Columns!
Table of Contents: - Intro Song: 00:00 - Intro: 00:20 - News: 05:26 - Concept of the week: Hive Partitioning 19:12 - PR of the week: PR 223 Add system.sync_partition_metadata procedure to sync Hive table partitions 29:20 - PR Demo: system.sync_partition_metadata procedure demo 34:57 - Question of the week: Why am I getting, 'Query exceeded maximum columns.' error? 1:00:56Show Notes: https://trino.io/episodes/5.htmlShow Page: https://trino.io/broadcast/
4: Presto on ACID, row-level INSERT/DELETE, and why JDK11?
Table of Contents: - Intro: 0:20 - News: 3:56 - Concept of the week: Presto on ACID 19:35 - PR of the week: PR 5402 Hive ACID row-level INSERT and DELETE 27:24 - PR Demo: Hive ACID row-level INSERT and DELETE demo 42:26 - Question of the week: Why is JDK 11 required to run Presto and how can I revert to JDK8? 54:19Show Notes: https://trino.io/episodes/4.htmlShow Page: https://trino.io/broadcast/
3: Running two Presto distributions and Kafka headers as Presto columns
Table of Contents: - Intro: 0:20 - News: 4:07 - Interview - Using multiple Presto distributions: 30:10 - PR of the week: PR 4462 Add Kafka headers as columns 51:35Show Notes: https://trino.io/episodes/3.htmlShow Page: https://trino.io/broadcast/
2: Kubernetes, arrays on Elasticsearch, and security breaks the UI
Table of Contents: - Intro: 0:20 - News: 3:39 - Presto event - Presto for any company size: 14:49 - Concept of the week: Presto on Kubernetes 28:19 - PR of the week: PR 2441 Add array support using the _meta mapping in Elasticsearch 41:24 - Question of the week: Why does my ui say Web Interface disabled? 55:06Show Notes: https://trino.io/episodes/2.htmlShow Page: https://trino.io/broadcast/
1: What is Presto, WITH RECURSIVE, and Hive connector
Table of Contents: - Intro: 2:58 - News: 6:48 - Concept of the week: What is Presto? - 24:37 - PR of the week: PR 5163 WITH RECURSIVE - 39:05 - Question of the week: Does the Hive connector depend on the Hive runtime? - 48:38Show Notes: https://trino.io/episodes/1.htmlShow Page: https://trino.io/broadcast/