Amazon, every mention
51 scenes (2021), the whole family · ← back to Amazon
tap a year for its mentions
every year 2021 anyone Matt Turck 122Benedict Evans 33Bob Muglia 25Spencer Kimball 18Prat Moghe 18Evan Kaplan 16Justin Borgman 15Tristan Handy 14Nate Stewart 13Aaron Katz 13
Verbatim, from the transcripts: the passages where Amazon comes up
Fireside Chat: Aaron Katz (Co-Founder & CEO, ClickHouse) with Matt Turck (Partner, FirstMark)
- ▶ 3:15 Aaron Katz So obviously things like Kafka and DBT and Kinesis,
- ▶ 11:35 Aaron Katz I first met Yuri when he was running platform engineering at Netflix and led their migration to the cloud, uh, for a number of years, I believe he was large AWS's largest customer at the time.
- ▶ 14:25 Aaron Katz And we've, we've seen other companies in their approach to neutralize, in many cases, the threat of large cloud service providers like, like AWS and others. 3 times in the scene
- ▶ 18:02 Aaron Katz You've got Redshift, BigQuery, Snowflake.
- ▶ 20:51 Aaron Katz We talked a little bit about, you know, ingestion and we're going to be investing heavily to make getting data from Kafka to ClickHouse or Kinesis or an integration on
Fireside Chat: DeVaris Brown (Founder & CEO, Meroxa) with Matt Turck (Partner, FirstMark)
- ▶ 11:28 DeVaris Brown Um, and that, that allows you to do things like, now I don't have to have the Postgres to Redshift connector or the MySQL to Salesforce connector.
- ▶ 14:54 DeVaris Brown So, you know, if you want to have a, because you can multiplex or, you know, kind of multi, you know, branch out that same stream to multiple destinations, uh, it's really easy for us to have like an immutable S three bucket
Fireside Chat: Ali Ghodsi (Founder & CEO, Databricks) with Matt Turck (Partner, FirstMark)
- ▶ 15:04 Ali Ghodsi And this was inspired by AWS. 3 times in the scene
- ▶ 21:08 Ali Ghodsi Um, you know, I'll start by saying, you know, it's kind of like people make it about zero sum, but if you answer it like this, do you think Google Cloud will eliminate Amazon Cloud and Microsoft Cloud, or do you think, you know, Amazon… 2 times in the scene
- ▶ 28:14 Ali Ghodsi Like if you look at Amazon Web Services, it's not a one trick pony, right? 2 times in the scene
- ▶ 28:18 Ali Ghodsi Amazon itself is not a one trick pony.
- ▶ 30:33 Ali Ghodsi And that has been really hard up until, you know, Red Hat and all these companies that were doing supporting services, until Amazon Web Services cracked the code on the business model.
Fireside Chat: Florian Douetteau (Founder & CEO, Dataiku) with Matt Turck (Partner, FirstMark)
- ▶ 6:17 Matt Turck And could you maybe, uh, paint a picture of, um, you know, the, the, the things, right, like Facebook, Amazon, Google, uh, on the one hand, and then, you know, the remaining 99.5% of the, of the world, and what that means in terms of…
Fireside Chat: Dev Ittycheria (President & CEO, MongoDB) with Matt Turck (Partner, FirstMark)
- ▶ 14:19 Matt Turck Search your, like, your S three folders. 2 times in the scene
- ▶ 19:51 Dev Ittycheria Now, you know, Amazon's tried to make some extensions to, you know, with Aurora, 6 times in the scene
- ▶ 19:51 Dev Ittycheria Now, you know, Amazon's tried to make some extensions to, you know, with Aurora,
- ▶ 21:15 Dev Ittycheria Uh, in fact, I would argue, for example, that AWS has done a better job monetizing MySQL than MySQL, Sun, or Oracle ever did, right? 4 times in the scene
Fireside Chat: Dave Burgess (Head of Data Engineering, Pinterest) w/ Matt Turck (Partner, FirstMark)
- ▶ 8:51 Dave Burgess Now we're, all of contrast is, is running on EWS. 2 times in the scene
- ▶ 8:54 Dave Burgess So we put all of this data into S three 3 times in the scene
- ▶ 10:22 Matt Turck And then we have like a red shift since you're on the AWS stack.
- ▶ 12:30 Dave Burgess And that's, you know, mostly from this data that comes into, that comes into S three and AWS.
- ▶ 12:30 Dave Burgess And that's, you know, mostly from this data that comes into, that comes into S three and AWS.
- ▶ 30:56 Dave Burgess And so, and so Kafka, yeah, Kafka basically, uh, brings all the data into S three 3 times in the scene
Fireside Chat: Bindu Reddy (Founder & CEO, Abacus.AI) with Matt Turck (Partner, FirstMark)
- ▶ 9:21 Bindu Reddy So let's assume you're using a redshift or a snowflake or what have you.
- ▶ 15:52 Bindu Reddy So for the people who have like that 500 person machine learning team, like take Pinterest, and Pinterest of course only uses AWS, as they've mentioned.
- ▶ 26:57 Bindu Reddy So literally you can connect, you know, your data from Snowflake, from Redshift, and from your data lake and Salesforce together and bring that all together.
Fireside Chat: Arjun Narayan (Founder & CEO, Materialize) with Matt Turck (Partner, FirstMark)
- ▶ 15:34 Arjun Narayan The integrations to tools like Kafka, uh, the integrations to, to, to, to pull batch data from S three.
- ▶ 22:25 Arjun Narayan Basically the ability to store large historical data sets on very cheap storage like Amazon S three, um, such that, you know, you don't have to really think about separating out your historical data set.
Fireside Chat: Chip Huyen with Matt Turck (Partner, FirstMark)
- ▶ 15:14 Chip Huyen Like, you can, um, like, either, like, um, have, uh, bigger, like, better servers, like, some AWS, you can bigger, like, GPUs and stuff, or we can have, like, um, uh, more power for,
Fireside Chat: Jack Hanlon (VP Data, Reddit) with Matt Turck (Partner, FirstMark)
- ▶ 9:36 Jack Hanlon From the top view, I'd say we're, we're AWS prod, um, with originally a lot of Postgres, um,
- ▶ 22:49 Matt Turck Folks, um, Mohammed asks, uh, what motivated the move to GCP from AWS to, to the extent you can talk about it?
- ▶ 23:01 Jack Hanlon Oh, uh, I mean, um, Redshift isn't very good. 3 times in the scene
- ▶ 23:10 Jack Hanlon I think SageMaker is, there's a lot of promise there and a lot of capability, but in terms of, like, having a, um, having an analytics tool or, or the thing that people are acquiring, Redshift feels way behind, whereas a number of years…
- ▶ 23:31 Jack Hanlon Snowflake, on the other hand, okay, so you could put Snowflake on top of AWS,
Fireside Chat: Savin Goyal (ML Infra team (Metaflow), Netflix) with Matt Turck (Partner, FirstMark)
- ▶ 3:20 Savin Goyal Uh, we are an AWS shop.
- ▶ 3:23 Savin Goyal Uh, so we use SG as
- ▶ 5:51 Savin Goyal So if say you were a data scientist at Netflix, then you could in theory use a Jupyter notebook, get access to, uh, your data from ST via a bunch of different query engines, and then, uh, 2 times in the scene
- ▶ 15:46 Savin Goyal Uh, you know, let's say if you're using S three as your data store, but now if you're using Kubernetes, uh, as your compute cluster and say, if you want to use step functions or Argo,
- ▶ 20:27 Savin Goyal They care about what sort of modeling libraries, uh, they should be using, but not so much about, you know, how the data is actually being stored in S three, as long as they make efficient access, uh, to S three.
- ▶ 21:44 Savin Goyal So, uh, so Netflix is an AWS shop. 6 times in the scene
Introducing Kedro
- ▶ 11:39 Kedro Product Manager In most cases, your data catalog could look something like this, where your data is an S three or is your blog storage or Google cloud storage or do file system somewhere.
- ▶ 16:17 Kedro Product Manager I did see one question that was asked, um, around how does this compare to, um, AWS SageMaker? 4 times in the scene
- ▶ 16:22 Kedro Product Manager So they actually did, um, the AWS team actually did feature, um, Kedra on the blog. 2 times in the scene
Data Observability and Pipelines: OpenLineage and Marquez
- ▶ 2:35 Julien Le Dem You may be depending on an S three bucket or a table in snowflake, but you don't know how it's produced, right?
- ▶ 9:54 Julien Le Dem It was like, before you have all those projects that are interested in metadata, Amundsen, DataHub, Marques, Atlas, that have to build all those integrations with all those projects, whether they're the Sparks, the Pandas, uh, the…
- ▶ 18:12 Julien Le Dem Your, um, data infrastructure, and you have an injection, and then you add a storage layer for streaming and for batch processing using things like Kafka or Sree or HDFS, and then you would have stream and batch processing, and usually you…
- ▶ 25:40 Julien Le Dem And, but nowadays in BigQuery and Snowflake and Redshift eventually caught on on being able to read Parquet and export as Parquet, right?
Fireside Chat: Alok Gupta (Head of Data Science & ML, DoorDash) with Matt Turck (Partner, FirstMark)
- ▶ 3:05 Alok Gupta Where we, we build, uh, products and services to help us in the things we're trying to do, but ultimately we get them to a stage of maturity and robustness through experimenting on ourselves where we can then offer them as white label…
- ▶ 3:05 Alok Gupta Where we, we build, uh, products and services to help us in the things we're trying to do, but ultimately we get them to a stage of maturity and robustness through experimenting on ourselves where we can then offer them as white label…
- ▶ 21:12 Matt Turck Am I understanding correctly that instead of running Databricks on something like S-III, you are running on Snowflake. 4 times in the scene
- ▶ 22:22 Alok Gupta Databricks runs on AWS.