Data Warehouse
topic on 8 shows · 33 statements across 18 episodes
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33 statements about Data Warehouse, every show
Shipper: A major AI model lab deploys company-wide data science query bot
“The way that it works inside of the big model companies, for example, like at least one of them has literally a data science bot that every single person in the org can query that is hooked up to their data warehouse that knows who's who so that it knows at th…”
Masad: Enterprises are skipping SaaS tools to build directly on data warehouses
“People are skipping the SaaS tools entirely and like building on top of their data warehouse.”
Ben Rogojan observed ML engineers at Facebook accessing data directly from warehouses
“Some companies do have maybe the ML engineers access data more directly from maybe the data warehouse, which was things I saw like at Facebook.”
Goyal: Internet-trained LLMs outperform models trained on internal enterprise data
“And I think the big insight or the crazy, you know, non-intuitive thing about LLMs is that something trained on the internet outperforms what an enterprise can produce with their own data trained on data in a data warehouse.”
Goyal: Enterprise AI data infrastructure will move away from data warehouse ETL
“And I think the way that enterprises will collect data and leverage it into, you know, these AI processes does not look like doing ETL on a data warehouse that's, you know, running in, in Amazon or something like that. I think it's gonna totally change.”
Goyal: AI semantic search will disrupt OLAP far more than OLTP
“What will really be disrupted is the OLAP workload. So relational, you can't just slap you know, semantic search and stuff into the architecture of a traditional data warehouse. I think that is actually a much deeper set of things that will need to change than…”
Keydunov: Querying a semantic layer over raw SQL reduces LLM errors
“Now, the query, I believe, should be created against semantic layer, because it reduces the room for the error, because what usually happens is that your query to semantic layer would be very simple. It would be like, give me that metric grouped by that dimens…”
Keydunov: Data-driven AI apps should start on a proper warehouse from day zero
“I would just recommend going through to warehouse as soon as possible. I think a lot of people feel that MySQL can be a warehouse, which can be maybe on like a lower scale, but you know, like, definitely not from a performance perspective. So just kind of havi…”
Catanzaro: Cloud data warehouse transformation costs are surging due to compute
“Storage is cheap, but compute is not. And if you are preparing your data in a data warehouse, you're using compute, and your bill is going to get pretty high.”
Iyengar: Data warehouses are poor analytical tools for event sequences
“Data Warehouse is becoming the loading dock for all of this data, which can be very easily modeled as events. But it's not a very great analytical tool for events because SQL is optimized for rows and tables and joints and not events and sequences of events an…”
Iyengar: Reverse ETL startups are reinventing the CDP on data warehouses
“Companies like in the reverse ETL space, like census and high touch are effectively reinventing the CDP reinventing data movement tool like segment on top of the data warehouse.”
Snowflake Expanded to a Data Platform Market at $500M Revenue
“When they got to 500,000,700 million, they actually changed the market. They picked a different market. They used to be in the data warehouse market. Uh-uh, that's not a big enough market, and they deliberately shifted their market To more, to a more broad dat…”
Centralizing enterprise data in one warehouse will never solve all data problems
“Data repositories, data warehouses, they all argue that just move all of your data in one place, and it's going to solve all of your problems. We've been hearing that promise for the last 25 years, and it's never solved all of our problems, and it never will.”
Wu: Reverse ETL pipes warehouse data back into operational business systems
“Reverse ETLs change this paradigm by closing the loop. So these tools enable companies to pipe the transformed unified data from the warehouse back into the upstream business systems from which the data was generated.”
Dehghani: Enterprise data usage shifted from operational reporting to embedding ML in applications
“We've moved away from, okay, I'm going to run a few, set up a warehouse and get a few reports and get an insight into the operation of my organizations to actually I want to run, you know, include ML, a data-driven way of solving problems into every feature of…”
Dehghani: The debate between data warehouses and lakes is irrelevant
“And I think this kind of funny war between warehouse and lake and web model We should access the data that that seems to me a little bit irrelevant because both of those access models are very acceptable.”
Ghodsi: Open-source Lakehouses will eventually render proprietary data warehouses obsolete
“So slowly what's happening is that the open source realm An ecosystem is emerging where you can do all of your analytics in this lake house paradigm, and you don't, eventually it will be the case that you will not need all these other, you know, proprietary ol…”
Handy defines the modern data stack across four distinct functional layers
“When we talk about the modern data stack, we think about what's really four layers. So there's data ingestion. There's the data warehouse. There's data transformation or like taking all that raw data and like turning it into something valuable. And then there'…”
Lowin: Modern tools let analysts query data warehouses directly over static CSVs
“If we think back to BI tools, say five, certainly 10 years ago, you would sort of beg for a CSV and God help you if the data, if the insight you weren't, the insight you were looking for was not in that CSV, you were sort of screwed. But now thanks to, you kno…”
Handy: Data lakes offer more flexibility but require more effort
“Where we are today, the data lake can kind of do anything. But it also probably takes more work to do anything. Whereas the data warehouse is, has a more constrained set of use cases, but it is much easier to get up and running for those constraints set of use…”
Handy: Data transformation has shifted inside data warehouses using SQL
“What's happening now or over the past, you know, five or so years, the, Transformation step now happens inside the data warehouse and it happens in SQL. And because of that, it is now accessible to a dramatically larger number of people.”
Handy: Reverse ETL will automate workflows and multiply the data market opportunity
“There's a class of tools that takes the data that is in your data warehouse and pushes it back to operational systems. And I think that you will, as soon as that starts happening, you can automate the entire process and all of these technologies become, you kn…”
Handy: Once sensitive data lands in a warehouse, it resists elimination
“If you land data in your warehouse, it is very hard to ever have it go away completely.”
Fraser: Cloud data warehouses render legacy tools like OLAP cubes obsolete
“The tools that you use to manage data and analyze data are actually getting simpler over the last 10 years. You don't need as many different things because a few tools, most importantly the data warehouse, Have gotten so much better over the last 10 years that…”
Fraser: BI dashboards are the most common data warehouse use case
“In practice, the most common use of data warehouses is to support business intelligence dashboards.”
Fraser: Fivetran has customers running billing out of their data warehouse
“We have customers who run billing out of their data warehouse.”
Fraser: In-warehouse transformation compute is cheaper than data engineering time
“The additional cost of compute and storage to replicate the extra data, to do those steps, those transformation steps inside the warehouse Are so small now, you know, they're less than what it's going to cost you to pay your data engineer for a week to build y…”
Levy: Indicative is unique in connecting directly to data warehouses
“In fact, we are unique in the sense that we allow you to connect directly to your data warehouse.”
Stanek: Hadoop and data warehouses are where data goes to die
“With all the investment in Hadoop and this and Hadoop that, you know, most companies are still data bankrupt. You know, Hadoop or Data Warehouse or whatever is a place where data goes to die”
Dhillon: Data lakes will eventually drown out traditional data warehouses
“The rising tide of the data lake, we think, will drown out the data warehouse in the fullness of time.”
Steier: Data warehouses cannot run huge ad-hoc analyses on a whim
“The data warehouse is not capable of doing very large analyses for just, you know, on a whim by anybody who might happen to ask a question”
Steier: Adding 10 billion rows to a data warehouse takes a long time
“If I need to, if I have a new data set, somebody hands me ten billion rows of data, and I want to do an analysis on it, you know, to get a data warehouse up and running, or to add that to an existing data warehouse is a non-trivial thing to do. It takes a long…”