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Cloud Data Lakes - The Future of Large Scale Data Analysis

Tom Tunguz

Cloud Data Lakes are a trend we’ve been excited about for a long time at Redpoint. This modern architecture for data analysis, operational metrics, and machine learning enables companies to process data in new ways. I’ll also be speaking, sharing some of the trends we see in this space.

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My Laptop is Faster than Your Cloud : Announcing MotherDuck

Tom Tunguz

Some of the brightest minds in data founded MotherDuck including BigQuery founding engineer Jordan Tigani & a broader team from Snowflake, Databricks, AWS, Meta, Elastic & Firebolt, among others. Interest in DuckDB has grown geometrically over the past few years because of what it can do with data.

Cloud 338
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IaaS PaaS SaaS: Mastering The 3 Different Cloud Service Models

How To Buy Saas

Manage Big Data Analysis: IaaS provides a suitable environment to manage large workloads and can process and analyze big data. Examples of IaaS Cloud Providers Amazon Web Services (AWS) Google Cloud Provider (GCP) IBM Cloud Microsoft Azure PaaS Taking a step ahead from IaaS, let us introduce you to PaaS or Platform-as-a-support.

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Benn Stancil, founder of Mode, on how data science can help us make better decisions

Intercom, Inc.

But as Benn points out, the future of data analysis isn’t an architecture diagram or business leaders looking at dashboards – it’s building an experience, and a very exciting one at that. Any insight from data analysis will only ever be as good as the data itself. And it’s a better product.

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What Is FinOps and How Can It Help Businesses Scale Their Financial Processes

Baremetrics

As well as predictive analytics, a related but separate branch of data analysis is the field of prescriptive analytics. Rather than being oriented towards the prediction of future usage, prescriptive analytics aims at calculating the statistically optimal course of action based on known data.

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Top 10 Trends for Data in 2024

Tom Tunguz

First, they have driven an increased demand for data and are causing a complete architecture inside companies. Second, they change the way that we manipulate data. Analysts will use automated data analysis, and it will be an expected tool in every product : notebooks, BI, databases, etc.

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$100 Million ARR Pivot: From Platform Product to Vertical Apps With Treasure Data CEO Kazuki Ohta (Podcast #506 and Video)

SaaStr

At $5 million ARR, the positioning shifted to a “big data-as-a-service” platform. The product grew more mature, with three main functions: data collection, data warehouse, and data analysis. . Commoditization From AWS & Google Cloud. As Ohta says, “Around 2014 in Q4, we were about to cross a $2.5