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It’s worth pointing out that Azure is a bit above the long term trendline, while AWS is still below (but accelerating up). GCP data is a bit more noisy as they don’t disclose GCP itself, but rather Google Cloud which includes GSuite. After earnings, that perception either changed positively or negatively.
The hyperscalers (AWS, Azure, GCP) are always some of the first companies to report earnings during earnings season (coming up in 2 weeks), and there’s always a read through for consumption names (meaning people believe there’s a correlation). Cloudflare is up 17%. Datadog is up 14%. Mongo is up 16%. Snowflake is up 14%.
While there are many steps to getting your infrastructure setup ready for a SOC 2 audit, below are some simple settings you can start with, if you’re using AWS or GCP ( Secureframe also supports Azure). By implementing these 5 security changes, you’ll have finished some of the more time-consuming elements of getting SOC 2 ready.
We now have results from the three hypersclaers (AWS / Azure / GCP). The most notable change in tone was Andy Jassy talking about AWS. The information provided is believed to be from reliable sources but no liability is accepted for any inaccuracies. ” Full quote below: “We're seeing a few trends right now.
Subscribe now Foundation Models Are to AI what S3 was to the Public Cloud Many people look at 2006 as the birth of the public cloud - the year Amazon launched AWS. Microsoft launched Azure in 2010, and Google launched GCP to the public in 2011 (they launched a preview of Google App Engine in 2008, but made it publicly available in 2011).
Cloud Giants Report Q2 We also got the Q2 quarters from AWS / Azure / GCP this week! The information provided is believed to be from reliable sources but no liability is accepted for any inaccuracies. This is for information purposes and should not be construed as an investment recommendation.
Hyperscalers Report Quarterly Earnings This week we saw AWS (Amazon), GCP (Google) and Azure (Microsoft) report earnings. Overall, it wasn’t pretty… AWS grew 28% when expectations were 30-31%. This post and the information presented are intended for informational purposes only.
” These are two quotes about AWS on the Amazon earnings call. AWS grew 16% in Q1, but called out growth in April (first month of Q2) was 11%. This post and the information presented are intended for informational purposes only. Tailwind of new workloads starting to overtake headwinds of optimizations.
All 3 (AWS, Azure, GCP) saw positive reacceleration Quarterly Reports Summary Top 10 EV / NTM Revenue Multiples Top 10 Weekly Share Price Movement Update on Multiples SaaS businesses are generally valued on a multiple of their revenue - in most cases the projected revenue for the next 12 months.
Next week we get all 3 hyperscalers reporting (AWS from Amazon, Azure from Microsoft, and GCP from Google). On AWS, in their Q4 earnings call they said AWS was growing “mid teens” in January (down from 20% in Q4). This post and the information presented are intended for informational purposes only.
Hyperscalers (AWS, Azure, GCP as companies look for cloud GPUs who aren’t building out their own data centers) Infra (Data layer, orchestration, monitoring, ops, etc) Durable Applications We’ve clearly well underway of the first 3 layers monetizing. Subscribe now Share Clouded Judgement Leave a comment
This can lead to an airpocket of valuation as companies transition to a different primary valuation metric Outside of the hypserscalers (Azure, AWS, GCP) who have uniquely benefited from AI revenue (mainly selling compute), everyone else has largely struggled. Coming in to Q1 there was broader optimism. Q4’s were generally good!
AI = Data + Compute I’ll continue beating this drum, but we got two great quotes from Azure and AWS this week. Satya at Microsoft said “Every AI app starts with data and having a comprehensive data and analytics platform is more important than ever.” AWS reports next week. So what did we learn?
Typical data lake storage solutions include AWS S3, Azure Data Lake Storage (ADLS), Google Cloud Storage (GCS) or Hadoop Distributed File System (HDFS). The information provided is believed to be from reliable sources but no liability is accepted for any inaccuracies. A natural question is “why do we have two tiers?
The hyperscalers (AWS, Azure, GCP) are seeing some uptick, but this is largely from selling compute (ie cloud GPUs). This post and the information presented are intended for informational purposes only. I’m hopeful we’re getting leading indicators that the light at the end of the tunnel is getting closer.
The AWS Well-Architected Framework is one such approach that helps adopt architectural best practices (whether or not you run on AWS) and adapt continuously. That, of course, dictates the isolation models used for information and access management. Governing authorities lay down regulation on private data protection.
You might see the three stages of the FinOps journey referred to as inform, optimize, and operate, a model popularized by the FinOps Foundation. While the inform-optimize-operate system has its place, in order to cut through the jargon, I prefer the terms inform, analyze, and act. Table of Contents. What Is FinOps?
Our goal is to give our customers and the market the data they need to build a solid case and make informed decisions about when they should take advantage of Cloud Marketplaces. .
Service providers like Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure offer server hosting and load-balancing services. Service providers like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer infrastructure services that support backend development.
They act as translators, transforming raw data sets into clear and actionable information that businesses can use to make better decisions. They act as translators, transforming raw data sets into clear and actionable information that businesses can use to make better decisions. What decisions will the insights inform?
TL;DR A data scientist is someone who uses their knowledge of statistics, programming, and specific industry expertise to extract meaningful information from data. A data scientist is someone who uses their knowledge of statistics, programming, and specific industry expertise to extract meaningful information from data.
A cloud server, like an AWS EC2 instance, is still a server. The only difference is that it is sitting in AWS' datacentres, rather than in your office. Everything that you read here is relevant to you whether you host your own web applications or use a cloud platform like AWS. Not really.
TL;DR A data scientist is someone who uses their knowledge of statistics, programming, and specific industry expertise to extract meaningful information from data. A data scientist is someone who uses their knowledge of statistics, programming, and specific industry expertise to extract meaningful information from data.
This can allow access all customer-sensitive information and spoof identities in virtual environments. During this process, it collects information about inputs, outputs, and potential attack vectors. This is known as code injection which enables an attacker to take control of a business system once a user executes the code.
This automated vulnerability testing tool can scan web applications even behind the login credentials page, helps you automate end-to-end API security as well as helps you secure your cloud platform console, eg. AWS, Azure or GCP. SQLMap SQLmap can perform various types of SQL injection attacks, including blind SQL injection.
Alison Wagonfeld is the Chief Marketing Officer for Google Cloud. She’s responsible for both GCP, which is the Google Cloud Platform and for G-Suite, which is Gmail, Calendar, Sheets, Docs, all the stuff that we all use everyday. Go to vidyard.com/saleshacker for more information.
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