Governo formaliza adesão à nuvem pública em contrato com Embratel

"(...) O projeto com a Embratel faz parte da contratação da primeira nuvem pública do governo federal, licitada ainda em 2018 e que começou a ser implementada no ano passado. Essa primeira contratação reúne 23 órgãos públicos e tem custo projetado de R$ 55 milhões. "


COVID-19 didn’t break your business. Data did.

(...) Not every enterprise stumbled, nor did every government. The defining factor wasn't how digital these public and private entities were. These surviving enterprises and governments embraced the data both pre-COVID-19 and during COVID-19 (...) 


How Amazon is solving big-data challenges with data lakes

"(...) A major reason companies choose to create data lakes is to break down data silos. Having pockets of data in different places, controlled by different groups, inherently obscures data. This often happens when a company grows fast and/or acquires new businesses. In the case of Amazon, it's been both (...)" (Werner Vogels)


Optimize Your Amazon S3 Data Lake with S3 Storage Classes and Management Tools (Youtube)

"As your data lake grows, it becomes increasingly important to manage objects at scale and optimize storage costs and resources. In this tech talk, AWS experts provide an overview of S3's capabilities that allow you to manage data at the object, bucket, and account levels. Learn about and watch demos for S3 Batch Operations. Also learn cost-optimization best practices by storing objects across the S3 Storage Classes."


Leitura Recomendada: "Factfulness: Ten Reasons We're Wrong about the World"

"I don’t love numbers. I am a huge, huge fan of data, but I don’t love it. It has its limits. I love data only when it helps me to understand the reality behind the numbers, i.e., people’s lives. In my research, I have needed the data to test my hypotheses, but the hypotheses themselves often emerged from talking to, listening to, and observing people. Though we absolutely need numbers to understand the world, we should be highly skeptical about conclusions derived purely from number crunching."


Australia-wide AWS deal

The Australian government's attitude towards cloud has been very positive, and according to Amazon Web Services (AWS) Worldwide Public Sector Asia Pacific regional managing director Peter Moore, what's prevented an all-in approach has been legacy arrangements and a traditional approach to procurement (...)


Machine Learning for Everyone

(...) Without all the AI-bullshit, the only goal of machine learning is to predict results based on incoming data. That's it. All ML tasks can be represented this way, or it's not an ML problem from the beginning. The greater variety in the samples you have, the easier it is to find relevant patterns and predict the result (...)


AWS, Microsoft or Google: Which cloud computing giant is growing the fastest?

"Spending on cloud computing infrastructure continues to grow at a furious pace, but cloud vendors will have to work harder for their profits from now on. Global cloud infrastructure services market grew 42 percent year-on-year in the first quarter of 2019 with Amazon Web Services (AWS) making the biggest gain in dollar terms with sales up by $2.3 billion (41%) on Q1 2018, according to data from tech analyst firm Canalys. That performance put AWS further ahead of second-placed Microsoft, even though it grew sales by $1.5 billion or 75 percent. Google was the fastest growing of the top three in percentage terms, up 83 percent from $1.2 billion to $2.3 billion (....)"


Cloud Data Warehouse Benchmark: Redshift, Snowflake, Azure, Presto and BigQuery

What data warehouse should I choose?


How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh (Martin Fowler)

Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product. (...)

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