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Showing posts from February, 2020

Extract, Load, Transform?

Even as technology changes, many things stay the same. Not least, in the world of enterprise-scale data processing, where massive volumes of data often need to be reformatted, restructured, and moved from one place to another (for a broader introduction, you can read our Key Criteria report on data pipelines ). The Extract-Transform-Load (ETL) model emerged with traditional, data-centric architectures in which large-scale, structured databases were king. Cloud computing is changing the game, for several reasons: ‘Elastic’ infrastructure (which scales according to need) reduces the constraints on what data can be stored where and when Serverless compute models work on an event-driven basis, which is more conducive to (potentially infrequent or nonlinear) streaming data sources Cloud-based data architectures offer greater flexibility, for example, by not requiring data to be fully structured in advance of storing it As a result of such drivers, models are emerging that are more ak
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Introducing the GigaOm Radar

I love working for GigaOm because of its dynamism and the forward-looking philosophy that fuels all our research projects. The firm is relatively recent to the market, but the idea of positioning our research activity and the resulting reports stands out from the crowd, thanks to a different approach that I cover here. We focus more on the technical aspects of solutions, and the vision, and capacity of execution of vendors instead of looking primarily at market share and other parameters that are not really of help if you want to build an IT strategy that is modern, flexible, and again, forward-looking! Be Safe, or Be Strong! In today’s organizations, IT decision-makers have two options, the second of which drives how we develop and publish our research. Stay safe, always. If this is your mantra, I am pretty sure you are not reading this, and you probably never heard of GigaOm before. Your favorite research analyst is a well-established firm, and your favorite saying is, “nobody g
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Voices in AI – Episode 107: A Conversation with Nir Bar-Lev

[voices_in_ai_byline] About this Episode On Episode 107 of Voices in AI , Byron and Nir Bar-Lev discuss narrow and general AI and the means by which we build them out and train them. Listen to this episode or read the full transcript at www.VoicesinAI.com Transcript Excerpt Byron Reese: This is Voices in AI brought to you by GigaOm and I’m Byron Reese. Today I’m excited my guest is Nir Bar-Lev. He is the CEO and the co-founder of allegro.ai. He holds a degree in law and economics from the University of Haifa. He holds a Bachelor of Science in software engineering. He holds an MBA from Wharton and probably a whole lot more. Welcome to the show, Nir. Nir Bar-Lev: Hi Byron, thank you so much, I’m honored to be on the show. So I’d like to start off with just a signposting kind of question, which is about the nature of intelligence and when we talk about AI, do you think we’re really building something that is truly intelligent, or are we building something that can mimic intelligenc
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