SQL on Hadoop for Enterprise Analytics

| July 26, 2016

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Hear why SQL on Hadoop is the future of analytics. Mike Xu, Looker Data Architect and Evangelist will share how recent updates to SQL query engines like Spark and Presto are finally allowing companies to harness Hadoop’s processing power for analytics. Looker Data Analyst Eric Feinstein shares how a top 10 health insurance company built an in-cluster data platform using Looker to make all their data in Hadoop accessible to thousands of analysts and business users across the company every day.

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OTHER ARTICLES

Big Data Is Helping Us Fight The Coronavirus But At What Cost To Our Privacy

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In some ways at least, technology has been able to tell us more about how and where the virus is spreading. Mostly, this has involved creatively harnessing the power of big data using temperature readings from smart thermometers to detect COVID-19 hot spots, or aggregating cellphone location data to point to the areas of the country where people are staying home. But against a backdrop of debate between civil liberties and public health, we also need to be asking where the line is digitally: How much surveillance is acceptable in the service of the greater good.

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Data Analytics Convergence: Business Intelligence(BI) Meets Machine Learning (ML)

Article | April 9, 2020

Headquartered in London, England, BP (NYSE: BP) is a multinational oil and gas company. Operating since 1909, the organization offers its customers with fuel for transportation, energy for heat and light, lubricants to keep engines moving, and the petrochemicals products. Business intelligence has always been a key enabler for improving decision making processes in large enterprises from early days of spreadsheet software to building enterprise data warehouses for housing large sets of enterprise data and to more recent developments of mining those datasets to unearth hidden relationships. One underlying theme throughout this evolution has been the delegation of crucial task of finding out the remarkable relationships between various objects of interest to human beings. What BI technology has been doing, in other words, is to make it possible (and often easy too) to find the needle in the proverbial haystack if you somehow know in which sectors of the barn it is likely to be. 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On the contrary, they are more useful than ever: whereas in the past humans were in the driving seat and controlling the demand for the use of the datasets acquired and curated diligently, we have now machines taking up that important role and hence unleashing manifold different ways of using the data and finding out obscure, non-intuitive relationships that allude humans. Moreover, machines can bring unprecedented speed and processing scalability to the game that would be either prohibitively expensive or outright impossible to do with human workforce. Companies have to realize both the enormous potential of using new automated, predictive analytics technologies such as machine learning and how to successfully incorporate and utilize those advanced technologies into the data analysis and processing fabric of their existing infrastructure. It is this marrying of relatively old, stable technologies of data mining, data warehousing, enterprise data models, etc. with the new automated predictive technologies that has the huge potential to unleash the benefits so often being hyped by the vested interests of new tools and applications as the answer to all data analytical problems. To see this in the context of predictive analytics, let's consider the machine learning(ML) technology. The easiest way to understand machine learning would be to look at the simplest ML algorithm: linear regression. ML technology will build on basic interpolation idea of the regression and extend it using sophisticated mathematical techniques that would not necessarily be obvious to the causal users. For example, some ML algorithms would extend linear regression approach to model non-linear (i.e. higher order) relationships between dependent and independent variables in the dataset via clever mathematical transformations (a.k.a kernel methods) that will express those non-linear relationship in a linear form and hence suitable to be run through a linear algorithm. Be it a simple linear algorithm or its more sophisticated kernel methods variation, ML algorithms will not have any context on the data they process. This is both a strength and weakness at the same time. Strength because the same algorithms could process a variety of different kinds of data, allowing us to leverage all the work gone through the development of those algorithms in different business contexts, weakness because since the algorithms lack any contextual understanding of the data, perennial computer science truth of garbage in, garbage out manifests itself unceremoniously here : ML models have to be fed "right" kind of data to draw out correct insights that explain the inner relationships in the data being processed. ML technology provides an impressive set of sophisticated data analysis and modelling algorithms that could find out very intricate relationships among the datasets they process. It provides not only very sophisticated, advanced data analysis and modeling methods but also the ability to use these methods in an automated, hence massively distributed and scalable ways. Its Achilles' heel however is its heavy dependence on the data it is being fed with. 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This approach will combine superior algorithmic processing capabilities of the new ML technology with the enterprise knowledge accumulated through BI efforts and will allow companies build on their existing data analytics investments while transitioning to use incoming advanced technologies. This, I believe, is effectively a win-win situation and will be key to the success of any company involved in data analytics efforts.

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Article | April 9, 2020

Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and speech recognition. However, as the deep learning matures and moves from hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components.

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HEALTHCARE CENTERS ARE TURNING TO AI TO COMBAT COVID-19

Article | April 9, 2020

Artificial Intelligence has emerged as a powerful tool in the time to fight against Covid-19. The technology is used to train computers to leverage big data-enabled models for pattern recognition, interpretation, and prediction using Machine Learning, NLP and Computer Vision. These applications can be effective to diagnose, envision, and treat Covid-19 disease, and they can also assist in managing socio-economic impacts. Since the pandemic spreads quickly, there has been a rush to explore and deploy AI to cure and address the soaring demand of patient treatment infected by Coronavirus.

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Spotlight

Smartifik Oy

Smartifik provides innovative language technology and artificial intelligence solutions for online customer service. Our product, Smartti, is a livechat-like service, but it is fully automatic and works without a human operator. Smartti serves thousands of customers simultaneously, is online 24/7 and provides a cost-efficient way to handle customer service.

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