Digital acceleration following the pandemic is progressing fast, and, for so many organizations, cloud is at the heart of it all. It's helping them become agile, innovate more and create value even through challenging times. Gartner predicts that cloud spending will grow 18.4% this year, to a total of $304.9 billion. However, even with massive cloud uptake, many organizations are lacking in their cloud maturity, according to Infosys' 2021 Cloud Radar Report. Capturing one's fair share of the cloud prize, without the landscape devolving into cloud chaos, is possible only when a company develops a clear view of the value at stake and the business cases that must be prioritized.
Apache Beam is one of the latest projects from Apache, a consolidated programming model for expressing efficient data processing pipelines as highlighted on Beam's main website . Throughout this article, we will provide a deeper look into this specific data processing model and explore its data pipeline structures and how to process them. Apache Beam can be expressed as a programming model for distributed data processing . It has only one API to process these two types of data of Datasets and DataFrames. While you are building a Beam pipeline, you are not concerned about the kind of pipeline you are building, whether you are making a batch pipeline or a streaming pipeline. For its portable side, the name suggests it can be adjustable to all. In Beam context, it means to develop your code and run it anywhere. To use Apache Beam with Python, we initially need to install the Apache Beam Python package and then import it to the Google Colab environment as described on its webpage . In this section, the architecture of the Apache Beam model, its various components, and their roles will be presented. Primarily, the Beam notions for consolidated processing, which are the core of Apache Beam. The Beam SDKs are the languages in which the user can create a pipeline. Users can choose their favorite and comfortable SDK. As the community is growing, new SDKs are getting integrated . Once the pipeline is defined in any supported languages, it will be converted into a generic language standard. This conversion is done internally by a set of runner APIs. I would like to mention that this generic format is not fully language generic, but we can say a partial one. This conversion only generalizes the basic things that are the core transforms and are common to all as a map function, groupBy, and filter. For each SDK, there is a corresponding SDK worker whose task is to understand the language-specific things and resolve them.
In September 2021, AI inside started the provision of "AI inside Cube Pro," the highest performance in the series of our in-house designed edge computing device "AI inside Cube." For AI inside to realize a society in which AI has spread to every corner of society, "AI inside Cube" is an essential element that composes the platform for anyone to create and use AI easily. As a company that provides AI services, we will introduce why we design and provide in-house developed edge computing devices in addition to software, and the future that we are aiming for. There are two primary environments to run AI; cloud computing service and edge computing service. Cloud computing service is when AI training and inference is processed in the cloud, and "cloud AI" refers to running AI in the cloud.
DoiT International (DoiT), a global multi-cloud software and managed service provider (MSP) with deep expertise in Kubernetes, machine learning, big data and proprietary cost optimization tooling, announced acceptance into the Amazon Web Services (AWS) MSP Partner Program. The AWS MSP Partner Program recognizes leading AWS Partner Network (APN) Consulting Partners highly skilled at providing full lifecycle solutions to customers. Next-generation AWS MSPs enable organizations to invent tomorrow, solve business problems and support initiatives by driving key outcomes. Their expertise, guidance and services help companies through each stage of the cloud adoption journey. "Our team is dedicated to helping companies achieve their strategic goals by leveraging the agility, breadth of services and pace of innovation offered by the public cloud," said Yoav Toussia-Cohen, co-founder and CEO of DoiT International.
CIOs are tooling up an assembly line of technologies to get back to business in 2022, including a mix of solutions leaned on heavily to weather the pandemic and new offerings aimed at making the most of emerging opportunities as the pandemic subsides. At its virtual IT Symposium/Xpo this week, Gartner identified the top tech strategies it sees CIOs embracing next year, including the "distributed enterprise," advanced AI, hyperautomation, cloud-native platforms, decision intelligence, and advanced security, among others. Tying together these trends is the C-suite's ongoing recognition of IT as an engine for business transformation. "The two top business priorities for CEOs going into 2022 are scaling digitation and building ecommerce, with the aim of getting back to business," said David Groombridge, research vice president at Gartner, noting that CIO priorities will vary depending on whether they are tasked with driving consumer revenue or building products. But all CIOs will have common set of technology priorities, the analyst predicts.
One of the world's largest technology shows, GITEX, kicks off in Dubai this week. At the event, cloud communications provider Avaya introduced its Experience Builders program, which aligns its services, partners, and developers around the creation of customer and employee experiences. The new initiative is built on Avaya's OneCloud, designed to be a composable back end to enable Avaya and its ecosystem to deliver new experiences. Composable software is a system design principle that deals with the inter-relationships of components. A highly composable system provides components that can be selected and assembled in various combinations to satisfy specific user requirements.
In the run up to Dreamforce 2021 in September, Salesforce announced new capabilities for Einstein Automate as well as new AI-driven workflows and RPA capabilities for Service Cloud . Prior to Dreamforce 2021, I had a chance to talk with Clara Shih, CEO of Service Cloud at Salesforce, about how the cloud-based software company sees automation and AI transforming, and actually humanizing, customer service. The following is a transcript of our interview, edited for readability. So let's talk automation, AI, RPA and how that relates to the Service Cloud and how that's kind of changing how organizations approach their interactions with their customers. Because I know that automation is a large part of many organization's digital transformation processes.
A new survey on data center staffing sponsored by the Uptime Institute indicates the skills shortages continue, and survey participants do not expect artificial intelligence (AI) to reduce skills requirements anytime soon. About 50% of the enterprise data center managers and operators surveyed claim to have difficulty finding skilled candidates, which is up from 38% in 2018. There could be a light at the end of the tunnel: Three out of four respondents believe AI-based technology will reduce their data center staffing needs at some point. However, they feel this shift is more than five years away. Let's look at what's happening right now.
Google Cloud and medical technology vendor Hologic, a women's health specialist, are working together on a new AI algorithmic approach to diagnosing cervical cancer. Cervical cancer is the fourth most common cancer in women. The World Health Organization (WHO) has targeted the disease for eradication. Hologic, based in Marlborough, Mass., in 2020 introduced its Genius Digital Diagnostic system, a digital cytology platform that combines advanced volumetric medical imaging technology and AI algorithms to help researchers identify abnormal cells in cervical and other cancers in women. Toward the WHO's ambitious goal, Google Cloud and Hologic in February 2021 unveiled a multiyear strategic collaboration based on integrating Google Cloud's machine learning (ML) technologies with Hologic's Genius system to significantly improve cervical cancer screening.
The importance of data to today's businesses can't be overstated. Studies show data-driven companies are 58% more likely to beat revenue goals than non-data-driven companies and 162% more likely to significantly outperform laggards. Data analytics are helping nearly half of all companies make better decisions about everything, from the products they deliver to the markets they target. Data is becoming critical in every industry, whether it's helping farms increase the value of the crops they produce or fundamentally changing the game of basketball. Used optimally, data is nothing less than a critically important asset. Problem is, it's not always easy to put data to work. The Seagate Rethink Data report, with research and analysis by IDC, found that only 32% of the data available to enterprises is ever used and the remaining 68% goes unleveraged.