Not surprisingly, it devotes considerable attention to research in this area. Google is a global leader in electronic commerce. Some of our research involves answering fundamental theoretical questions, while other researchers and engineers are engaged in the construction of systems to operate at the largest possible scale, thanks to our hybrid research model. We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and communication. Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU.įrom our company’s beginning, Google has had to deal with both issues in our pursuit of organizing the world’s information and making it universally accessible and useful. Sometimes this is motivated by the need to collect data from widely dispersed locations (e.g., web pages from servers, or sensors for weather or traffic). No matter how powerful individual computers become, there are still reasons to harness the power of multiple computational units, often spread across large geographic areas. Dremel is available for external customers to use as part of Google Cloud’s BigQuery. Some examples of such technologies include F1, the database serving our ads infrastructure Mesa, a petabyte-scale analytic data warehousing system and Dremel, for petabyte-scale data processing with interactive response times. This type of data carries different, and often richer, semantics than structured data on the Web, which in turn raises new opportunities and technical challenges in their management.įurthermore, Data Management research across Google allows us to build technologies that power Google's largest businesses through scalable, reliable, fast, and general-purpose infrastructure for large-scale data processing as a service. The goal is to discover, index, monitor, and organize this type of data in order to make it easier to access high-quality datasets. Through those projects, we study various cutting-edge data management research issues including information extraction and integration, large scale data analysis, effective data exploration, etc., using a variety of techniques, such as information retrieval, data mining and machine learning.Ī major research effort involves the management of structured data within the enterprise. The overarching goal is to create a plethora of structured data on the Web that maximally help Google users consume, interact and explore information. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others). When sourcing EU candidates, please refer to guidance on using social media for recruiting and collecting candidate information as per the General Data Protection Regulation, or GDPR.Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. site:/users/ “badges” “android developer”Īdd more criteria in your Boolean search string for Android developers to find profiles that better match your requirements.Search for developers on specific sites, like Stack Overflow, through Google by including the site: operator and terms found exclusively on member profiles. Excluding more terms reduces false positives. When running this command on Google, you’ll get results that include the words “resume” or “CV” in the page title. (intitle:resume OR intitle:cv) “android developer” -job -jobs -sample -examples Here’s an example of a generic search string that you can further modify to find resumes:
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