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The Evaluation of TPC-H on Spark and Spark SQL in ALOJA was conducted at the Big Data Lab to obtain the master degree in Management Information Systems at the Johann-Wolfgang Goethe University in Frankfurt, Germany. Furthermore, the analysis was partially accomplished in collaboration and close coordination with the Barcelona Super Computer Center. The intention of this research was the integration of a TPC-H on Spark Scala benchmark into ALOJA, an open-source and public platform for automated and cost-efficient benchmarks and to perform an evaluation on the runtime of Spark Scala with or without Hive Metastore compared to Spark SQL. Various alternate file formats with different applied compressions on underlying data and its impact are evaluated. The conducted performance evaluation exposed diverse and captivating outcomes for both benchmarks. Further investigations attempt to detect possible bottlenecks and other irregularities. The aim is to provide an explanation to enhance knowledge of Spark’s engine based on examining the physical plans. Our experiments show, inter alia, that: (1) Spark Scala performs better in case of heavy expression calculation, (2) Spark SQL is the better choice in case of strong data access locality in combination with heavyweight parallel execution. In conclusion, diverse results were observed with the consequence that each API has its advantages and disadvantages. Surprisingly, our findings are well spread between Spark SQL and Spark Scala and contrary to our expectations Spark Scala did not outperform Spark SQL in all aspects but support the idea that applied optimizations appear to be implemented in a different way by Spark for its core and its extension Spark SQL. The API on top of Spark provides extra information about the underlying structured data, which is probably used to perform additional optimizations. In conclusion, our research demonstrates that there are differences in the generation of query execution plans that goes hand-in-hand with similar discoveries leading to inefficient joins, and it underlines the importance of our benchmark to identify disparities and bottlenecks. Speaker Raphael Radowitz, Quality Specialist, SAP Labs Korea

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Enabling Low-cost Open Data Publishing and Reuse

In the space of just a few years we’ve seen the transformational power of open data; both for transparency and accountability in public data, and efficiency and innovation with businesses in private data. In its first year, institutions and individuals throughout Europe have supported public sector bodies in releasing data and numerous start-ups, developers and SMEs in reusing this data for economic benefit. However, we are still at the beginning of the open data movement, and there is still more that can be done to make open data simpler to use and to make it available to a wider audience. The core goal of the DaPaaS project is to provide a Data- and Platform-as-a-Service environment, where 3rd parties (such as governmental organisations, SMEs, developers and larger companies) can publish and host both data sets and data-intensive applications, which can then be accessed by end-user applications in a cross-platform manner. You can find out more about DaPaaS on the detailed about page. Essentially, DaPaaS aims to make publishing, consumption, and reuse of open data, as well as deploying open data applications, easier and cheaper for SMEs and small public bodies which otherwise may not have sufficient technical expertise, infrastructure and resources required to do so. see also http://www.slideshare.net/eswcsummerschool/wed-roman-tutopendatapub-38742186

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The career development of our teammates is among the key responsibilities of a leader - and оur personal career development vision & plan plays a critical role for our long term growth and success. Despite their importance, our career vision is often not getting enough attention and level of detail, or is hampered by easily avoidable mistakes. In this discussion, we’ll address typical mistakes related to long-term career planning, some best practices, and practical steps for building our own long-term career development vision (or the ones of the teammates we are leading), so that career planning becomes a long term journey with clear why/how/what, rather than just a list of SMART goals

Open Source @ Uber

Uber began its open source journey in 2015 when three passionate engineers decided to contribute Uber’s work back to the community. In only four years, Uber’s open source program has fostered 350+ outstanding open source projects with 2,000+ contributors worldwide delivering over 70,000 commits. Since 2017, four of Uber’s open source projects have won InfoWorld’s Best of Open Source Software Awards. In this talk, Brian Hsieh & Marin Dimitrov will share more details on Uber’s open source journey, program and best practices, and how Uber enables open innovation by fostering a healthy and collaborative open source culture

Trust - the Key Success Factor for Teams & Organisations

>>> Most leaders agree that trust is a key factor for the success o the team and the organisation and that they are actively working to build trust. And yet, various studies imply that almost half of the teams and organisations worldwide experience lower trust levels with their managers, teammates and the rest of the organisation, which leads to decreased engagement, productivity and success. >>> In this talk we will discuss why trust is a key success factor for every team and every organisation, some good practices for building, sustaining and rebuilding trust, as well as the most common mistakes related to trust building

Uber @ Telerik Academy 2018

Machine Learning @ Uber

Career Advice for My Younger Self

Scaling Your Engineering Organization with Distributed Sites

Building, Scaling and Leading High-Performance Teams

The document discusses building, scaling, and leading high-performance teams. It covers cultural values, attracting top talent through transparent hiring processes and a magical interview experience, coaching and growth through onboarding, knowledge sharing, mentoring, and feedback, and leadership through execution, vision, emotional intelligence, and effective team design. The speaker is an engineering manager sharing experiences from Uber on developing teams and talent.

Uber @ Career Days 2017 (Sofia University)

Uber's engineering team aims to build highly scalable, available, and flexible platforms to achieve Uber's mission of providing transportation that is as reliable as running water everywhere for everyone. Uber currently operates in over 600 cities across 80 countries. The platforms need to handle data from tens of millions of daily trips while ensuring riders and drivers can access documents and data 24/7. Uber also aims to build flexibility into its platforms to meet various compliance requirements in the over 80 countries it operates in worldwide.

Career Days 2012 @ Sofia University

Linked Data for the Enterprise: Opportunities and Challenges

1) Semantic technologies and linked data can help address challenges of integrating disparate data sources and providing unified access to enterprise information. 2) Case studies demonstrate successes in areas like semantic search, knowledge discovery, and dynamic publishing by linking and enriching content. 3) Adoption challenges include developing domain ontologies, query performance, data quality, and getting enterprise IT teams familiar with semantic technologies.

Semantic Technologies and Triplestores for Business Intelligence

Linked Data Marketplaces

This document discusses data marketplaces and the potential benefits of linked data for data marketplaces. It provides an overview of several existing data marketplaces including Factual, InfoChimps, Azure DataMarket, Freebase, Socrata, and Kasabi. These marketplaces vary in their data domains, models, sizes, monetization approaches, and tools for data access. The document also outlines benefits of the semantic web and linked data for data marketplaces, such as unified data representation, global identifiers, interlinked datasets, and easy integration of existing linked open data. However, challenges include ensuring data quality and performing large-scale data integration across different schemas.

Linked Data Management