I started my career as an options trader at J.P. Morgan and then moved on to build their first modern data stack. Currently, I am a principal analytics leader at Fivetran, an up-and-coming market leader in data integration. Along the way, I've had to learn how to build complex information systems the hard way. Today, I want to share some best practices I've picked up that will help you succeed. The modern data stack is a set of cloud-native data tools centered around automation, lowered costs, and ease-of-use to end users throughout the lifecycle of data management.
Azure Synapse Analytics is an unlimited information analysis service aimed at large companies that was presented as the evolution of Azure SQL Data Warehouse (SQL DW), bringing together business data storage and macro or Big Data analysis. Synapse provides a single service for all workloads when processing, managing and serving data for immediate business intelligence and data prediction needs. The latter is made possible by its integration with Power BI and Azure Machine Learning, due to Synapse's ability to integrate mathematical machine learning models using the ONNX format. It provides the freedom to handle and query huge amounts of information either on demand serverless (a type of deployment that automatically scales power on demand when large amounts of data are available) for data exploration and ad hoc analysis, or with provisioned resources, at scale. As one of the few Microsoft's Power BI partners in Spain, at Bismart we have a large experience working with both Power BI and Azure Synapse.
From Hammurabi's stone tablets to papyrus rolls and leather-bound books, the Arab region has a rich history of recordkeeping and transactional systems that closely matches the evolution of data storage mediums. Even modern-day data management concepts like data provenance and lineage have historic roots in the Arab world; generations of scribes meticulously tracked Islamic prophetic narrations from one narrator to the next, forming lineage chains that originated from central Arabia. Database systems research has been part of the academic culture in the Arab world since the 1970s. High-quality computer science and database education was always available at several universities within the Arab region, such as Alexandria University in Egypt. Many students who went through these programs were drawn to database systems research and became globally prominent, such as Ramez Elmasri (professor at University of Texas, Arlington), Amr El Abbadi (professor at University of California, Santa Barbara), and Walid Aref (professor at Purdue University).
The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.
Artificial intelligence is no more the future of marketing; it is pretty much at the moment. Consider all the ways AI technology has already started contributing to our everyday lives. Artificial intelligence is progressively becoming a central part of numerous industries and has various use cases, particularly in marketing. All businesses, big or small, have begun using AI to some extent to upgrade their website, products, and customer experience over time. If reports are to be believed, the top-performing organizations are more than 2x likely than their peers to use AI for marketing purposes.
As known from many articles and publications, SAP offers three solutions for data warehousing. The SAP Business Warehouse (BW) was first published in 1997 and has therefore been a constant figure in the SAP Data Warehouse range for more than two decades. With HANA as a database platform, the HANA SQL Data Warehouse approach has been developing since 2015, which initially consisted of loosely coupled tools, but has since evolved into an open, yet highly integrated set of tools and methods, that can also be used to develop large data warehouse systems. Since 2019, the Data Warehouse Cloud has been completing the SAP solution as a SaaS solution. These three approaches are not in competition.
Data Warehousing – In today's flood of data, it is becoming increasingly difficult to maintain a clear data management system. More and more data sources are recorded via different software systems. A unified, centralized system can facilitate analysis and ensure that only one data truth exists in an organization. Data warehouse systems are built by integrating data from multiple heterogeneous sources and, in addition to centralization, performs the task of structuring data, supporting analytical reporting and structuring decision-making. The system can perform data cleansing as well as data integration and data consolidation and does not require transaction processing or recovery.
Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bah\'ia Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.
Classical network embeddings create a low dimensional representation of the learned relationships between features across nodes. Such embeddings are important for tasks such as link prediction and node classification. In the current paper, we consider low dimensional embeddings of dynamic networks, that is a family of time varying networks where there exist both temporal and spatial link relationships between nodes. We present novel embedding methods for a dynamic network based on higher order tensor decompositions for tensorial representations of the dynamic network. In one sense, our embeddings are analogous to spectral embedding methods for static networks. We provide a rationale for our algorithms via a mathematical analysis of some potential reasons for their effectiveness. Finally, we demonstrate the power and efficiency of our approach by comparing our algorithms' performance on the link prediction task against an array of current baseline methods across three distinct real-world dynamic networks.