The popularity of large-scale real-time analytics applications (real-time inventory/pricing, recommendations from mobile apps, fraud detection, risk analysis, IoT, etc.) keeps rising. These applications require distributed data management systems that can handle fast concurrent transactions (OLTP) and analytics on the recent data. Some of them even need running analytical queries (OLAP) as part of transactions. Efficient processing of individual transactional and analytical requests, however, leads to different optimizations and architectural decisions while building a data management system. For the kind of data processing that requires both analytics and transactions, Gartner recently coined the term Hybrid Transactional/Analytical Processing (HTAP). Many HTAP solutions are emerging both from the industry as well as academia that target these new applications. While some of these are single system solutions, others are a looser coupling of OLTP databases or NoSQL systems with analytical big data platforms, like Spark. The goal of this tutorial is to 1-) quickly review the historical progression of OLTP and OLAP systems, 2-) discuss the driving factors for HTAP, and finally 3-) provide a deep technical analysis of existing and emerging HTAP solutions, detailing their key architectural differences and trade-offs.