Abstract:
Technological innovation as driven by startups is hard to formalize (and manage) as the context may be unknown or quickly changing. To be successful, the innovation process involves not only inventions (new methods) but also context, e.g. user behavior, and timing, e.g. market readiness. In this talk, I illustrate various innovation strategies based on startup success stories, in particular LeanXcale, which delivers a new generation HTAP DBMS product. I also give hints to promote innovation within startups.

Abstract:
NewSQL is the latest technology in the big data management landscape, enjoying a fast growing rate in the DBMS and BI markets. NewSQL combines the scalability and availability of NoSQL with the consistency and usability of SQL. By providing online analytics over operational data, NewSQL opens up new opportunities in many application domains where real-time decision is critical. Important use cases are Google Adwords, proximity marketing, real-time pricing, risk monitoring, real-time fraud detection, etc. NewSQL may also simplify data management, by removing the traditional separation between operational database and data warehouse / data lake (no more ETLs!). However, a hard problem is scaling out transactions in mixed operational and analytical (HTAP) workloads over big data, possibly coming from different data stores (HDFS, SQL, NoSQL). Today, only a few NewSQL systems have solved this problem. This tutorial provides an in-depth presentation of NewSQL, with its principles, architectures and techniques. We provide a taxonomy of NewSQL systems based on major dimensions including targeted workloads, capabilities and implementation techniques. We illustrate with popular NewSQL systems such as Google F1/Spanner, LeanXcale, CockroachDB, SAP HANA, MemSQL and Splice Machine. In particular, we give a spotlight on some of the more advanced systems. We also compare with major NoSQL and SQL systems, and discuss integration within big data ecosystems and corporate information systems. Finally, we discuss the current trends and research directions.

Abstract:
The first edition of the book Principles of Distributed Database Systems, co-authored with Prof. Tamer Özsu (University of Waterloo) appeared in 1991 when the technology was new and there were not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker who claimed in 1988 that in the following 10 years, centralized DBMSs would be an “antique curiosity” and most organizations would move towards distributed DBMSs. That prediction has certainly proved to be correct, and most systems in use today are either distributed or parallel. The fourth edition of this classic textbook provides major updates, in particular, new chapters on big data platforms, NoSQL, NewSQL and polystores. In this tutorial, we introduce these major updates, with a focus on NewSQL. A first in-depth presentation of NewSQL was given in a tutorial at IEEE Big Data 2019 with Prof. Ricardo Jimenez-Peris (CEO and founder at LeanXcale). In this tutorial, I provide a taxonomy of NewSQL systems based on major dimensions including targeted workloads, capabilities and implementation techniques. I illustrate with popular NewSQL systems such as Google Spanner, LeanXcale, CockroachDB, SAP HANA, MemSQL and Splice Machine. Finally, I discuss the current trends and research directions.

Abstract:
NewSQL is the latest technology in the big data management landscape, enjoying a fast-growing rate in the DBMS and BI markets. NewSQL combines the scalability and availability of NoSQL with the consistency and usability of SQL. By blending capabilities only available in different kinds of database systems such as fast data ingestion and SQL queries and by providing online analytics over operational data, NewSQL opens up new opportunities in many application domains where real-time decision is critical. Important use cases are eAdvertisement (such as Google Adwords), IoT, performance monitoring, proximity marketing, risk monitoring, real-time pricing, real-time fraud detection, etc. NewSQL may also simplify data management, by removing the traditional separation between NoSQL and SQL (ingest data fast, query it with SQL), as well as between operational database and data warehouse / data lake (no more ETLs!). However, a hard problem is scaling out transactions in mixed operational and analytical (HTAP) workloads over big data, possibly coming from different data stores (HDFS, SQL, NoSQL). Today, only a few NewSQL systems have solved this problem. In this talk, I introduce the solution for scalable transaction and polystore data management in LeanXcale, a recent NewSQL DBMS.