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See Us at Distributed Data Summit 2018

We’re pleased to announce that ScyllaDB is a diamond sponsor at the upcoming Distributed Data Summit – Apache Cassandra and Friends in San Francisco on September 14th. We’re excited to be part of this event, which brings together the top Apache Cassandra leaders and practitioners for one day of technical sessions. Like all Global Data Geeks events, you can also expect time for networking (and free drink tickets) at Happy Hour!

Distributed Data Summit

Interested in attending? Our diamond sponsorship includes a half-price discount for our guests, so use this link to save 50% off your registration, or apply this code when ordering through Eventbrite: “friend-of-scylladb-50”.

ScyllaDB CEO Dor Laor and CTO Avi Kivity will each give talks at the summit, plus Yahoo! Japan will present on their use of Cassandra and Scylla. Here is an overview of those sessions:

Cassandra and ScyllaDB at Yahoo! Japan
Shogo Hoshii – Yahoo! Japan
Murukesh Mohanan – Yahoo! Japan
Yahoo! JAPAN is one of the most successful internet service companies in Japan. Shogo will introduce his company’s business and its scale. He will go on to discuss the number of Cassandra nodes and clusters, and what kind of data is stored in them. He will go on to introduce some of the incidents that happened with the company’s Cassandra implementation, and how they managed to handle them. Murukesh Mohanan will go on to discuss the Yahoo! Japan NoSQL Team’s evaluation of ScyllaDB as a successor of Cassandra in exceedingly heavy traffic.

OLTP or Analytics? Why not both?
Avi Kivity – ScyllaDB
OLTP and Analytics are very different. One is characterized by many concurrent small requests, with a high sensitivity to latency, while the other typically processes large streams of data with more emphasis on throughput.
Avi’s talk will cover:

  • The different requirements of the two workloads
  • How ScyllaDB optimizes for both
  • Performance isolation of different workloads within ScyllaDB
  • How ScyllaDB supports concurrent OLTP and Analytics without sacrificing either latency or throughput
  • Measurements

Shard per core vs threads in databases
Avi Kivity – ScyllaDB
This talk will cover the shard-per-core architecture of Scylla and compare it to the traditional threaded design employed by Cassandra. The recent adoption of thread-per-core by Datastax is further evidence of the gains this architecture offers over a threaded design. We will discuss the various design decisions that make a shard-per-core design ideal and how to tackle the architecture issues that come with it.

Was Cassandra the right baseline for ScyllaDB?
Dor Laor – ScyllaDB
The ScyllaDB team has been re-implementing Cassandra in C++ for the past 4 years, but was Cassandra the right baseline? Sure, Cassandra has hundreds of people years behind it and design wins that other frameworks can only imagine, but there are caveats too. If you follow the rest of the industry, from multi-tenant CosmosDB to transactional CockroachDB there’s a lot going on. Even within the Cassandra community there are different voices around sidecar management processes.
It’s a good time to take a step back and consider whether it was right to embrace the Cassandra design and APIs versus starting from a clean slate. This talk will cover programming language choices (Java, Go, C++), the use of existing engines (RocksDB), implementation of secondary indexes and management consoles, and more.

Summit Details
Distributed Data Summit – Apache Cassandra and Friends
Register and save 50%
Mission Bay Conference Center
1675 Owens Street
San Francisco, CA 94158
Friday, September 14, 2018 from 8:00 AM to 8:00 PM (PDT)

We hope to see you there! Stop by our booth to talk about your real-time big data projects, see a demo of Scylla, pick up swag and get some free drink tickets.

Next Steps

  • Scylla Summit 2018 is around the corner. Register now!
  • Learn more about Scylla from our product page.
  • See what our users are saying about Scylla.
  • Download Scylla. Check out our download page to run Scylla on AWS, install it locally in a Virtual Machine, or run it in Docker.