01:09:27
Applied Distributed Research in Apache Cassandra
Jonathan Ellis explains the challenges and successes Cassandra has had in creating transactions, materialized views, and a strongly consistent cluster membership within this peer-to-peer paradigm.
47:56
In-Memory Caching: Curb Tail Latency with Pelikan
Yao Yue introduces Pelikan - a framework to implement distributed caches such as Memcached and Redis. She discusses the system aspects that are important to the performance of such services.
49:40
Causal Consistency for Large Neo4j Clusters
Jim Webber explores the new Causal clustering architecture for Neo4j, how it allows users to read writes straightforwardly, explaining why this is difficult to achieve in distributed systems.
47:03
Real-Time Recommendations Using Spark Streaming
Elliot Chow discusses the data pipeline that they built with Kafka, Spark Streaming, and Cassandra to process Netflix user activities in real time for the Trending Now row.
39:21
Demistifying DynamoDB Streams
Akshat Vig and Khawaja Shams discuss DynamoDB Streams and what it takes to build an ordered, highly available, durable, performant, and scalable replicated log stream.
54:36
ScyllaDB: Achieving No-Compromise Performance
Avi Kivity discusses ScyllaDB, the many necessary design decisions, from the programming language and programming model through low-level details and up to the advanced cache design, and more.
27:42
Spring Data Hazelcast: Fluently Accessing Distributed Repositories
Victor Gamov and Neil Stevenson present using Spring Data for a Hazelcast project, built on the KeyValue module and providing infrastructure components for creating repository abstractions.
25:21
Sneek Peek at Spring Data Cassandra
Mark Paluch and John Blum present the changes in Spring Data Cassandra and what to expect from the upcoming version.
29:38
Next Level Redis with Spring
Christoph Strobl explains Redis' Object Hash Mapping and Secondary Indexes as well as the Spring Data Repository abstraction that lets one seamlessly interact with Redis.
43:42
In-Memory Caching: Curb Tail Latency with Pelikan
Yao Yue introduces Pelikan, a framework to implement distributed caches such as Memcached and Redis.
01:06:28
But I Need a Database that _Scales_
Aaron Spiegel reviews common scaling techniques for both relational and NoSQL databases, discussing trade-offs of these techniques and their effect on query flexibility, transactions and consistency.
31:29
Cassandra and DataStax Enterprise on PCF
Ben Lackey and Cornelia Davis start with the use cases for on-demand, dedicated DSE clusters, cover the solution design, and demo the system, touching also the support that Spring has for Cassandra.
CONTENT IN THIS BOX PROVIDED BY OUR SPONSOR
|
Database as a Service for MongoDB. Simple, elegant, secure. Import your data with ease.
|
|
Featured Blog Post |
New — Live Migrations, More AWS Regions, Queryable Backups & More in MongoDB Atlas
Announcing Free Tier and Live Migration Tool for MongoDB Atlas
Developing a Facebook Chatbot with AWS Lambda and MongoDB Atlas
|
Featured Case Studies |
Thoughtfulness at scale is a giant idea. What's yours?
By building a robotic fleet to send "handwritten" notes across the world, Bond helps companies be more thoughtful in a scalable way.
Leaf in the Wild: World’s Most Installed Learning Record Store Migrates to MongoDB Atlas to Scale Data 5x, while Reducing Costs.
Learning Locker moves away from ObjectRocket to scale its learning data warehouse, used by the likes of Xerox, Raytheon and U.K. Universities.
Thermo Fisher moves into the cloud with MongoDB Atlas & AWS.
Biotechnology giant uses MongoDB Atlas and an assortment of AWS technologies and services to reduce experiment times from days to minutes.
|
On-demand webinars |
Serverless Architectures with AWS Lambda and MongoDB Atlas
Developing with the Modern App Stack: MEAN and MERN (with Angular2 and ReactJS)
Simplifying the Database Experience with MongoDB Atlas
Follow MongoDB on:
