Tim Berglund
ÜberConf
Denver · July 18 - 21, 2017
VP Developer Relations at Confluent
Tim is a teacher, author, and technology leader with Confluent, where he serves as the Vice President of Developer Relations. He is a regular speaker at conferences and a presence on YouTube explaining complex technology topics in an accessible way. He tweets as @tlberglund, blogs every few years at http://timberglund.com. He has three grown children and two grandchildren, a fact about which he is rather excited.
Presentations
Graph Theory You Need to Know
Which marriages in the village will last? Which chicken is queen of the coop? How many crayons do you need to color a county map of Tennessee? What do all of these questions have in common? They're all graph problems.
Spark Workshop
Apache Cassandra is a leading open-source distributed database capable of amazing feats of scale, but its data model requires a bit of planning for it to perform well. Of course, the nature of ad-hoc data exploration and analysis requires that we be able to ask questions we hadn’t planned on asking—and get an answer fast. Enter Apache Spark.
Four Distributed Systems Architectural Patterns
Developers and architects are increasingly called upon to solve big problems, and we are able to draw on a world-class set of open source tools with which to solve them. Problems of scale are no longer consigned to the web’s largest companies, but are increasingly a part of ordinary enterprise development. At the risk of only a little hyperbole, we are all distributed systems engineers now.
Distributed Systems in One Lesson
Normally simple tasks like running a program or storing and retrieving data become much more complicated when we start to do them on collections of computers, rather than single machines. Distributed systems has become a key architectural concern, and affects everything a program would normally do—giving us enormous power, but at the cost of increased complexity as well.
Distributed Systems in One Lesson
Normally simple tasks like running a program or storing and retrieving data become much more complicated when we start to do them on collections of computers, rather than single machines. Distributed systems has become a key architectural concern, and affects everything a program would normally do—giving us enormous power, but at the cost of increased complexity as well.
Streaming Data with Apache Kafka
The toolset for building scalable data systems is maturing, having adapted well to our decades-old paradigm of update-in-place databases. We ingest events, we store them in high-volume OLTP databases, and we have new OLAP systems to analyze them at scale—even if the size of our operation requires us to grow to dozens or hundreds of servers in the distributed system. But something feels a little dated about the store-and-analyze paradigm, as if we are missing a new architectural insight that might more efficiently distribute the work of storing and computing the events that happen to our software. That new paradigm is stream processing.
Kafka Connect
Your goal is simple: take that is happening in your company—every click, every database change, every application log—and made it all available as a real-time stream of well-structured data? No big deal! You’re just taking your decades-old, batch-oriented data integration and data processing and migrating to to real-time streams and real-time processing. In your shop, you call that Tuesday. But of the several challenges to tackle, you’ll have to get data in and out of that stream processing system, and there’s a whole bunch of code there you don’t want to write. This is where Kafka Connect comes in.