Menu
Sign In Search Podcasts Charts People & Topics Add Podcast API Pricing
Podcast Image

Confluent Developer ft. Tim Berglund, Adi Polak & Viktor Gamov

Engaging Database Partials with Apache Kafka for Distributed System Consistency ft. Pat Helland

20 May 2021

Description

When compiling database reports using a variety of data from different systems, obtaining the right data when you need it in real time can be difficult. With cloud connectivity and distributed data pipelines, Pat Helland (Principal Architect, Salesforce) explains how to make educated partial answers when you need to use the Apache Kafka® platform. After all, you can’t get guarantees across a distance, making it critical to consider partial results.Despite best efforts, managing systems from a distance can result in lag time. The secret, according to Helland, is to anticipate these situations and have a plan for when (not if) they happen. Your outputs may be incomplete from time to time, but that doesn’t mean that there isn’t valuable information and data to be shared. Although you cannot guarantee that stream data will be available when you need it, you can gather replicas within a batch to obtain a consistent result, also known as convergence. Distributed systems of all sizes and across large distances rely on reference architecture for database reporting. Plan and anticipate that there will be incomplete inputs at times. Regardless of the types of data that you’re using within a distributed database, there are many inferences that can be made from repetitive monitoring over time. There would be no reason to throw out data from 19 machines when you’re only waiting on one while approaching a deadline. You can make the sources that you have work by making the most out of what is available in the presence of a partition for the overall distributed database.Confluent Cloud and convergence capabilities have allowed Salesforce to make decisions very quickly even when only partial data is available using replicated systems across multiple databases. This analytical approach is vital for consistency for large enterprises, especially those that depend on multi-cloud functionality. EPISODE LINKSWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Kafka streaming in 10 minutes on Confluent CloudUse 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

Audio
Featured in this Episode

No persons identified in this episode.

Transcription

This episode hasn't been transcribed yet

Help us prioritize this episode for transcription by upvoting it.

0 upvotes
🗳️ Sign in to Upvote

Popular episodes get transcribed faster

Comments

There are no comments yet.

Please log in to write the first comment.