Kafka Webinar Series On-Demand
According to the 2018 Apache Kafka Report, 94% of organizations plan to deploy new applications or systems using Kafka this year. At the same time, 77% of those same organizations say that staffing Kafka projects has been somewhat or extremely challenging.
In this multi-part webinar series, StreamSets will take learnings from our customers and share practical tips for making headway with Kafka. Each session will discuss common challenges and provide step-by-step details for how to avoid them. By the end of the series you'll have many more tools at your disposal for ensuring your Kafka project is a success.
Watch any session by clicking on the Title Link below.
Getting started with Kafka can be harder than it needs to be. Building a cluster is one thing, but ingesting data into that cluster can require a lot of experience and often a lot of rework. During this session we'll demystify the process of creating pipelines for Apache Kafka and show how you can create Kafka pipelines in minutes, not hours or days. In this session you'll learn:
- Designing any-to-any Kafka pipelines in minutes
- Snapshotting and monitoring data in Kafka
- Editing pipelines quickly and easily without major disruption
When it comes to scaling out Apache Kafka, there's often a trade off between complexity, performance and cost. In this session, we'll look at five different ways to scale up to handle massive message throughput with Kafka and StreamSets In this session you'll learn:
- Scaling pipelines vertically and horizontally
- Getting scale by streaming in a cluster
- Leveraging Kubernetes to elastic scaling
Kafka and Tensorflow can be used together to build comprehensive machine learning solutions on streaming data. Unfortunately, both can become black boxes and it can be difficult to understand what's happening as pipelines are running. In this talk we'll explore how StreamSets can be used to build robust machine learning pipelines with Kafka. In this session you'll learn:
- How to easily build pipelines with Tensorflow and Kafka
- Visualizing data in Tensorflow pipelines
- Creating reusable code fragments for standardizing pipeline best practices
With streaming platforms like Kafka, data arguably never rests. As data flows through and across data sources and destinations, it's possible that sensitive data goes unnoticed and potentially gets in the hands of the wrong people or land in the wrong application. In-stream data protection helps ensure that any data flowing from Kafka is protected from unwanted use and exposure. In this session you'll learn:
- How to implement global data policies for all streaming data
- Detecting and protecting sensitive data within individual Kafka pipelines
- Implementing multiple data security policies to augment data at rest solutions
