Confluent Platform: Data Streaming for the Enterprise

In a command window, run the following commands to experiment with topics. Confluent Platform ships with Kafka commands and utilities in $CONFLUENT_HOME/bin. This bin/ directory includes both Confluent proprietary and open source Kafka utilities. Learn how Kora powers Confluent Cloud to be a cloud-native service that’s scalable, reliable, and performant. Confluent Platform ships a number of command line interface (CLI) tools,
including the Confluent CLI. These are all listed under CLI Tools Shipped With Confluent Platform
in the Confluent documentation, including both Confluent provided and Kafka utilities.

And if the system gets overwhelmed, Kafka can act as a buffer, absorbing the backpressure. Another use case is using change data capture (CDC) to allow your relational technologies to send data through Kafka to technologies such as NoSQL stores, other event-driven platforms, or microservices—letting you unlock static data. In these circumstances, Kafka can serve as a message broker as well as an independent system of record. Used by over 70% of the Fortune 500, Apache Kafka has become the foundational platform for streaming data, but self-supporting the open source project puts you in the business of managing low-level data infrastructure. With Kafka at its core, Confluent offers complete, fully managed, cloud-native data streaming that’s available everywhere your data and applications reside.

• Step through the basics of the CLI, Kafka topics, and building applications.
• The following image shows an example of Control Center running in Normal mode.
• Reduced infrastructure mode means that no metrics and/or monitoring data is visible in Control Center and
  internal topics to store monitoring data are not created.
• Today, over
  80% of the Fortune 100 are powered by data streaming technology – and the majority of those leverage
  Confluent.
• If you’re a developer, using Confluent means that you get to use Kafka without having to set it up on your own servers.

Events have a tendency to proliferate—just think of the events that happened to you this morning—so we’ll need a system for organizing them. Apache Kafka’s most fundamental unit of organization is the topic, which is something like a table in a relational database. As a developer using Kafka, the topic is the abstraction you probably think the most investing to beat inflation about. You create different topics to hold different kinds of events and different topics to hold filtered and transformed versions of the same kind of event. All of these are examples of Kafka connectors available in the Confluent Hub, a curated collection of connectors of all sorts and most importantly, all licenses and levels of support.

Kafka 101 and terminology

Confluent Cloud provides Kafka as a cloud service, so that means you no longer need to install, upgrade or patch Kafka server components. You also get access to a cloud-native design, which offers Infinite Storage, elastic scaling and an uptime guarantee. Confluent Control Center is a GUI-based system for managing and monitoring Kafka. It allows you to easily manage Kafka Connect, to create, edit, and manage connections to other systems.

They help
ensure that data is consistent, accurate, and can be efficiently processed and
analyzed by different systems and applications. Schemas facilitate data sharing
and interoperability between different systems and organizations. Kafka is a powerful platform, but it doesn’t offer everything you need out-of-the-box.

• A modern system is typically a distributed system, and logging data must be centralized from the various components of the system to one place.
• For example, a payment, a website click, or a temperature reading, along with a description of what happened.
• Apache Kafka® is an enormously successful piece of data infrastructure, functioning as the ubiquitous distributed log underlying the modern enterprise.
• These clients are usually the easiest, fastest, and most secure way to
  communicate directly with Kafka.
• Connect, process, and react to your data in real time with a complete, self-managed platform for data in motion.

A bonus optimization, one that is also demo oriented, is to write your connector instances into a startup script, rather than adding them after the worker is already running. You can also use ksqlDB to add connector instances or you can add them from the Confluent Cloud console. Create, import, share streams of events like payments, orders, and database changes in milliseconds, at scale. Confluent Platform is a streaming platform that enables you to organize and manage data from many different sources with one reliable, high performance system. An abstraction of a distributed commit log commonly found in distributed databases, Apache Kafka provides durable storage. Kafka can act as a ‘source of truth’, being able to distribute data across multiple nodes for a highly available deployment within a single data center or across multiple availability zones.

With open source Kafka alone, you’re on the hook to build and maintain foundational tooling and infrastructure, such as connectors, data governance and security, disaster recovery capabilities, and more. And when ready to deploy, the platform creates a significant ongoing operational burden — one that only grows over time. If all you had were brokers managing partitioned, replicated topics with an ever-growing collection of producers and consumers writing and reading events, you would actually have a pretty useful system.

Org.apache.Kafka.connect.transforms.cast$Key will let you cast a few of your data fields to a new type before they are written into Kafka and org.apache.Kafka.connect.transforms.TimestampConverter will let you convert message timestamps. Identify your new settings in the JSON configuration for your connector then finally launch the connector to view the actual transformed data. Kafka Connect is the pluggable, declarative data integration framework for Kafka. It connects data sinks and sources to Kafka, letting the rest of the ecosystem do what it does so well with topics full of events. As is the case with any piece of infrastructure, there are a few essentials you’ll want to know before you sit down to use it, namely setup and configuration, deployment, error handling, troubleshooting, its API, and monitoring. Confluent Developer recently released a Kafka Connect course covering these topics, and in each of the sections below, I’d like to share something about the content of each lesson in the course.

Using the Confluent Cloud managed connector API

For an example Docker compose file for Confluent Platform, refer to
Confluent Platform all-in-one Docker Compose
file. The file is for a quick start tutorial
and should not be used in production environments. Confluent Platform includes of a full-featured, high-performance client for Go. For more information, see the ksqlDB documentation,
ksqlDB Developer site, and
ksqlDB getting started guides on the website. Confluent Security Plugins are used to add security capabilities to various Confluent Platform tools and products.

Using the Confluent Cloud managed connector CLI

Create a Kafka topic to be the target for a Datagen source connector, then check your available plugins, noting that Datagen is present. Next, set up a downstream MySQL sink connector, which will receive the data produced by your Datagen connector. Once you’ve finished, learn how to inspect the config for a connector, how to buy coinbase stock how to pause a connector (verifying that both the connector and task are paused by running a status command), then how to resume the connector and its task. Schemas are used in various data processing systems, including databases,
message brokers, and distributed event and data processing frameworks.

Go above & beyond Kafka with all the essential tools for a complete data streaming platform. Kafka provides high throughput event delivery, and when combined with open-source technologies such as Druid can form a powerful Streaming Analytics Manager (SAM). Events are first loaded in Kafka, where they are buffered in Kafka brokers before they are consumed by Druid real-time workers. For a multi-cluster deployment, you must configure and start as many ZooKeeper instances
as you want clusters, and multiple Kafka server properties files (one for each
broker).

Apache Kafka® Service Powered by the Kora Engine

Most software companies record every single website visit and click, and some go even deeper. Once you have more than few users interacting with your product, you’re talking about millions of different events per day. This is
relevant for trying out features like Replicator, Cluster Linking, and
multi-cluster Schema Registry, where you want to share or replicate topic data across two
clusters, often modeled as the origin and the destination cluster.

To a first-order approximation, this is all the API surface area there is to producing messages. This happens automatically, and while you can tune some settings in the producer to produce varying levels of durability guarantees, this is not usually a process you have to think about as a developer building systems on Kafka. All you really need to know as a developer is that your data is safe, and that if one node in the cluster dies, another will take over its role. Whether brokers are bare metal servers or managed containers, they and their underlying storage are susceptible to failure, so we need to copy partition data to several other brokers to keep it safe.

Help with solving Kafka-esque data problems

Confluent Hub has downloadable connectors for the most popular data sources and sinks. Tiered Storage provides options for storing large volumes of Kafka data
using your favorite cloud provider, thereby reducing operational burden and cost. With Tiered Storage, you can keep data on cost-effective object storage, and
scale brokers only when you need more compute resources. Each release of Confluent Platform includes the latest release of Kafka and additional tools and services that make it easier
to build and manage an Event Streaming Platform. Confluent Platform delivers both community and commercially licensed features that
complement and enhance your Kafka deployment.

This quick start shows you how to run Confluent Platform using Docker on a single broker, single cluster
development environment with topic replication factors set to 1. Check out the announcement blog to learn how we’ve re-architected Flink as a cloud-native service to provide simple, serverless stream processing. But sometimes, it isn’t practical to write and maintain an application that uses the native clients.

It allows
you to shift data to create an even workload across your cluster, while throttling rebalance traffic to minimize impact on
production workloads while rebalancing. Looking at what we’ve covered so far, we’ve got a system for storing events durably, the becoming a trader ability to write and read those events, a data integration framework, and even a tool for managing evolving schemas. In this hands-on exercise, learn how to add single message transforms in the Confluent Cloud UI for your Datagen mock data managed connector.