Apply suggestions from code review

Co-authored-by: Jessica <[email protected]>
Co-authored-by: Benedikt Rollik <[email protected]>

Author: 3 people

pages/clusters-for-kafka/concepts.mdx

@@ -22,23 +22,23 @@ Clusters for Apache Kafka® is Scaleway's product to provide a secure, fully-man
 
 ## Credentials
 
-Credentials consist of a username and password that allow you to connect to your cluster. These are defined during cluster creation. During Private Beta, a limit of one user (ie one set of credentials) per cluster is applied.
+Credentials consist of a username and password that allow you to connect to your cluster. These are defined during cluster creation. During Private Beta, a limit of one user (i.e. one set of credentials) per cluster is applied.
 
 ## Data replication
 
 Data replication is the mechanism that ensures event data is copied across multiple brokers in a cluster, for fault tolerance and high availability. If a node fails, data replication means Apache Kafka® can keep serving data without downtime or data loss.
 
 ## Endpoint
 
-During Private Beta, access to your Cluster for Apache Kafka® is via private endpoints only. When creating your cluster, you must attach it to a [Private Network](/vpc/concepts/#private-networks), and all nodes in the cluster receive a private IPv4 endpoint on this network. Endpoints are in a format similar to `bf610fb0-f7eb-4ec3-a893...634aea.Kafka.fr-par.scw.cloud`.
+During Private Beta, access to your Cluster for Apache Kafka® is via private endpoints only. When creating your cluster, you must attach it to a [Private Network](/vpc/concepts/#private-networks), and all nodes in the cluster receive a private IPv4 endpoint on this network. Endpoints are in a format similar to `bf610fb0-f7eb-4ec3-a893...634aea.kafka.fr-par.scw.cloud`.
 
 ## Event streaming
 
-Clusters for Apache Kafka® is specifically designed for implementing event streaming at scale. Event streaming is the process of capturing, storing and processing a continuous flow of events in real time. Each event represents something that happened,e.g.`User added item to cart`, `User clicked 'Pay now'`, `Payment was rejected by bank`. Events are stored in topics, e.g. `Inventory`, `Payment`, `Orders`. Typically, programs and microservices subscribe to these topics to consume their events and take action in relation to them, e.g. `Generate invoice`, `Check for fraud`, `Program delivery`.
+Clusters for Apache Kafka® is specifically designed for implementing event streaming at scale. Event streaming is the process of capturing, storing and processing a continuous flow of events in real time. Each event represents something that happened, e.g.`User added item to cart`, `User clicked 'Pay now'`, `Payment was rejected by bank`. Events are stored in topics, e.g. `Inventory`, `Payment`, `Orders`. Typically, programs and microservices subscribe to these topics to consume their events and take action in relation to them, e.g. `Generate invoice`, `Check for fraud`, `Program delivery`.
 
 ## Node
 
-A Cluster for Apache Kafka® consists of one or more nodes, also called **brokers**. Nodes work together to store, manage and distribute event streams. When creating a cluster, you can choose between **dedicated** nodes or **shared** nodes, based on your workload requirements, budget and use case:
+A Cluster for Apache Kafka® consists of one or more nodes, also called **brokers**. Nodes work together to store, manage, and distribute event streams. When creating a cluster, you can choose between **dedicated** nodes or **shared** nodes, based on your workload requirements, budget, and use case:
 
 - **Dedicated node**: Runs only your cluster, with no other customers sharing the broker's resources. 
 - **Shared node**: Runs your cluster along with other customers', on the same broker.