What are the two common components of an event streaming platform?

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Event streaming platforms have become increasingly popular in recent years due to their ability to process and analyze large volumes of real-time data. These platforms enable organizations to keep up with the fast-paced modern world by providing a scalable and reliable solution for event-based processing. While there are several components that make up a complete event streaming platform, two of the most common ones are event ingestion and event processing.

## Event Ingestion.

Event ingestion is the process of capturing and collecting events from a variety of sources. In an event streaming platform, events can come from diverse data sources such as databases, sensors, applications, and even social media platforms. The primary goal of event ingestion is to gather this data in real-time and make it available for further processing and analysis.

### Data Sources.

Event ingestion platforms provide connectors to different data sources, enabling organizations to easily integrate and ingest data from various systems. These connectors may include database connectors, file connectors, messaging system connectors, and more. By offering a wide range of data source connectors, event ingestion allows organizations to capture events from virtually any system or device.

### Data Collection and Streaming.

Once the data sources are connected, the event ingestion component collects the events and streams them into the event streaming platform. This streaming process ensures that events are ingested in real-time, enabling organizations to access and analyze up-to-the-minute data. The streaming mechanism can be based on a publish-subscribe model, where events are published to a topic and subscribed to by interested consumers.

## Event Processing.

Event processing is the second common component of an event streaming platform. Once events are ingested, they need to be processed and analyzed to extract meaningful insights. Event processing provides the capabilities to transform, enrich, filter, and aggregate events in real-time.

### Stream Processing.

Stream processing is a key aspect of event processing. It involves applying real-time transformations and computations on the ingested events as they flow through the platform. Stream processing enables organizations to derive valuable insights from the data as it arrives, allowing them to make informed decisions in real-time.

### Event Enrichment and Filtering.

Event processing platforms offer various techniques for enriching and filtering events. Enrichment involves enhancing the events with additional context or metadata, often by joining them with related data sources. Filtering, on the other hand, allows organizations to discard or prioritize specific events based on predefined criteria. These capabilities help organizations focus on the most relevant and valuable events for their specific use case.

### Aggregation and Analytics.

Another crucial aspect of event processing is the ability to aggregate and analyze events. Platforms often provide functionalities for grouping events based on specific criteria, such as time windows or event types. Aggregation allows organizations to summarize and gain insights from the raw event stream. Additionally, event processing platforms may offer built-in analytics capabilities or integrations with external analytics tools, enabling organizations to perform complex analyses on the streaming data.

In conclusion, event streaming platforms consist of various components that work together to enable organizations to process and analyze real-time events effectively. Event ingestion and event processing are two essential components that form the foundation of such platforms. Event ingestion collects events from diverse sources and streams them into the platform. Event processing, on the other hand, applies real-time transformations, enrichment, filtering, and analytics, enabling organizations to derive valuable insights from the streaming data. By harnessing the power of these components, organizations can stay ahead in today's fast-paced world by making data-driven decisions in real-time.

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