Apache Kafka Makes Streaming Data Processing Fast and Simple
The scale at which data is being produced and collected by companies today is astonishing. According to Cisco, the average organization now manages 162.9 TB of data. Event streaming (also called stream processing) enables organizations to work with these large volumes of data from various sources as that is being generated. It allows business teams to access data in motion instantly and gather actionable insights or make adjustments on an ad hoc basis. A variety of tools exist to facilitate stream processing, and one of the most popular of these tools is the Apache Kafka streaming platform.
What Is Apache Kafka?
The Kafka streaming platform is a high-throughput, low-latency platform for handling real-time data feeds and, with Kafka Connect, it can connect to external systems for data import and export. It was initially developed to serve as a publish-subscribe messaging system to handle LinkedIn’s vast amounts of data. It’s since evolved into a robust open-source event streaming software platform used by organizations in nearly every industry to collect, process, store, and analyze data at scale.
What Is Event Streaming?
To understand the Kafka streaming platform, it’s helpful to have a solid grasp of event streaming. Event streaming is the process of capturing data in real time from a variety of sources such as internal and external systems, databases, websites, applications, mobile devices, and sensors. This data is generated in continual streams of events. An event (also called a record or message) is defined as a change in state (such as a financial transaction or a customer filling out a web form). Each event has a key, value, time stamp, and optional metadata headers.
Event streaming describes storing these streams; manipulating, processing, and reacting to the event streams; and routing the event streams to different destinations. Thus, event streaming facilitates the continuous flow and interpretation of data.
What You Can Do with Kafka
Apache Kafka has three core capabilities associated with its streaming architecture: publishing (writing) and subscribing to (reading) streams of events (including continuous import/export of data from/to other systems), storing streams of events, and processing streams of events as they occur (or retrospectively).
There’s a reason the Kafka streaming platform is so widely used. It has many different applications for a variety of business types. Here are just a few common use cases.
Data integration: Because Kafka can effectively handle high-throughput real-time data, it’s ideal for data integration.
Metrics and monitoring: Kafka can efficiently aggregate statistics from internal and external applications to produce centralized feeds of operational data.
Log aggregation: You can use Kafka as an improved log aggregation solution. It abstracts away file details and provides a cleaner abstraction of log or event data.
Stream processing: Many people use Kafka simply for its stream processing capabilities. Raw input data can be consumed from Kafka topics and then aggregated, enriched, or transformed in other ways for additional consumption or follow-up processing.
Publish-subscribe messaging: Kafka is ideal for large-scale messaging and user activity tracking, the original use case. The platform features excellent throughput, built-in partitioning, replication, and fault tolerance.
Industries using Kafka
Because there are so many use cases for event streaming, a wide range of industries can benefit from the Kafka streaming platform. Some examples include:
Finance: For processing payments and financial transactions in real time
Healthcare: For monitoring patients in hospitals and predicting changes in patients’ health conditions to ensure fast treatment in emergency situations
Retail: For collecting and immediately responding to customer interactions and purchases
The Kafka Connect API: Powering Import and Export
Kafka offers several APIs for various purposes. The Kafka Connect API allows users to build and run reusable data import/export connectors. These connectors can consume (read) or produce (write) event streams from and to systems and applications for integration. The API makes it easy to quickly define connectors to move even the largest collections of data into and out of Kafka.
Kafka Connect is powerful because it can ingest entire databases and collect metrics across application servers. It makes data available for stream processing with low latency. And an export can deliver data from Kafka topics for offline analysis.
Snowflake and Kafka for Seamless Data Streaming
The Snowflake Connector for Kafka uses the Kafka Connect API to read data from one or more Kafka topics and load the data into a Snowflake table. Increasingly, organizations are finding that they need to process data as soon as it becomes available.
Additionally, Snowflake and Kafka address the growing demand for separating storage and compute. Once streaming data is loaded into the Snowflake Data Cloud, users can take the unstructured data from Snowflake and use an ELT tool such as Matillion to convert it to structured data and conduct advanced analytics with machine learning.
Test Drive the Snowflake Data Cloud with Kafka
The Snowflake platform makes event streaming simple. Spin up a Snowflake free trial to:
Explore the Snowflake UI and sample data sets
Process semi-structured data with full JSON support
Instantly scale compute resources up and down to handle unexpected workloads and unique concurrency needs
Set up and test-run data pipelines and connect to leading BI tools
Experiment with programmatic access
To test drive Snowflake, sign up for a free trial.