Decision Intelligence Academy

Edge computing involves processing data at the edge of a network rather than in a centralized data center. This approach reduces bandwidth usage by filtering, aggregating, or analyzing data locally. By bringing computation to the edge, businesses can gain real-time insights and improve the overall performance of their applications.

Edge computing offers significant benefits in terms of data security and privacy. By processing data closer to its source, businesses can reduce the risk of data breaches and ensure compliance with data privacy regulations. Less data needs to be transmitted over the internet, minimizing exposure to potential security threats.

In addition to security benefits, edge computing can lead to cost savings. By processing data at the edge, organizations can reduce the amount of data transmitted to centralized data centers, lowering bandwidth costs and storage requirements. This targeted approach allows businesses to focus on valuable data while discarding unnecessary information.

A Directed Acyclic Graph (DAG) is a graphical data structure used to represent a series of tasks or activities. Each node in the graph represents a task, and the directed edges between nodes indicate the order in which the tasks must be executed. A DAG ensures that tasks are executed in a specific order without creating any cycles or loops.

DAGs provide a visual representation of complex data processing workflows. In the context of batch processing, a DAG can be used to model the steps involved in generating sales reports. For instance, a DAG can depict the loading of raw sales data, currency conversion, data summarization, and report generation. This visual representation helps in understanding and optimizing the overall workflow.

Apache Kafka® is a distributed, fault-tolerant platform that enables real-time data processing. It uses a publish-subscribe messaging model, where producers (data sources) publish data to topics, and consumers (data sinks) subscribe to these topics to receive the data stream. Kafka is known for its excellent performance, low latency, fault tolerance, and high throughput which make it a popular choice for a wide range of applications.

Apache Kafka is a distributed streaming platform designed to handle high-volume, real-time data streams. It enables developers to build scalable and reliable applications that process and respond to data as it flows.

Kafka stores data in a persistent, fault-tolerant manner, making it suitable for various use cases, including:

• Message Broker: Replacing traditional message brokers for high-throughput, low-latency messaging.
• Log Aggregation: Centralizing and storing logs from various sources.
• Stream Processing: Processing data streams in real-time using frameworks like Apache Flink, Apache Spark Streaming, or Cogility’s Cogynt.

Apache Kafka often serves as the backbone of real-time data pipelines, where data is ingested, processed, and stored in a data warehouse or data lake for further analysis. This architectural pattern is sometimes referred to as the Kappa Architecture.

What Companies Use Apache Kafka?

Apache Kafka is used by organizations in every industry - from software, financial services, healthcare, to government and transportation. Kafka is used by thousands of companies including over 80% of the Fortune 100. Among these are Box, Barclays, Target, Cloudflare, Intuit, and more.

Apache Flink® is a powerful, open-source platform designed for both batch and streaming data processing. It provides a unified programming model for handling both types of data, allowing developers to build scalable and fault-tolerant applications. Flink's core capabilities include:

• Stream Processing: Processing continuous, unbounded streams of data in real-time.
• Batch Processing: Processing large, static datasets efficiently.
• State Management: Managing stateful computations, such as sessionization, windowing, and timeouts.
• Fault Tolerance: Ensuring data consistency and reliability in case of failures.

Flink originated from the Stratosphere research project. A collaboration of the Technical University of Berlin, the Humboldt University of Berlin, and the Hasso Plattner Institute) and became an Apache Incubator project.

Flink is a versatile framework for building data pipelines that can handle both batch and streaming data. It automates low-level tasks such as task scheduling and resource allocation, allowing developers to focus on the core logic of their data processing applications. This simplifies the development of complex data pipelines and enables efficient processing of large datasets.

Flink excels at stream processing, where it efficiently processes data from message queues like Apache Kafka or Apache Pulsar. By handling data in a sequential manner, Flink can identify patterns and trends in real-time, enabling timely insights and decision-making.

Who Companies Use Apache Flink?

Flink is used by numerous companies across many industries. A few of the well-established companies that leverage Flink include Amazon, CapitalOne, Comcast, eBay, ING, Netflix, Uber and Yelp.