The design framework for managing and integrating data across cloud platforms, ensuring scalability and efficiency.

The capability to process data as it arrives, allowing for immediate analysis and action.

A distributed streaming platform used for building real-time data pipelines and streaming applications.

A serverless computing service that runs code in response to events, enabling scalable data processing.

The ability to handle growth in data volume without compromising performance or efficiency.

A series of data processing steps that move data from source to destination, often involving transformation and storage.

A cloud computing model that allows developers to build and run applications without managing servers.

Software applications used to oversee the performance and health of data pipelines.

Systems that record events and transactions within data pipelines for troubleshooting and auditing.

Issues that arise when a data pipeline struggles to accommodate increased data loads.

A point in a data pipeline where performance is limited, causing delays in data processing.

A software architecture pattern that reacts to events, often used in real-time data processing.

Continuous flow of data that is processed in real-time, often used in big data applications.

Companies that offer cloud computing services, such as AWS, Google Cloud, and Azure.

The accuracy and reliability of data, crucial for effective decision-making.

Categories in Kafka where messages are published, allowing for organized data streaming.

Applications that send data to Kafka topics, initiating the data flow.

Applications that read data from Kafka topics for processing or analysis.

Techniques used to improve the efficiency and speed of data processing in pipelines.

Strategies for controlling and optimizing expenses associated with cloud resources.

A testing methodology that evaluates the entire data pipeline from start to finish.

Processes that allow for continuous improvement based on performance data and user input.

Methods for recording and presenting information about data architectures and processes.

Testing that ensures different components of the data pipeline work together as expected.

A detailed plan outlining the objectives and methods for a data pipeline project.

Visual representations of data architectures, illustrating components and data flow.

Strategies implemented to enhance the capacity of data pipelines to handle increased loads.