Data in Motion
Data in motion refers to data that is actively being transmitted or processed in a system, often through pipelines that move data between different stages of processing.
Data Pipelines
Data pipelines are a series of connected processing elements that move data between different stages of processing. A typical data pipeline involves several stages of processing, such as ingestion, transformation, enrichment, and analysis, with data moving from one stage to the next as it is processed.
Pipelines can be used to move data between different applications or systems, or to transform and enrich data as it moves through a system. They are often used in real-time or near-real-time applications, such as stream processing or real-time analytics.
Stream Processing
Stream processing is a type of data processing that involves processing data as it is generated or ingested, rather than processing it after it has been stored. Stream processing can be used to analyze or filter data in real-time, and is often used in applications such as fraud detection, sensor data processing, and real-time analytics.
Popular stream processing platforms include Apache Kafka, Apache Flink, and Apache Storm.
Batch Processing
Batch processing involves processing large volumes of data in a batch or offline mode, often in a scheduled or periodic manner. Batch processing can be used to perform complex data transformations, such as ETL (extract, transform, load) operations, and is often used in applications such as data warehousing and business intelligence.
Popular batch processing platforms include Apache Spark and Apache Hadoop.
Data Integration
Data integration involves combining data from multiple sources and making it available for analysis or processing. This can involve moving data between different systems or applications, or transforming and merging data to create a unified view of the data.
Popular data integration platforms include Apache NiFi, Talend, and Microsoft Azure Data Factory.
Data Governance
Data governance involves managing the availability, usability, integrity, and security of data used in an organization. Data governance can be applied to data in motion as well as data at rest, and involves establishing policies and procedures for managing data throughout its lifecycle.
Popular data governance platforms include Collibra and Informatica.
Summary
Data in motion is a critical component of modern data architectures, and involves moving and processing data in real-time or near-real-time. Data pipelines, stream processing, batch processing, data integration, and data governance are all important aspects of managing and analyzing data in motion.