Data in Motion
The term “data in motion” refers to data that is actively being processed or transformed or moving from a source device, sensor, storage device, or data communications node to a target location. The target location can be an intermediate node or a destination, where it can become “data at rest”.
Data in Use
When data is created, edited, transformed, or processed, it is considered “in use”. Common data creation sources are transaction systems such as point-of-sale (POS) terminals, banking, or stock market transactions. IoT applications create data based on events such as car telemetry, motion detection, or voltage fluctuations. Event-driven data creation that generates prolific amounts of data is not limited to business applications. Streamed data from gaming, social media engagement, and sports events generate masses of data that are consumed by marketing systems to drive multi-channel advertising campaigns.
Data Processing
Cloud providers often consider data to be in motion when it is being actively processed by an application. Examples include data from an IoT sensor for updating a weather map, data sent in an email, or data being replicated to a remote location to provide data protection. For data to be processed, it must be moved from non-volatile secondary storage to volatile RAM or CPU cache. When data is altered by an application, it needs to be written out to secondary storage for it to persist across server shutdowns and unplanned power failures. Data stored on non-volatile secondary storage such as disk and SSD is termed “data at rest”.
Three States of Data?
The three states of data are:
- Data at Rest – on secondary storage media.
- Data in Motion – between media, applications, or locations.
- Data in Use – being used or created by applications.
Data Encryption and Data in Motion
When data is in motion, it is susceptible to hacking by anyone who can intercept communication between endpoints. If this data is readable by humans, it can be stolen or used to gain access to secure servers. For this reason, data needs to be obfuscated using a crypto key and encryption techniques such as AES and Rivest-Shamir-Adleman (RSA). AES encryption is used for in-transit and at-rest encryption. RSA encryption is commonly used to protect data being transmitted between endpoints of a wide area network (WAN). Data blocks can be encrypted using different key lengths. Longer keys take more time to break. The sensitivity of the data in motion usually dictates which key length is used. Examples of available keys include AES-128 bit, AES-192 bit, and AES-256 bit keys. Actian products can encrypt data at rest or in motion using a 256-bit AES key.
Ways to Protect Data in Motion
Data protection is best achieved if implemented proactively. Encryption is not the only way organizations have to protect data. Firewalls can be used effectively to stop outsiders from entering private networks. Strong passwords and access policies can be used to protect sensitive data from internal and external threats. Data should always be encrypted when traversing any external or internal networks.
Transient Data in a Data Warehouse
Databases that underpin data warehouses are managed by a relational database management system (RDBMS). To ensure each database transaction is completed in a timely manner, a database needs four properties – atomicity, consistency, isolation, and durability. When databases possess these properties, they are said to be ACID-compliant.
To deliver ACID compliance, an RDBMS must be able to protect transactional data to maintain data integrity. To do this, every user data creation, update, or deletion is recorded in a transaction log which is always written to secondary storage. Transient data in RAM maintains consistency by only allowing one user or application program to make changes to it by locking it until the transaction is either committed or rolled back. When a database server suffers from an unplanned shutdown, it performs a rollback of the transaction log to the last checkpoint when it is restarted. It then rolls forward through the transaction log applying any data changes since the checkpoint. An orderly shutdown performs a transaction log checkpoint to minimize startup times.
Actian and the Data Intelligence Platform
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
FAQ
Data in motion refers to information that is actively moving between systems, applications, devices, or services in real time. It includes event streams, API calls, logs, IoT data, and any data transmitted across networks.
Data in motion is continuously transmitted and processed as events occur, while data at rest is stored in a database, file system, or data warehouse. Data in motion prioritizes low latency, whereas data at rest prioritizes durability and long-term storage.
Common technologies include Apache Kafka, Amazon Kinesis, Google Pub/Sub, Apache Pulsar, MQTT, WebSockets, streaming ETL frameworks, and change data capture (CDC) tools that capture and forward database updates in real time.
It enables real-time analytics, event-driven applications, fraud detection, operational monitoring, personalization engines, and continuous data pipelines that require instant processing and fast decision-making.
Use cases include IoT monitoring, real-time dashboards, supply chain tracking, cybersecurity alerting, user activity analytics, financial transaction processing, and feeding real-time data into machine learning and AI pipelines.
Challenges include ensuring low-latency delivery, maintaining data quality, guaranteeing message ordering, handling high-throughput streams, preventing data loss, managing schema evolution, and integrating with both operational systems and analytics platforms.
