What Is Data Movement?

3 min. read

Data movement is the transfer of data between cloud or on-premise data stores. This might involve ingesting, replicating, or transforming data as it travels through different databases and applications.

Data Movement Explained

Data movement (sometimes referred to as data flow) is the process of transferring data from one location or system to another – such as between storage locations, databases, servers, or network locations. Data movement plays a part in various information management processes such as data integration, synchronization, backup, migration, and data warehousing.

While simply copying data from A to B tends to be simple, data movement gets complicated when you need to manage volume, velocity, and variety: handling large amounts of data (volume), managing the speed at which data is produced and processed (velocity), and coping with diverse types of data (variety). Modern data movement solutions will often incorporate features such as data compression, data validation, scheduling, and error handling to improve efficiency and reliability.

As organizations shift their infrastructure to public cloud providers, data movement is becoming a more central consideration. While on-premise environments were typically monolithic and designed around ingesting data into an enterprise data warehouse, cloud environments are highly dispersed and dynamic.

The cloud's elasticity allows businesses to easily spin up new services and scale resources as needed, creating a fluid data landscape where datasets are frequently updated, transformed, or shifted between services. This fluidity presents its own challenges as organizations must ensure data consistency, integrity, and security across multiple services and platforms.

Data Movement and Cloud Data Security

In the context of security, data movement can become an issue when organizations lose visibility and control over sensitive data. Customer records or privileged business information can be duplicated and moved between services, databases, and environments, often leading to the same record existing in multiple data stores and processed by different applications, sometimes in more than one cloud.

This continuous data movement introduces complexities when it comes to protecting sensitive data – particularly in terms of complying with data residency and sovereignty, maintaining segregation between environments, and tracking potential security incidents. For example, when data is regularly moved between databases, the security team might miss an incident wherein the data is moved into unencrypted or publicly-accessible storage.

Cloud data security tools help you map the movement of sensitive data, and identify flows that should trigger immediate responses from security teams. They can also help you prioritize incidents that pose a more serious risk, primarily those involving sensitive data flowing into unauthorized or unmonitored data stores.

5 Types of Data Movement, With Examples

Data Replication

Copying the same datasets and storing them in different locations. This is typically done for backup and recovery scenarios, to ensure data availability, and to minimize latency in data access across geographically distributed systems. For example: an e-commerce company replicates their inventory database across several regional servers to ensure rapid access for users worldwide.

Data Migration

Moving data from one system or storage location to another, often during system upgrades or when moving data from on-premise servers to cloud environments. Migrations can be complex due to the large volumes of data involved and the need to ensure data integrity during the move. For example: a business migrates its customer data from a legacy on-premise system to a cloud database as a service such as Snowflake.

Data Integration

Combining data from different sources into a unified view. Typically this is done when you are trying to get a comprehensive view of business operations from disparate business systems, or in order to cleanse and normalize data from a single source. For example: A healthcare provider integrates patient data from multiple systems (scheduling, medical records, billing) to provide a comprehensive patient profile.

Data Streaming

Real-time data movement, where data is continuously generated and processed as a stream – often for monitoring, real-time analytics, or operational use cases. Data streaming services move event-based data that is generated from sources such as applications and sensors to data storage platforms or applications, where it can be analyzed and acted upon immediately. For example: A ride-sharing service like Uber streams location data from drivers' phones to their servers for real-time matching with passengers.

Data Ingestion

Obtaining and importing data into a database (or distributed storage system), either for immediate use or long-term storage. Ingestion can be batch-based or streaming. The process involves loading data from various sources and may include transformation and cleaning of the data to fit the destination storage schema. For example: A financial services firm ingests stock market data from various exchanges into a data lake for further analysis and machine learning.

The Fragmented Landscape of Data Movement Tools

The best evidence for the central role data movement plays in the modern data stack is the highly diverse and complicated tooling landscape that has emerged to support it. Below you will find a small sample of the vendors operating in this space — each category could literally be expanded to dozens of specialized tools, cloud-native solutions, and open source frameworks.

Data Movement Category Vendors Description
Data Replication AWS DMS (Amazon) Homogeneous and heterogeneous database migrations with minimal downtime, used for large-scale migrations and database consolidation.
GoldenGate (Oracle) Real-time data replication and transformation solution, used for high-volume and mission-critical environments.  
IBM InfoSphere Supports high-volume, high-speed data movement and transformation, including complex transformations and metadata management.  
Data Migration AWS Snowball (Amazon) Physical data transport solution that uses secure appliances to transfer large amounts of data into and out of AWS, useful for limited network bandwidth scenarios.
DMS (Amazon) Supports both homogeneous and heterogeneous migrations and continuous data replication with high availability.  
Azure Migration (Microsoft) Comprehensive suite of tools and resources to simplify, guide, and expedite the migration process.  
BigQuery Data Transfer (Google Cloud) Automates data movement from SaaS applications to Google BigQuery on a scheduled, managed basis.  
Informatica Offers an AI-powered data integration platform to access, integrate, and deliver trusted and timely data.  
Talend Provides a suite of data integration and integrity apps to integrate, clean, manage, and transform data.  
Data Integration Fivetran Automated data pipeline solutions to load and transform business data in a cloud warehouse.
Azure Data Factory (Microsoft) Data integration service that allows creation, scheduling, and management of data-driven workflows for ingesting data from disparate sources.  
AWS Glue (Amazon) Fully managed ETL service, useful for preparing and loading data for analytics.  
Apache Nifi (Open Source) Supports data routing, transformation, and system mediation logic, good for real-time or batch data pipelines.  
DataStage (IBM) Provides extensive data transformation capabilities for structured and unstructured data.  
Google Cloud Data Fusion Fully managed, cloud-native, data integration service to help users efficiently build and manage ETL/ELT data pipelines.  
Data Streaming Apache Kafka (Open Source) Distributed event streaming platform, good for high-volume, real-time data streams.
Amazon Kinesis (Amazon) Collects, processes, and analyzes real-time, streaming data, useful for timely insights and reactions.  
Google Cloud Pub/Sub Messaging and ingestion for event-driven systems and streaming analytics.  
Azure Stream Analytics (Microsoft) Real-time analytics on fast moving streams of data from applications and devices.  
Confluent Provides a fully managed Kafka service and stream processing, useful for event-driven applications.  
Data Ingestion Fluentd (Open Source) Open source data collector for unified logging layer, allowing you to unify data collection and consumption.
Logstash (Elastic) Server-side data processing pipeline that ingests data from multiple sources, transforms it, and then sends it to a "stash" like Elasticsearch.  
Kinesis Firehose (AWS) Fully managed service for delivering real-time streaming data to destinations such as Amazon S3, Redshift, Elasticsearch, and more.  
Apache Flink (open source) Open source stream processing framework for high-performance, reliable, and accurate real-time applications.  

Google Cloud Dataflow

Fully managed service for stream and batch processing with equal reliability and expressiveness.  

Data Movements FAQs

Data movement refers to the transfer of data between cloud or on-premise data stores. This might involve ingesting, replicating, or transforming data as it travels through different databases, servers, applications, or cloud platforms.
Data migration or the process of transferring data from one system to another. A good example is when a business migrates its customer data from a legacy on-premise system to a cloud database as a service such as AWS.
Types of data movement include data replication, data migration, data integration, data streaming, data archiving, and data ingestion. Each type of data movement serves different purposes and requires appropriate security measures.
Data flow in the cloud refers to the movement and transformation of data between various components, services, and storage locations within a cloud architecture. It is a crucial aspect of managing and processing data, ensuring that information reaches the right destination, in the correct format, and at the appropriate time. Efficient data flow management involves designing effective pipelines, leveraging data integration and synchronization tools, and ensuring data security and compliance throughout the process.
A data flow diagram (DFD) is a graphical representation that maps the flow of data within a system, such as a cloud architecture. It illustrates how data moves between processes, data stores, and external entities, making it easier to understand and analyze complex data workflows. DFDs help identify bottlenecks, inefficiencies, and potential security vulnerabilities, enabling organizations to optimize and secure their data management processes.
Data integration in the cloud is the process of combining data from multiple sources, such as databases, applications, and services, into a unified and coherent dataset. It enables organizations to gain insights, make informed decisions, and streamline operations by providing a holistic view of their data. Data integration techniques include extract, transform, load (ETL) processes, data replication, and real-time data streaming. Cloud-native data integration tools and services facilitate seamless data integration across various cloud platforms and services.
Data synchronization in the cloud is the process of maintaining data consistency and coherence across multiple storage locations, systems, and services. It ensures that the latest data updates, additions, or deletions are reflected across all copies of the data, providing a unified view and preventing data conflicts. Synchronization can be performed in real-time or on a scheduled basis, depending on the organization's requirements. Cloud-based data synchronization tools and platforms help manage data synchronization between on-premises and cloud environments, as well as across multiple cloud providers.
Data warehousing in the cloud is a large-scale, centralized data storage solution that consolidates data from various sources, such as transactional databases, log files, and external systems. It enables organizations to store, manage, and analyze vast amounts of structured and semi-structured data efficiently. Cloud data warehouses, such as Amazon Redshift and Google BigQuery, offer scalability, flexibility, and cost-effectiveness compared to traditional on-premises data warehouses. They support advanced analytics, business intelligence, and machine learning use cases, facilitating data-driven decision-making.
Data compression in the cloud is the process of reducing the size of data files to optimize storage, transmission, and processing. Compression algorithms can be lossless, preserving the original data's integrity, or lossy, sacrificing some data quality for higher compression ratios. Data compression techniques are especially valuable in cloud environments, where large data volumes and distributed architectures can lead to increased storage costs and network latency. Implementing data compression helps organizations reduce storage requirements, improve data transfer speeds, and lower overall cloud infrastructure costs.
Data validation in the cloud involves verifying the accuracy, consistency, and quality of data before processing, storage, or transmission. Validation techniques include data type checking, format validation, and range checking, ensuring that data complies with predefined rules and constraints. Data validation is crucial for maintaining data integrity, preventing errors, and ensuring the reliability of analytics and decision-making processes. Cloud-based data validation tools and services can automate validation tasks, streamline data workflows, and ensure data quality across distributed cloud environments.
Scheduling in data movement involves the automated execution of data transfer, integration, or synchronization tasks at predetermined times or intervals within cloud environments. Scheduling helps organizations automate repetitive data management tasks, optimize resource usage, and ensure timely data updates across various services and storage locations. Cloud-based scheduling tools and services, such as AWS Data Pipeline or Azure Data Factory, enable organizations to create, manage, and monitor scheduled data movement tasks, improving efficiency and data consistency.
Error handling in data movement involves detecting, managing, and resolving errors that occur during the transfer, integration, or synchronization of data in the cloud. Errors can result from issues such as data corruption, network disruptions, or format inconsistencies. Effective error handling strategies include implementing data validation checks, monitoring data movement processes, and creating fallback mechanisms to recover from failures. Cloud-based data movement tools and services often provide built-in error handling features, enabling organizations to maintain data integrity and minimize the impact of data movement errors.
Data residency refers to the physical location where data is stored, processed, and managed within cloud infrastructures. Organizations must often comply with specific data residency requirements dictated by industry regulations, privacy laws, or contractual obligations. These requirements can restrict the geographical regions where data is allowed to be stored or processed. Cloud service providers offer regional data centers and storage options to help organizations meet data residency requirements, ensuring compliance with applicable laws and minimizing potential legal and financial risks.
Data sovereignty pertains to the legal and regulatory framework governing data ownership, privacy, and access control based on the country or jurisdiction where the data resides. In the context of cloud computing, data sovereignty requirements can impact how organizations store and manage data across international borders, as different countries enforce varying data protection and privacy laws. To adhere to data sovereignty regulations, organizations must carefully select cloud providers and data center locations, ensuring that data storage and processing activities comply with the relevant legal requirements.
Data replication in the cloud is the process of creating and maintaining multiple copies of data across different storage locations, systems, or services. Replication ensures data availability, minimizes latency for geographically distributed users, and provides redundancy for backup and recovery purposes. Cloud providers offer various data replication services and options, such as synchronous or asynchronous replication, to meet organizations' specific needs in terms of performance, consistency, and fault tolerance.
Data streaming in the cloud involves the real-time transfer and processing of data generated continuously by sources such as applications, sensors, or user interactions. Streaming enables organizations to analyze and act on data as it is produced, facilitating real-time monitoring, analytics, and decision-making. Cloud-based data streaming services, such as Amazon Kinesis or Google Cloud Pub/Sub, provide scalable and fault-tolerant solutions for ingesting, processing, and storing streaming data, catering to diverse use cases and performance requirements.
Data ingestion in the cloud is the process of obtaining and importing data from various sources into cloud-based storage systems or databases for immediate use or long-term storage. Ingestion can occur in batch or streaming modes, depending on the data type and use case. Data ingestion involves loading data from multiple sources, potentially transforming and cleaning it to fit the destination storage schema. Cloud data ingestion services, such as Azure Data Factory or AWS Glue, simplify and automate data ingestion tasks, supporting diverse data formats and integration scenarios.
Security incidents in data movement refer to events that compromise the confidentiality, integrity, or availability of data as it is transferred, integrated, or synchronized within cloud environments. Such incidents can result from unauthorized access, data leaks, misconfigurations, or cyberattacks targeting data movement processes. To mitigate security incidents in data movement, organizations must implement robust security measures, including data encryption, access controls, and continuous monitoring, ensuring data protection and compliance throughout the data lifecycle.