What Is Data Movement: Purpose, Tools, & Examples

Organizations accumulate information from multiple sources, such as sensory devices, web analytics tools, and social media platforms. These datasets are significant for crucial initiatives, such as obtaining customer insights for marketing campaigns, financial analysis, and operational efficiency. To perform such advanced analysis, multiple departments within the organization must work together with seamless data movement between their systems.  

Effective data workflows ensure all your employees can access the latest information and get a unified view of all operations. This helps eliminate discrepancies and inconsistencies during the decision-making process. This article will delve deeper into practical examples and tools available to simplify and optimize data movement within your organization.

What Is Data Movement?

Data movement refers to transferring information between different locations within your system or organization. This can involve moving data from on-premises storage to cloud environments, between databases, or from data lakes to analytics platforms. 

Data movement is crucial during data warehousing, system upgrades, data synchronization, and integration. It allows you to populate warehouses, migrate data to new environments during upgrades, sync the latest information between systems, and consolidate data for a consistent view. Various tools and techniques, such as managed file transfer services, cloud-based data integration platforms, and pipelines, allow for secure and efficient data movement.

Purpose of Data Movement

Data movement streamlines most of your organization’s processes and allows you to implement an effective data management strategy. Below are several reasons why you should have a robust data movement infrastructure.

Connecting Data from Disparate Sources

Data often resides in various databases, applications, and cloud storage locations. Data movement helps you to break down these silos and bring the scattered information together for a more comprehensive understanding. This gives you deeper insights into customer behavior, operational trends, and market dynamics. Productive data movement also minimizes processing overhead and resource requirements for downstream analytics. 

Moving Data for Processing and Insights

Raw data can have limitations. With data movement, you can facilitate information transfer to dedicated systems for further processing and analyses, such as sales forecasting and marketing campaign optimization. Having readily available data pipelines for processing gives you a significant advantage over your competitors. 

To Handle Growing Data Volumes

Strategic data movement can help you distribute data across various storage tiers based on access frequency and necessity. For example, you can move less frequently accessed data to cost-effective cloud storage solutions and keep often-used data readily available on high-performance systems. This ensures optimal storage utilization and quick access to critical information.    

Keeping Data Consistent across Different Systems

Data inconsistencies across various systems can lead to inaccurate reporting and biased outcomes. Data movement plays a vital role in maintaining your data’s integrity. Techniques like data replication, ELT/ETL processes, and data synchronization help ensure consistent data versions across your organization. This eliminates discrepancies and fosters trust in the data used for analytics and reporting.

Data Movement Technologies

With the purpose of data movement established, this section delves into the technologies that make it all happen. These tools ensure efficient, secure, and reliable transfer of information across your data infrastructure.

ETL

ETL, which stands for extract, transform, load, is a data movement technology better suited for structured data with a well-defined schema. It allows you to extract data from various sources, cleanse and transform it into a usable format, and load it into your preferred destination. 

ETL offers advantages like better data quality control since transformations happen before loading. However, due to the additional processing step, it can be slower and might not be ideal for high-volume or unstructured data. 

ELT

ELT (extract, load, and transform) allows you to load the raw data directly into the target system, like a data lake, and perform the transformations within that environment. It offers a faster alternative to ETL by skipping the initial transformation steps.

ELT also provides more flexibility as you can easily accommodate schema changes or add new data sources without re-processing the entire dataset. However, this can raise concerns about data quality, consistency, and accuracy.  

CDC

CDC, or Change Data Capture, is a technique used in data integration to identify and track changes made to data in a source system. It focuses on capturing only the changes made to data since the last transfer. There are two main approaches to achieve this: log-based and trigger-based CDC.

CDC minimizes data movement, improving efficiency and reducing bandwidth strain, especially for frequently updated data. It is ideal for synchronizing data warehouses or other systems with real-time changes.

Replication

Data replication refers to creating and maintaining multiple copies of the same data in different locations. A replicated copy can keep applications running and minimize downtime if your primary source system becomes unavailable due to hardware failure or maintenance. 

With data replication, your employees can access the data closest to them, reducing network latency and improving performance for geographically dispersed users. This method is efficient for smaller datasets but can become resource-intensive with large volumes.

Streaming

Data streaming refers to the continuous flow of data generated in real-time by various sources. This is ideal for high-velocity data like sensor feeds, where you need to identify trends, patterns, or anomalies in the data as it arrives.

Streaming data allows you to react immediately to events or changes in data, leading to quicker problem identification and damage prevention. However, managing and scaling streaming infrastructure can be complex, requiring specialized skills and tools.

Data Movement Vs. Data Migration Vs. Data Synchronization

Data movement, migration, and synchronization are all essential processes for managing data within your organization. While they all involve transferring data, their purposes and functionalities differ significantly. The table below explores the key differences between them: 

Examples of Data Movement

Data movement plays a crucial role in various organizations across industries. Below are some examples demonstrating its importance:

Cloud Migration

Companies are increasingly migrating their data storage and applications to the cloud. Data movement facilitates this transition by helping you securely transfer vast information from on-premises servers to cloud-based platforms. Cloud migration enables scalability, improved accessibility, and potential cost savings.

Moving Customer Data of an E-commerce Business

Moving customer data for an e-commerce business involves transferring sensitive information such as customer profiles, purchase history, and payment details. With data movement, you can transfer this information and implement security measures such as encryption and compliance with data privacy and protection regulations.

Moving Sales & Marketing Data

Sales and marketing teams often use data from different sources, like CRM systems and marketing automation tools. Data movement helps consolidate this information into a central location, enabling your teams to gain a holistic view of campaign performance and sales pipelines. This results in better collaboration and data-driven decision-making.

Healthcare Data

Hospitals generate massive amounts of patient data, including electronic medical records and diagnostic test results. Secure data movement allows healthcare providers to transfer this information into centralized repositories while maintaining data integrity and accessibility. Such data infrastructure makes providing high-quality patient care easier and increases operational efficiency. 

Data Movement Tools to Ease up the Process

Many data movement tools have entered the market, offering various functionalities to help you simplify and automate the process. You should choose the one that fits your business needs. 

Airbyte

Airbyte is a data integration and replication platform that helps you quickly build and manage data pipelines. Its no-code, user-friendly interface allows you to perform data movement without any technical expertise. 

Airbyte supports an ELT approach that reduces latency when extracting and loading high-volume, high-velocity data. Here are some features of Airbyte for you to explore:

• Extensive Library of Connectors: Airbyte automates data movement by offering you a library of over 350 pre-built connectors. These connectors help you connect multiple data sources to a destination system. Using Airbyte’s low-code Connector Development Kit (CDK), you can also build custom connectors within minutes.‍
• Change Data Capture: You can leverage Airbyte’s Change Data Capture (CDC) functionality to capture the incremental changes occurring at the source system. This feature helps you efficiently utilize resources while dealing with constantly evolving large datasets.‍
• Schema Change Management: The schema change management feature allows you to configure settings to detect and propagate schema changes at the source. Based on these settings, Airbyte automatically syncs or ignores those changes. 

To learn more about Airbyte’s features, you can refer to the official documentation and other educational resources, blogs, and tutorials to familiarize yourself with the platform. 

Skyvia

Skyvia is a versatile data movement tool that can help you streamline your workflows in various scenarios, including data warehousing, CRM or ERP integration. It is a no-code platform that allows you to handle multiple data integration needs, including ETL, ELT, reverse ETL, and one-way and bidirectional data sync. 

Skyvia empowers you to handle data of any complexity, regardless of flow and transformation needs. However, it only supports over 180 sources and can have a bit of a learning curve.   

Matillion

Matllion is a cloud-based data integration and transformation tool that can help your organization with data movement processes. It supports various data integration requirements like ETL, ELT, reverse ETL, and data replication and is best suited for cloud data warehousing and analytics.

While Matillion provides an intuitive graphical interface and optimization for high-volume data processing, it offers only around 150 data source connectors. This limits the scope for easy integration with diverse platforms. This tool can also be resource-intensive and can incur higher costs.

Data Movement Strategies

Effective data movement strategies are essential for effortlessly transferring data within your organization. Here are some key considerations to optimize your data movement approach.

Batch Processing

Batch processing is a data movement strategy where you can accumulate, process, and transfer data in large, individual chunks at scheduled intervals. It is efficient and helps minimize network strain by handling data in bulk. 

Batch processing is advantageous for tasks requiring significant computational resources, such as end-of-day financial transactions and bulk data imports. By executing operations in batches, you can optimize performance and resource utilization, reducing the load on primary systems during peak hours. Apache Spark and AWS Batch are tools you can leverage to execute batch processing.

Real-Time Streaming

In contrast to batch processing, real-time streaming continuously transmits data sequentially as it is generated at the source. This method offers minimal latency and high throughput, making it ideal for real-time decision-making scenarios such as Internet banking, stock market platforms, and online gaming. 

However, real-time streaming can be resource-intensive and requires robust infrastructure to handle the constant data flow. Some popular tools for real-time data streaming include Apache Kafka, Apache Flink, and Amazon Kinesis.

Incremental Transfer

This data movement strategy focuses on transferring only the new or modified data since the last successful transfer. Incremental transfers are efficient for frequently changing high-volume datasets as they minimize redundant transmissions and bandwidth consumption. Implementing incremental data transfer after database replication, backup, and synchronization tasks ensures that only updated information gets reflected at the destination. 

By transferring only the modified data, your organization can maintain up-to-date datasets with minimal overhead. Airbyte, Talend, IBM Informix, and Hevo are well-known tools for incremental data transfer.

Full Data Transfer

Full data transfer involves transferring the entire dataset from the source to the destination system at a single point in time. You can use it for initial data migrations, replicating static datasets, and complete backups. 

While full data transfer ensures accurate data movement, it can be resource-intensive and time-consuming, especially for large datasets. Full data transfer is usually utilized when data integrity and completeness are top priorities. Some data transfer tools that can help you are IRI NextForm, Matillion, and Stitch. 

Challenges in Data Movement

While data movement is seemingly straightforward, it can present several hurdles that can significantly impact the progress, efficiency, and success of your data initiatives.

Here are some of the most common challenges you can encounter during data movement:

• Data Compatibility: Your data can reside in various formats and structures, making it difficult to integrate and move data from source to destination.
• ‍Data Loss or Corruption: Data errors or disruptions during transfer can lead to data loss or corruption, compromising the integrity of your data at its destination.
• ‍Lack of Experienced Resources: Performing data movement without proper expertise can lead to errors, causing delays and impacting the overall efficiency.‍
• Lack of Standardization: Without standardized processes and tools for data movement, your overall data management strategy can be inconsistent and inefficient.‍
• Data Security and Compliance: Ensuring data security and adhering to regulations can be complex, with multiple touchpoints representing opportunities for unauthorized access or data breaches.

How to Use Airbyte to Move Data Efficiently?

With Airbyte, you can seamlessly transfer data between various sources and destinations. Here's a step-by-step guide explaining how to move your data using Airbyte. 

Step 1: Set up Your Data Source

• Airbyte offers two types of accounts: Airbyte Cloud and a self-managed version. Once you set it up, log into your account, and you will see the screen below.   

• Select the Sources tab on the left side of the screen, enter your data source (e.g., Postgres) in the Search field, and select the corresponding connector as shown below.

• Enter all the specific configuration details required for your chosen data source, including credentials such as Host, Database Name, Username, and Password. 

• After filling in all the details, scroll down and select Set up Source button. Airbyte will run a quick check to verify the connection. 

Step 2: Set up Your Destination

• To set up your destination, click on the Destination tab on the left side of the Airbyte homepage and enter your preferred data warehouse or cloud storage solution. Select the corresponding connector (e.g., Snowflake) as shown below.

• Enter all the credentials, such as Host, Warehouse, Database, and Default Schema. Scroll down and click on the Set up Destination button. Airbyte will run a verification check to ensure a successful connection.

Step 3: Set up Your Connection

• Click on the Connections.
• You will see a screen indicating four steps to complete the connection. The first and second steps involve selecting your previously configured source and destination.

• In the third and fourth steps, you will need to configure other settings like replication frequency and sync mode, explaining how your data should sync. It also includes entering your destination namespace and configuring how Airbyte should handle schema changes.

• Once you fill in all the required details, click on Set up Connection. With this, you have successfully built your Airbyte pipeline for data movement. 

Conclusion

Data movement is fundamental to your data strategy. It helps you easily transfer the required data to the location where it is needed. This seamless data movement within the organization enables you to extract valuable insights, improve decision-making, and gain a competitive edge. 

The article explores various data movement strategies, tools, and challenges that can help your organization establish efficient and secure data pipelines to leverage your information assets. By gaining a holistic view of your data, you can increase operational efficiency and make smarter business decisions crucial to sustainable growth. 

FAQs

What is the main goal of the data movement?

The main goal of data movement is to ensure the right data is available in the right places at the right time and in the appropriate format for various data management and analysis purposes.

What is data movement vs data integration?

Data movement is the transfer of data from one location to another, while data integration involves collecting data from disparate sources and loading it into a centralized location.