Thursday, April 18, 2024

Data Migration vs. ETL: What’s the Difference?

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When it comes to moving large volumes of data between storage locations, there are two main approaches: data migration and ETL. This article explains the differences and similarities of both methods, how they work, and the best tools on the market for each.

Data migration involves moving data from one system to another, often to upgrade or replace a legacy system. ETL—which stands for Extract, Transform, and Load—is the process of pulling data from one or more sources, transforming it into a suitable format, and loading it into the target location. The key difference is scale. Data migration is typically used to transfer whole databases while ETL is often used for smaller datasets or parts of a database. Organizations are more likely to use data migration when replacing an outdated system, moving to the cloud, or merging with another company because it allows for better business continuity by moving all of the company’s data wholesale.

How Does Data Migration Work?

At a high level, data migration is simply the process of moving a database from one storage system to another. There are several approaches, including transfering the data directly or exporting it externally and then importing it to the new system. The goal of the process is to ensure all data is retained during the move and that it remains consistent with the new system’s data format.

One of the biggest challenges of data migration comes when moving it from an outdated system to a new system, which can increase the likelihood of data loss or corruption. It’s important to have a migration strategy in place that takes both systems and transfer paths into consideration.

How Does ETL Work?

ETL is a migration process that involves extracting data from its sources, transforming it to fit the specific format of the target system, and loading it into the new system. Done in three separate steps, ETL is often used by smaller organizations or when smaller data sets are required for a hyper-specific purpose, such as annual reports or business intelligence.

The first step is data extraction, which can be done using a variety of methods from querying a database to directly reading a file. Once the data has been extracted, it may or may not need to go through a format transformation process using a series of rules and algorithms. Finally, the transformed data is imported, or loaded, into the target system.

ETL’s advantage is that it allows the transfer of only specific data rather than an entire database, which can save time and resources and reduce the risk of error and inconsistencies. But the process tends to require more manual intervention than data migration and it can interrupt business continuity at times.

What Are Some Popular Tools for Data Migration and ETL?

There are a number of tools available for data migration and ETL, each with its own strengths and weaknesses—here are a few of the most popular.

Microsoft icon

Microsoft SQL Server Migration Assistant

Microsoft SQL Server Migration Assistant simplifies the process of migrating data from to SQL servers. It supports a variety of database sources, such as Oracle, MySQL, and Access, as well as many data formats, including JSON, hierarchical data, spatial data, and XML data.

Pentaho icon

Pentaho Data Integration Kettle

Kettle is Pentaho’s free, open-source data migration tool capable of performing ETL processes, data cleaning, and data synchronization tasks. It supports various data formats, including XML data, CSV, and JASON, and can extract data from sources ranging from SQL and NoSQL databases to APIs, applications, and web services.

Informatica icon

Informatica PowerCenter

Informatica PowerCenter is a comprehensive data integration solution that combines a wide range of proprietary and open-source data integration strategies and solutions. It enables companies to export, transform, and load data from a variety of sources, but it’s best used for real-time integration.

Amazon Web Services icon

AWS Database Migration Service

AWS Database Migration Service (DMS) is a cloud-based solution that facilitates the movement of data from old systems to the AWS cloud. It supports a variety of database sources, including Oracle, SQL Servers, MySQL, and PostgreSQL. Also, as a fully-managed service, it ensures minimal downtime and continuous replication as well as automation of scalable features.

Talend icon

Talend Open Studio

Talend Open Studio is a free and open-source data integration tool that combines various data and application integration services, such as ETL, data quality, data profiling, and MDM (Master Data Management). It supports a wide range of data formats and can be used for batch and real-time data migration and integration.

Quest icon

Quest Migration Manager

Quest Migration Manager is a data migration and consolidation solution that facilitates and automates the process of moving data for active directories. It’s best used for the migration and restructuring of user accounts, data, and systems with a minimal impact on business continuity.

Bottom Line: Data Migration vs. ETL

While data migration and ETL may seem identical at first glance, there are a number of differences between the two approaches that better suit them for different tasks. Data migration is a good fit for moving entire databases, while ETL works best for limited or specific data sets. Choosing the right method depends on the volume of the data, the type of migration, and whether the data needs to be reformatted for the new database.

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