What is ETL for data migration?

ETL refers to the cycle of extracting (E), transforming (T), and loading (L) data from various sources and changing the data to meet specific business rules and requirements. The data is then loaded into target storage, typically a data warehouse. ETL in data migration refers to moving data or information from one source to another. While the process of data migration might appear straightforward, it requires significant data transformation processes.

Data can be extracted from a number of sources, including:

• Cloud environments, such as cloud-based CRM or ERP software
• Existing databases
• Legacy systems
• On-premise or cloud data warehouses
• Data storage platforms, solutions, or applications
• Data analytics tools
• Mobile devices

In the case of data integration, ETL solutions can synchronize data from one source to another. For example, when pulling data from a custom-built website to an ERP or CRM system, data is synchronized in both locations.

ETL vs. data migration vs. data integration

ETL, data migration, and data integration are all processes that involve data. However, they’re each used for specific purposes and with different methods. Understanding how each is different from the others will help you choose the right data migration approach for your business.

Put simply, data migration and ETL processes are used when organizations are looking to understand more of their data. Additionally, both approaches entail moving data from one source to another. Data integration, on the other hand, does not involve moving information from one place to another. Instead, data is gathered from various sources for the purpose of cleansing and analyzing the gathered data.

ETL

As previously mentioned, ETL stands for Extract, Transform, Load. ETL involves extracting data from one or multiple sources, cleaning it, and transforming it into an easily digestible format that can be understood across the enterprise. The data is then loaded into another database or destination. The ETL tool that extracts data from one source is also responsible for maintaining data consistency, ensuring information is accurate and up-to-date before sending it to the final destination.

Data migration is similar to ETL as both processes move data from one information source to another. However, with data migration, the format of data does not change, whereas in ETL processes, it does.

Data migration

IBM describes data migration as “the process of transferring data from one storage system or compute environment to another.” Data migration can take place in a few ways, including between computer systems, storage systems, or data formats. There are a number of reasons organizations may need to complete data migration projects, such as replacing on-premise servers or consolidating data centers. It’s also an essential step in migrating to a cloud environment.

One of the primary differences between data integration and data migration is that during data integration, a number of disparate data sources are brought together. Data migration, on the other hand, simply involves moving data from one place to another.

Data integration

Data integration is the process of collecting and integrating data from internal and external systems into a single, unified view. The process begins with data ingestion, then cleanses the information, and finally maps and transforms it. More simply, data integration is a critical step in creating actionable business intelligence.

Data integration is similar to both data migration and ETL; however, data integration is typically utilized when organizations want to get more out of their existing data. Combining data sources from across the enterprise provides additional context into insights, enabling users to make better, more informed decisions.

What is the ETL data migration process?

The ETL data migration process will vary depending on the specific ETL tool being used. However, most follow a similar process:

1. First, the data migration is thoroughly scoped to determine stakeholders, budgets, deadlines, communication processes, and reporting requirements.
2. Next, resources are evaluated to determine which method and tool will be used during the process.
3. The migration process is planned out and testing resources are put in place.
4. The migration process is tested from beginning to end to ensure limitations or weaknesses are identified and resolved before migration occurs.
5. The data migration team begins developing the migration project. This is usually done in stages to mitigate risks before they occur.
6. Finally, after the go-live method is tested, the migration process is executed.

Use Cases and examples of ETL migration

Knowing when to use ETL migration is a critical part of using the process correctly. Below are just a few examples and use cases of when ETL migration is best utilized:

ETL in data warehousing
The most common use case of ETL is data warehousing. For example, when a client needs to bring historical data into their current warehouse, ETL tools centralize that data, providing users with easy access to critical insights.

Enabling self-service reporting
ETL tools eliminate the need for IT personnel or technical support when creating data reports. The solutions can easily extract data from across the enterprise and create data visualizations such as graphs, charts, maps, or dashboards. By democratizing data, all employees can access and analyze data to make decisions.

Provides real-time monitoring and alerts
ETL tools can create pipelines that constantly extract data from various sources and process it in real time. With real-time analysis, organizations can actively track and monitor key metrics as they’re happening, which drives business outcomes.

How to choose the right ETL tool for data migration

Not all ETL tools are created equal. When evaluating various platforms, look for solutions that are:

• Flexible: With data coming from a variety of sources, you’ll want an ETL tool that is flexible enough to coordinate, merge, and change data with ease.
• Cost: Price is a critical factor when evaluating ETL tools. When looking at tools, ask questions such what annual costs you’ll have to pay, does the cost go up if the amount of data sources increases, or how much will it cost to build your initial pipeline.
• Ease-of-use: Some ETL tools are designed for more technical audiences, so you’ll want to ensure you’ve considered the use cases and audiences that will be using the tool. Ensure the platform balances simplicity with capability so you can get the most from your data.

Using ETL tools to streamline and simplify data migration can be incredibly beneficial. However, prior to undergoing the process, you’ll want to ensure you’ve chosen a tool that is flexible, scalable, and powerful enough to handle your data. When you’ve selected the right tool, it’s easy to migrate data even across the most disparate of sources. This makes it possible for your entire organization to delve deep into data and make better, data-driven decisions.

ETL for data migration FAQs

Have additional questions about ETL for data migration or how to choose the best ETL migration platform? We’re here with answers.

Is ETL used in data migration?
Yes, ETL is a type of migration process that involves extracting data from a number of sources and loading it into a new system.

What are the four types of data migration?
The four types of data migration are:

• Database, which involves transferring data between two different databases
• Application data migration, when a company transfers information from one framework to another
• Storage migration, which occurs when moving data from a hard disk or on-premise solution to the cloud or another storage device
• Cloud migration,when companies move data stored in other software or applications to the cloud

What is the difference between data transfer and ETL?
The primary difference between these two processes is scale. Data transfer or migration processes are usually used to transfer large datasets, while ETL is used for smaller datasets or parts of databases.

What are the steps of data migration?
Typically, data migration involves assessing the data sources, designing the migration path, building a migration solution, conducting live tests, executing the migration plan, and auditing the process.