ETL vs. Data Migration vs. Data Transfer + Integration Tools

What is data migration?

Data migration is the process of moving data from one system to another. The source and destination could be any system. It could be a storage medium, a new application, cloud space, or database.

Data migration is an integral part of digitization and cloud adoption. It is required whenever a data update, backup, or switch to a newer system is undertaken. And this could happen for a variety of reasons, such as:

Upgrading the storage systems, servers, hardware equipment
Migrating from one cloud vendor to another
Consolidating multiple repositories, websites, or services into one system
Infrastructure maintenance operations
New or updated software installations
Data center relocation

Whatever the underlying purpose, a data migration project must be undertaken properly. In some cases, it can be straightforward, but when multiple sources and data formats are involved, it can be a complex process.

Hence, data migration processes, no matter their difficulty, are generally designed to follow a specific set of plans. These plans could differ for each organization or the particular purpose of data migration.

Some of the common types of data migration include storage migration, database migration, cloud migration, business process migration, and application migration.

Data migration vs. data transfer

At a quick glance, data migration and data transfer might look like the same process. But they differ quite sharply when it comes to their usability and application.

Both data migration and data transfer start with a process called data mapping. Data mapping connects data fields of different sources.

Data transfer is simply the transfer of data from one medium to another. It does not consider how the data is to be formatted or how it will be used by the destination system.
A good example would be the data transfer that happens across a wired internet connection or a simple USB-based file transfer. Data sets are simply transferred from one source to a target system.
But the data migration process is a much more complex process. It requires you to extract the data from the source system, like data warehouses, without any loss and prep it so it can be readily imported and used by the target system.
A data migration process can involve one or more sources; thus, data from multiple sources should also be consolidated. Data migration thus includes various stages of data cleaning, profiling, validation, and quality assurance.
While the big data transfer process can be carried out with simple copy, paste or move operations, data migration must be carefully planned and executed. You may also need additional tools and expertise to carry out a data migration process.
The data migration process's duration is also longer and dependent on various factors besides the data size.

3 Phases of data migration

As mentioned earlier, data migration must be planned as a standalone process and executed properly. This calls for an optimized data migration strategy that considers all the different factors that come into play.

Doesn't matter if you are migrating data to Azure, Amazon AWS, Redshift, Salesforce, or Oracle database, the steps and procedure involved will be pretty much the same.

The organization must consider factors like data confidentiality, security processes, technology, schema, and expertise required when creating the data migration strategy.

Thus, data migration is often carried out as a project, just as any other planned activity is taken care of. The three main phases of a data migration project are explained below.

1. Planning

The planning phase of the data migration process puts out the strategy of the data migration project.

In this stage, a basic need analysis is followed by carefully considering the various requirements and technology required to successfully complete the data migration process. This phase must also analyze and decide on the various methods, tools, and other resources and the budget allocated for the data migration process.

Some of the important things to consider when crafting a data migration plan are:

Data-relevant parameters including data confidentiality, location, format like CSV, and other security and privacy concerns. This process is called data discovery and identification.
The size and scope of the data migration project. The schedule, goals, and timeline of the project must be established. And all relevant stakeholders must be communicated the same.
Assessment of the resources available.
The various steps involved in executing the data migration plan.
The testing frameworks that will be used to validate the migrated data.
Any follow-up and maintenance operations associated with the data migration plan.

2. Migration

Once you have a data migration plan, you must execute it according to the plan. Whatever strategy you have chosen to follow, it must be communicated with the relevant stakeholders. Failure to follow the process or a plan that did not consider the practicalities of data migration could result in a failed data migration process.

Here is a basic flow of operations that happen during a migration process:

Data preparation from data lake, such as data cleanup and enhancement.
A proof-of-concept migration is carried out to validate the strategy.
The migration process is tested for any inadequacies and flaws.
Once the proof of concept migration is completed, production migration is carried out on a larger scale covering the actual project scope.

Establishing a monitoring and reporting framework is important to catch and resolve any errors during the migration process. You should also follow a proper quality assurance framework to validate the finished migration process.

3. Optimization

Optimization is the final phase of a data migration process, where an existing data migration process is evaluated and improved upon.

Optimization aims to reduce errors, improve source data quality, and make the process more resource- and time-efficient.
This calls for continuous monitoring throughout the data migration process and follow-up maintenance activities. Some common metrics, such as CPU, memory, and IO utilization, can be used to regulate performance.
Data operations in the new system must be evaluated and optimized to reduce the cost, performance, and security concerns that could arise.

In many organizations with huge volumes of data, data migration is often carried out in different phases. The optimization stage of a data migration activity helps identify performance bottlenecks and pain points with the current strategy. This information can further improve any ongoing or future data migration projects.

It is important to mention that data migration differs from data replication. Data migration moves the data to a new location and abandons the old database. On the other hand, data replication copies the data to a new location without discarding or deleting the data source.

How is Data Migration Different from ETL?

ETL stands for Extract, Transform, and Load, which is an operation where data from one or more sources are gathered, transformed, and loaded into a new system for use. On the surface, both ETL and data migration processes can be quite similar.

While data migration is basically a process of moving data from one system to another, ETL is specifically used when data needs to be transformed, that is, changed to a degree before loading.
Both data migration and ETL can be used together or separately as the organization requires.

In general, data migration rarely involves any activity that changes the data. A good example of ETL can be demonstrated with the data integration process when merging two different organizations' data.

The data types from different sources can have different formats and overlapping information that must be properly sorted before loading into the new system.

What is the Purpose of ETL?

ETL is used in workflows where multiple data sources are involved, and data needs to be transformed in some way to remain usable. ETL operations can be summarized as:

Extracting data from legacy systems or multiple sources like SQL servers, email, web pages, CRM, ERP systems, flat files, and so on.
Data cleaning to ensure data consistency. The data transformation activities can also include data manipulation operations such as doing translations, grouping raw data, calculations, currency conversions, text editing, unit conversions, and so on.
Loading the prepared data into a target database.

The ETL process can also be applied as part of a wider data migration project. These specific use cases include areas like business intelligence, machine learning, software synchronization, legacy system update, and so on.

What are the Best Tools for Data Migration?

Here are some important things to look for in your data integration tool:

Supported data sources
Module extensibility
Technical support provided
Ease of use
Customization availability
On-premise or cloud-based support
Proprietary or open-source tool
Processing specifics such as batch or real-time processing

Here are some of the best tools for data migration:

1. Portable

Portable is the best ETL tool if you use long-tail data sources. Potable is backed up by a good team that delivers excellent tech support and a high level of custom connectors.

Advantages

A huge number of ready-to-use built-in connectors (more than 300)
Quick turnaround times for any custom connector request
Free long tail connectors maintenance

Disadvantages

It is only available for use in the US

Pricing

Portable allows you to start with a free plan that supports free synchronizations from any source to any data warehouse. You can be charged $200 per month for each scheduled data flow. As for custom solutions, you can get in touch with the Portable team and get quotes based on your scalability and requirements.

2. Informatica

Informatica embeds various data virtualization tools with features that help you deal with data governance, integration, application integration, and analytics. Some notable features include Informatica PowerCenter, B2B Data transformation, and more. It is a comprehensive set of tools best suited for organizations looking for a one-stop solution for data transformation.

Advantages

Provides both cloud-based and on-premise deployments
Advanced data transformation features
Good customer support and documentation

Disadvantages

Limited scheduling options
Debugging can be difficult
Performance-heavy transformation operations

Pricing

Informatica follows an IPU (Informatica Processing Unit) pricing model where you pay for as many capabilities as you seek. The more scaling up you need, the more your cost will be.

3. Talend

Talend is a data management platform with easy-to-use GUI-based options. It has an ETL feature called Stitch that carries out a good range of data extraction, basic transformations, and loading operations. It can be easily set up with Python, Java, and SQL.

Advantages

No coding knowledge is required. Easy to use built-in GUI
Easy cloud data integration and integration with all major platforms
Connectors available for Spark, machine learning, and NoSQL

Disadvantages

The free version is very limited and comes with self-service
Limited data transformation options
No on-premise deployment options
Limited destinations

Pricing

While Talend does have a forever-free plan, it is very limited in terms of feature support. You can use their ETL tool, Stitch, at a starting price of $100 per month, which can go up to $2500 per month. Depending on the rows and number of destinations supported.

4. Dell Boomi

Dell Boomi is another GUI-based data transformation tool that can work with both cloud and on-premise deployments. It is best suited for organizations looking to employ a data migration tool across a hybrid infrastructure.

Advantages

Real-time data integration support
Connectors for both public and private clouds
Excellent performance and cost savings

Disadvantages

No free trial is available
Needs better documentation
Limited dashboard and GUI options

Pricing

While Boomi does not offer any free trial or freemium version, it does not charge any setup fee either. Their on-premise plans start from $500 per month, and advanced plans may go up to $8000 per month.

5. Jitterbit

Jitterbit is an iPaaS service provider that can work with both cloud (SaaS based) and on-premise deployments. Jitterbit helps automate your data processes with the help of AI-based automation technology. They can be used as a one-stop solution for all your data transformation needs covering the entire data life cycle.

Advantages

Supports both on-premise and cloud data flows
AI-based engine for optimized data integration processes
300 pre-built templates to ease the data transformation process

Disadvantages

Needs better UI
A higher learning curve is needed to get used to the bulky features and testing required
Expensive pricing plans

Pricing

The cloud-based data integration services for Jitterbit start at $100 per month, and other advanced plans start at around $1000 per month. It does offer a free trial for a limited time that you can check out.

Recap of ETL for Data Migration

To summarize, data migration has various applications in an organization. You might want to migrate data to relocate the data center, conduct infrastructure maintenance operations, upgrade storage systems, and so on. The best data migration tool supports a range of data sources, is easy to use, is customizable, and supports ETL operations. Speaking of which, Portable ticks all the right options and helps you with effortless data migration. To learn more, contact us today.