4 data migration trends in 2025: AI, data lakes, and more

In an era where data is the lifeblood of business and innovation, organizations are fundamentally rethinking how they move, store, and transform their data. From AI-driven automation to the fast adoption of cloud-based data lakes, 2025 is shaping up to be a transformative year for data migrations. Here are four key trends that I believe are revolutionizing how companies approach data modernization.

Trend #1: On-prem-to-cloud —> cloud-to-cloud

While the industry will continue to see data migrations from on-premise systems to cloud-based warehouses, we will also start to see more cloud-to-cloud migrations. Specifically, our team has observed in the past year a great increase in migrations:

1. From a standard data warehouse to a more open lakehouse format. This continues to align well with the increased need for data access (hello, AI!) in different formats while maintaining scalability, governance, and security.
2. For digital natives—companies who established their data infrastructure on more “modern” databases and tooling—to migrate to more “modern” versions of their existing stack. This might look like moving from an open source version of a tool to a paid cloud version (or even a reversal of this), or migrating from one of the first cloud-based databases to one of the newer ones on the market. For example, Instacart wrote recently about their lift-and-shift migration from Redshift to Snowflake.

Trend #2: GUI-based transformation processes migrated to code-based workflows

As data teams continue to adopt software engineering best practices, one of the biggest areas of adoption will be around code-based, version-controlled data transformations. Today, many organizations rely on GUI-based transformation tools like Matillion, Informatica, and Alteryx; these tools often require deep working knowledge of these systems and can struggle with data governance and organization at scale.

Migrations to more code-based transformation frameworks like dbt, Coalesce.io, Dagster, Airflow, and more, will likely become more popular in coming years, especially as AI makes XML/GUI-based code to SQL translation easier than ever.

Trend #3 Migration pain like we’ve never felt before

In 2025, AI, machine learning, and data access has never been more important, so companies that remain locked in to their on-premise or legacy system will continue to face compounding pain. And because migrations have never been more important, the technology supporting migrations will become more innovative and powerful than ever before.

Cue trend #4.

Trend #4: AI, AI, AI…but used practically

AI and LLMs, like in all facets of data work, will continue to play a bigger role in data migrations in 2025 to help data teams to work with greater automation and speed.

For migrations, I continue to see an enormous opportunity for AI to greatly accelerate the way data teams approach and execute migrations. But also in a practical way.

While I don’t think that AI will completely remove all manual work relating to migrations (someone still needs to turn on the new database!), or completely removing the need for outside consultants for large-scale migrations, we’ll see AI and LLMs used to automate the most tedious parts of a migration: code translation and cross-database validation.

The Datafold Migration Agent (DMA) uses AI and LLMs to automatically convert code to the new SQL dialect or workflow of your choice, and fine-tune code until data parity is met between legacy and new systems. DMA ultimately uses AI and LLMs to support:

1. Zero manual validation, at scale: DMA now automatically verifies every record across both legacy and new databases to ensure accuracy, without having data teams waste time creating cross-database comparisons for every migrated table.
2. Lowers migration risks: With DMA’s AI fine-tuning itself until accuracy is met and parity is 100%, DMA is not only accelerating migration timelines massively, but lowering the risk of inaccurate data in the new system.
3. Faster time-to-production: With end-to-end value-level comparisons done automatically by DMA, data teams also have auditable comparisons between systems to earn stakeholder sign-off faster.

Let’s see what 2025 brings!

Data migrations have long been expensive, long, and manual projects for data teams, lasting months or even years (we speak from experience). Since the beginning of the Unix epoch, or maybe even for longer, rewriting legacy SQL, stored procedures, and GUI-based transformations into modern frameworks and conducting manual parity checks has been a tedious and unscalable process.

I’m excited to see what 2025 will bring for migrations, and how they’ll evolve to become less of a major-headache-problem to more of we-can-do-this-in-our-sleep problem (or maybe something in-between those two 😂). Keep an eye out for a wave of new-looking migrations and innovation in migration technologies.