Managing Database Migrations with Alembic

Damla_Kurt — Sun, 28 Jun 2026 11:53:18 +0000

As applications evolve, their data models also evolve. Developers may need to add new fields, change existing ones, or introduce completely new tables to support new features. In SQL-based systems, these changes must also be reflected in the database schema. If the code changes but the database does not, the application will break or behave incorrectly.

Alembic is a tool used together with SQLAlchemy to manage these database schema changes in a controlled and safe way. Instead of manually writing SQL scripts every time a change happens, Alembic helps developers track, generate, and apply these changes step by step.

The process starts when the developer updates the SQLAlchemy model. For example, a Movie table might originally have only id and title, and later the developer adds a new field such as rating. At this point, only the Python model has changed, not the database itself.

To detect and prepare this change for the database, the developer runs:


alembic revision --autogenerate -m "add rating column"

This command compares the current SQLAlchemy models with the existing database schema. Based on the differences, Alembic automatically generates a migration file inside the alembic/versions/ folder.

Inside this file, Alembic writes Python code that describes the database change. For example, it may include a function like:

def upgrade():
    op.add_column(
        "movies",
        sa.Column("rating", sa.Integer())
    )

At this stage, the developer does not trust the file blindly. They open and review it manually to make sure everything is correct. They check whether the correct table is being modified, whether the column name is correct, and whether the data type matches the intention.

After confirming that the migration is correct, the developer applies it to the database using:

alembic upgrade head

This command executes the migration and updates the actual database schema. After this step, the database and the SQLAlchemy models are synchronized again.

In summary, the workflow is: the developer first changes the model, then generates a migration file with Alembic, reviews the generated code inside the alembic/versions/ folder, and finally applies the changes to the database using the upgrade command. This process ensures that database changes are consistent, version-controlled, and safe across different environments.

LangChain, NLP

Damla_Kurt — Sun, 28 Jun 2026 11:50:02 +0000

Artificial Intelligence (AI) is the field of creating computer systems that can perform tasks requiring human intelligence, such as learning, problem-solving, pattern recognition, and decision-making. AI is widely used in many areas of daily life and continues to transform the way people interact with technology. However, AI does not think or feel like humans. It works by analyzing data and following algorithms, which means its decisions depend on the quality of the data it is trained on. If the data contains bias, the AI may also produce biased results.

LangChain
One of the most useful tools for building AI applications is LangChain. LangChain is a framework that helps developers create advanced conversational AI systems by connecting large language models with external resources such as APIs, databases, and memory. It also supports multi-step reasoning, prompt management, and agent-based workflows, allowing AI applications to provide more accurate, context-aware, and dynamic responses. As a result, LangChain makes it easier to develop powerful and flexible AI solutions for real-world applications.

NLP

Natural Language Processing, or NLP, is a branch of artificial intelligence that focuses on enabling machines to understand, interpret, and generate human language. It works by training on large text datasets to internalize syntax, context, and meaning. NLP powers key applications like sentiment analysis, named entity recognition, and translation. Using tools like LangChain, developers can build applications, such as chatbots, that leverage NLP models to interact naturally with users. These models rely on large-scale language understanding, enabling them to handle complex NLP tasks and be iteratively improved through user feedback and performance monitoring.

DEV Community: Damla_Kurt

Managing Database Migrations with Alembic

LangChain, NLP