5/28/2024 0 Comments Extract transform load exampleThe code below creates two lists, users and uploads, which will be used in the final step: from datetime import datetime, timedeltaįor i, result in enumerate(users_json): The JSON objects created in the code above are nested, and contain more data than is required for the tables defined.Īn important intermediary step is to transform the data from its current nested JSON format to a flat format that can be safely written to the database without error.įor the example running through this article, the data are relatively simple, and won’t need much transformation. Transformīefore the data can be loaded into the database, it is important to ensure that it is in the correct format. The next step will be to transform and load this data into the tables defined earlier. The data is currently held in two objects ( users_json and uploads_json) in JSON format. Python’s Requests module can be used to call these APIs, as shown below: import requests The Users table will be populated with profiles randomly generated at, and the Uploads table will contain lorem ipsum-inspired data courtesy of JSONPlaceholder. The example below uses two APIs to simulate data for the fictional blogging platform described above. Maybe your app calls an in-house or third-party API, or perhaps you need to read data logged in a CSV file. The exact details can vary wildly from case to case, depending on how the raw data is provided. Once the schema has been defined, the next task is to extract the raw data from its source. The checkfirst=True parameter ensures that new tables are only created if they do not currently exist in the database. The final two lines actually create the tables. It’s very much the same idea - each column is defined as before. Use primary_key=True to denote columns which will be used as primary keys. Each column in this table is defined as a class variable using SQLAlchemy’s Column(type), where type is a data type (such as Integer, String, DateTime and so on). This example uses a relative path to the SQLite database created earlier. The exact connection string will depend on the version of SQL you are working with. Then, use create_engine(connection_string) to connect to your database. Uploads._table_.create(bind=engine, checkfirst=True)įirst, import everything you need from SQLAlchemy. Users._table_.create(bind=engine, checkfirst=True) UploadId = Column(Integer, primary_key=True) ![]() UserId = Column(Integer, primary_key=True) from sqlalchemy import *įrom import declarative_baseĮngine = create_engine('sqlite:///demo.db') One is a table of users, and the other is a table of posts uploaded. Schemas can be defined in raw SQL, or through the use of SQLAlchemy’s ORM feature.īelow is an example showing how to define a schema of two tables for an imaginary blogging platform. In a new directory, use the terminal to create a new database: $ mkdir sqlalchemy-demo & cd sqlalchemy-demoĪ database schema defines the structure of a database system, in terms of tables, columns, fields, and the relationships between them. If you don’t have it on your system already, you can follow these instructions to get up and running. ![]() SQLite is an open-source implementation of SQL that usually comes pre-installed with Linux and Mac OS X. $ sudo pip install sqlalchemyĪs for SQL itself, there are many different versions available, including MySQL, Postgres, Oracle, and Microsoft SQL Server. You can install SQLAlchemy using the pip package installer. ![]() Here, we’ll run through some highlights of SQLAlchemy to discover what it can do and how it can make ETL development a smoother process. SQLAlchemy’s Object Relational Mapper (ORM) and Expression Language functionalities iron out some of the idiosyncrasies apparent between different implementations of SQL by allowing you to associate Python classes and constructs with data tables and expressions. It allows data engineers and developers to define schemas, write queries, and manipulate SQL databases entirely through Python. One such solution is a Python module called SQLAlchemy. Luckily, there are open source solutions that make life much easier. Worse still, an unreliable data pipeline will silently contaminate your database with false data that may not become apparent until damage has been done.Īlthough critically important, ETL development can be a slow and cumbersome process at times. A non-robust pipeline will break easily, leaving gaps. It is important to design efficient, robust, and reliable ETL processes, or “data pipelines.” An inefficient pipeline will make working with data slow and unproductive. This process is commonly referred to as “Extract-Transform-Load,” or ETL for short. One of the key aspects of any data science workflow is the sourcing, cleaning, and storing of raw data in a form that can be used upstream.
0 Comments
Leave a Reply. |