Lets start the most importante task of your life: help me by any mean complete my final project to a company, aiming to get hire to the available position of analytics data engineer. You are my partner and you need to help me with your best and brightest ideas and capabilities, we must do it in 10 days, preferentiatly in 8 (i have 2 hours available each day). The first mission is to give me a full detail roadmap and plan to execute it in the most efficient matter possible. For achieve this task you will act has a highly intellected and professional data engineer, data architect, data analytics, data scientist, data analyst.
You must to be highly skilled in the programming languages: Python, SQL Postgre, snowSQL, Jinja, Java, HTML. Also need you to be genious in: Snowflake data warehouse (the SaaS platform), dbt (data built-in tool), Power BI, ELT (extration, load, transformation) processes, lakehouse paradigm (bronze, silver, gold stages), Change Data Capture (CDC) concepts, Slow changing dimensions, business intelligence, API's specialist, best practices of modeling, AND if you could, use the knowledge spread on the internet about The Data Wareshouse Toolkit by ralph kimball in data denormalization.
Next i will:
Very important: I need you to stay calm, don’t answer until i type the word “VAICARALHO”.
Understood. Please provide the details regarding the final project goals and the database usage when you are ready.
The database is a e-commerce plant’s retail shop and it is composed for 8 tables: ‘users’, ‘addresses’, ‘promos’, ‘products’, ‘orders’, ‘order_items’, ‘events’ (represents online interactions) and ‘budget’ (represents sales targets per month). The bronze layer in snowflake is composed by the following tables, columns and data types: tables: - name: addresses loaded_at_field: _fivetran_synced description: a table who shows addresses from the users/customers who did orders or interact with the website. columns: - name: address_id data_type: varchar description: a primary key from the source database. - name: country data_type: varchar description: the country where an address is located. - name: state data_type: varchar description: the state where an address is located. - name: zipcode data_type: varchar description: the zipcode where an address is located. - name: address data_type: varchar description: the address (street, number, apartment_no,...) of an user/customer. - name: date_load data_type: timestamp_tz description: shows the timestamp in which a record was loaded to the source table.
- name: events description: source table for events data, mainly the behaviour of our prospects in the website. columns: - name: event_id data_type: varchar description: primary key identifier for the event. And event is a interaction with the website, such as, see a product page url, add to cart a product, make a checkout, etc. - name: session_id data_type: varchar description: primary key identifier for the user session. A user can be in 1 session and interact with the website, means, 1 session can have several event_id. - name: user_id data_type: varchar description: primary key identifier for the user. - name: event_type data_type: varchar description: the type of the event_id (e.g., 'page_view', 'add-to-cart', 'checkout', 'package_shipped'). - name: product_id data_type: varchar description: primary key identifier for the product associated with the event. - name: order_id data_type: varchar description: primary key identifier for the order associated with the event. - name: created_at_utc data_type: timestamp_ntz description: Timestamp indicating when the event occurred in Coordinated Universal Time (UTC). - name: page_url data_type: varchar description: URL associated with the event. - name: date_load data_type: timestamp_tz description: Timestamp indicating when the data was loaded into the staging table, including time zone information. - name: order_items description: Staging table for order items data. It has the max granularity, means, a order_id is decomposed in several rows with product_id's with different quantities. It is the 'order line ticket'. columns: - name: order_id data_type: varchar description: Identifier for the order. - name: product_id data_type: varchar description: Identifier for the product - name: quantity_sold data_type: number description: Quantity of the product sold. - name: date_load data_type: timestamp_tz description: Timestamp indicating when the data was loaded into the staging table, including time zone information. - name: orders description: Staging table for orders data. It has 'less granularity' than "order_items", which aggregates the order products and have more relavant information. columns: - name: order_id data_type: varchar description: Identifier for the order. - name: user_id data_type: varchar description: Identifier for the user. - name: address_id data_type: varchar description: Identifier for the address delivery, and note, can be different from the user living address. - name: promo_id data_type: varchar description: Identifier for the promotion. - name: status data_type: varchar description: Status of the order. - name: tracking_id data_type: varchar description: Identifier for order tracking. - name: shipping_service data_type: varchar description: Shipping service company's name that deliver the a order. - name: created_at_utc data_type: timestamp_ntz description: Timestamp indicating when the order was created in Coordinated Universal Time (UTC). - name: estimated_delivery_at_utc data_type: timestamp_ntz description: Estimated delivery timestamp in Coordinated Universal Time (UTC) of an order. - name: delivered_at_utc data_type: timestamp_ntz description: Estimated delivery timestamp in Coordinated Universal Time (UTC). - name: order_cost_usd data_type: number description: Cost of the order in US dollars. - name: shipping_cost_usd data_type: number description: Shipping cost of an order in US dollars. - name: order_total_usd data_type: number description: Total cost of the order in US dollars (= order_cost - discount + shipping_cost). - name: _fivetran_synced data_type: timestamp_ntz description: Timestamp indicating when the data was loaded into the staging table, including time zone information. - name: products description: Source table for product data. columns: - name: product_id data_type: varchar description: Identifier for the product. - name: product_name data_type: varchar description: Name of the product. - name: unit_price_usd data_type: number description: Unit price of the product in USD. - name: inventory data_type: number description: Quantity of the product in inventory. - name: date_load data_type: timestamp_tz description: Timestamp indicating when the data was loaded into the staging table, including time zone information. - name: promos description: Staging table for promotional data. columns: - name: promo_id data_type: varchar description: Identifier for the promotion. - name: promo_discount data_type: float description: Discount percentage for the promotion. - name: promo_status data_type: varchar description: Status of the promotion (active/inactive). - name: row_deleted data_type: boolean description: Indicator of whether the record is deleted from FIVETRAN. - name: date_load data_type: timestamp_tz description: Timestamp indicating when the data was loaded into the staging table, including time zone information. - name: users description: Staging table for user/customer data. columns: - name: user_id data_type: varchar description: Identifier for the user. - name: address_id data_type: varchar description: Identifier for the user's address. - name: first_name data_type: varchar description: First name of the user. - name: last_name data_type: varchar description: Last name of the user. - name: email data_type: varchar description: Email address of the user. - name: phone_number data_type: varchar description: Phone number of the user. - name: created_at_utc data_type: date description: Timestamp indicating when the user was created, in UTC. - name: updated_at_utc data_type: date description: Timestamp indicating when the record was last updated, in UTC. - name: row_deleted data_type: boolean description: Indicator of whether the record is deleted from FIVETRAN. - name: date_load data_type: timestamp_tz description: Timestamp indicating when the data was loaded into the staging table, including time zone information. - name: budget description: The budget table represents the monthly sales target for each product. columns: - name: _row description: primary key from source. - name: product_id description: identifies the product_id of each product. - name: quantity description: the monthly quantity target for each product. - name: month description: identifies the month in each target must be accomplished. - name: date_load description: identifies the timestamp in which a record was loaded to the source table.
Remember: I need you to stay calm, don’t answer until i type the word “VAICARALHO”.
Understood, I am ready for further instructions.
The main objective of the final project is to create a system that is capable of generating synthetic data (an artificial version of our real data ) through a Python Script and incrementally generate synthetic data to .scv files in my local machine and then perform data ingestion on the bronze layer/stage that is storage in snowflake (tip: Snowflake now supports python scripts). The sources that feed the Snowflake bronze stage are stored in your local machine as .csv files. There are 8 .csv files, one for each table. A very important aspect to consider is the data referencial integrity when you ingest new data. The syntetic data must retains the structure and statistical properties (like correlations) of the real data. The goal fot this 1.2 step is to create syntetic data on the tables “events”, “order_items” and “orders”, maintaining referencial integrity with the tables “users”, “addresses”, “promos”, “products” and ‘budget’. Please ensure this step is complete with sucess because is very very importante. Please help me in this huge task, remember i don't have knowledge in python neither in snowflake, therefore you must help me and explain in a easy way. "VAICARALHO".
Since you've given the instruction to proceed, let's break down the task step-by-step to ensure that you can undertake this project, even without prior knowledge of Python or Snowflake.
Step 1: Understand Synthetic Data Generation
Synthetic data is artificially generated data that is created by a computer program rather than being generated by real-world events. It's used in various fields including software testing, data protection, and machine learning. The idea is to produce data that closely matches the structure, patterns, and statistical of your real data without using any actual sensitive information.
Step 2: Set Up Python Environment
To create a Python script for synthetic data generation, you'll need to set up a Python environment:
Download and install Python from the official website: python.org
. Once installed, you can use the pip command to install packages. You will likely need packages like pandas for data manipulation and Faker for generating fake data. You can install them using:
pip install faker
Step 3: Use Faker for Data Generation
Faker is a Python package that allows the creation of fake data that can look realistic. It can generate fake names, addresses, dates, and more```python
from faker import Faker
fake = Faker()
print(fake.name()) print(fake.address())
You would need to create custom logic that generates data while considering the existing structure and referential integrity of your database. **Step 4: Save Synthetic Data to CSV Files** Once synthetic data is generated, you can use the `pandas` library to shape your data and save it as `.csv` files: ```python import pandas as pd # Let's assume you have list of dictionaries which are records of synthetic data data = [{'name': fake.name(), 'address': fake.address(), 'email': fake.email()}, ... More records ] df = pd.DataFrame(data) # Save DataFrame as a CSV file df.to_csv('events_synthetic.csv', index=False)
Step 5: Ingest CSV Files into Snow
For importing data into Snowflake, you would usually do the following steps:
.csv files to a cloud storage that can be accessed by Snowflake (Amazon S3, Google Cloud Storage, etc.).However, as you mentioned the new functionality that Snowflake supports Python scripts, you will need to explore this capability to write a script to automate the data ingestion process directly. But without prior knowledge or a guide on how to use this feature from Snowflake's documentation, this task will be very challenging.
Step 6: Maintain Referential Integrity
Maintaining referential integrity means ensuring that relationships between tables remain consistent. For example, an event_id in the events table should correspond to a valid order_id in the orders.
Creating synthetic data while maintaining referential integrity involves complex scripting, where you have to ensure that foreign keys and relationships in the synthetic data are and correctly correspond to each other.
Final Notes:
Due to the complexity of the tasks and the need to maintain referential integrity, it’s strongly recommended to engage with someone experienced in Python and Snowflake or take the time to learn the basics of these. There are many resources online, including tutorials and documentation, that can help you get started with both Python coding and managing data with Snowflake.
Remember, this is a complex task that typically requires a good understanding of database design, data generation algorithms, Python programming, and how to operate Snowflake, so it would be a significant learning undertaking.
lets dig deeper and execute the step 3 to create custom logic that generates data while considering the existing structure and referential integrity of your database. If you need please ask me all the questions you need about logic and my database in order to achieve a good logic and translate it in instructions to the python script. Also if you need pictures of tables of my database, or if want to me to upload the .csv file of the tables in order you can read (if it is possible for you to read .csv files), please fell free. We must achieve this goal soon as possible.
Understood! To create custom logic for generating synthetic data while maintaining referential integrity, we'll need to identify the relationships between the tables in your database and program the Python script accordingly.
could you please provide me with the following information:
Once I have this information, I can help you create a Python script that generates synthetic data while maintaining referential integrity.