The phrase “data is the new oil” is likely shared. The sound is both significant and somewhat perplexing. Exactly what does it mean? More importantly, what role do you play?
This guide is for you if you are interested in data but do not know how to develop your curiosity into a skill or a career. Beginning with learning about SQL, it guides you through the initial stages of creating something valuable.
Let us get started.
Communicating with Data: Acquiring SQL
The majority of people start here. The majority of structured data is stored in databases, which we communicate with using SQL (Structured Query Language).
Every command does not have to be committed to memory. Consider SQL to be similar to posing a straightforward question:
- “Who registered last week?”
- “What was the number of French orders?”
- “How long did users spend on the site on average in March?”
Writing queries may initially seem technical. But it all makes sense when you see actual responses derived from actual data. That is an important moment. Additionally, no matter how sophisticated you become in this field, you will continue to use SQL. As a result, it is advantageous to become accustomed to it early.
Python: Using Data to Perform Actions
Using SQL, you can ask questions. Python enables you to take action based on the answers.
It begins with basic tasks, such as opening a CSV file and tidying up some jumbled data. But soon, you will be writing scripts to gather information from websites, process it, and possibly even generate reports on their own.
Additionally, you will begin utilizing some useful Python libraries:
- Pandas for manipulating data
- Requests to obtain information from APIs
- cron or schedule to execute scripts on a predetermined timetable
Being an expert in Python is not necessary. A good place to start is by writing straightforward, useful scripts.
Where Is All of This Information Stored?
Now that you are working with data, you will begin to consider its storage location.
You are likely to encounter the following locations:
- Relational databases, such as MySQL or PostgreSQL, are well-structured, well-organized, and excellent for structured data.
- BigQuery and Snowflake are examples of cloud data warehouses designed for speed and scale, particularly for analysis.
- More adaptable data lakes, such as Amazon S3, are used to store unstructured or raw data.
You do not have to become proficient in every one of them at once. Try launching a small database locally, adding some sample data, and then beginning to query. It is an excellent learning method.
Create Something Simple (Yet Real)
While following tutorials can teach you a lot, building something yourself teaches you a lot more.
Avoid thinking too much about it. Begin modestly:
- A script that monitors your city’s daily weather conditions.
- a dashboard that displays your individual spending.
- A small tool that makes movie recommendations based on your personal ratings.
Connecting the dots is the aim: obtain some data, take action with it, and make it easily useful. It is your creation, even if it is not flawless, like when you cook your first meal.
From Pipelines to Scripts
A pattern will emerge as you build more: gather data, clean it, store it, and use it. We call that a data pipeline.
Initially, it is merely a Python script consisting of a few steps. However, you will soon need tools to help you manage and automate things as they expand:
- Using Airflow to plan and monitor your data workflows
- To change and test data within your database, use DBT.
- Docker can help you create a repeatable and consistent environment.
Once you start building regularly, these tools will make your life easier, even though they are not for beginners.
Utilizing Tools
Your projects will become increasingly reminiscent of real-world work as you proceed. It entails learning:
- Git to monitor modifications and work together
- Platforms for cloud computing to handle and store data at scale
- Tests and basic logging to identify issues early
The phrase “I am trying things out” gives way to “I am building something that other people could use” at this point.
Where to Go Following This
You are on the right track if you have read this far. The next steps could include improving the reliability of your pipelines, learning better SQL techniques, or creating cleaner data models.
But do not worry about that right now.
Continue to learn. Continue to construct. And continue to solve minor issues; that is how all data engineers begin.
Developing More Intelligent SQL
In the upcoming post, we will go beyond SQL to write better, faster, and more maintainable queries that are used on a daily basis by actual businesses.
Until then, try new things, do research, and most of all, have fun.
