Introduction to Data Engineer with Python
Data Engineers use Python for data analysis and creation of data pipelines where it helps in data wrangling activities such as aggregation, joining with several sources, reshaping and ETL activities. Python has several tools that help in data analysis and there are libraries which help to complete the analytic process with few codes. Knowledge of database tools is important for a data engineer to manage the data well and to know the analytic process. This helps to combine several tasks into a single role thus managing the analytics process easily. Complex problems can be solved easily with Python in analytics.
What is Data Engineer with Python?
Programming skills are important for a Data Engineer and Python being easy to code, most Data Engineers are happy with Python being used in pipelines and data analytics. Data architecture and the way database works are known by Data Engineers so that all the implementation and database development can be started by them easily. This database should be linked with any applications and Python knowledge is inevitable here. Machine learning is also important for Data Engineers which can be managed with the knowledge of Python.
Top 5 Python used in Data Engineering
These are the most used Python packages in Data Engineering.
Various scientific methods in addition to the numerical methods are offered in this module which can be used by Data Engineers to solve complex problems. Optimization modules along with linear algebra, integration and interpolation functions, several special functions, and even signal image processing can be done with the help of SciPy module in Python.
The data structures offered here are simple and easy to understand and has high performance in all the data provided. This package is good in data wrangling and data manipulation. Data can be visualized and handled faster than any other modules provided by Python.
3. Beautiful Soup
This module helps in data extraction by scraping and parsing techniques. Any format can be parsed easily as it considers the data as hierarchically ordered including the web pages. This helps data engineers even to parse HTML and any other web pages.
This module is used for the sole purpose of data extraction, manipulation, and data table loading. Tables can be easily converted here with few lines of code and the data export is also supported here. This helps to transfer data from SQL, CSV, or any other format easily. This is called PETL due to Python module for Extracting, Transforming, and Loading tables.
ETL workflows can be easily created using Pygrametl as this has all the ETL functionalities. This is faster and all the codes are available directly in the module. The dimension in the ETL is measured by a dimension object and has a connection with a table or several tables within the dataflow. All the activities of ETL such as lookup, insertion, and deletion of data, copying data from one source to another is done by Pygrametl itself.
Use Cases Data Engineer with Python
1. Data Acquisition: Data acquisition involves contacting the source and getting the data in the required format and these sources can be API or any web application. Python helps here with the coding and the packages to build the pipeline based on the source and collect the information. Also, we can use ETL jobs to do data acquisition which again involves Python.
2. Data Manipulation: Python has several libraries and Pandas library is known for manipulating data for user’s requirements. We can read the data in any format and manipulate it. If the dataset is large, we can use PySpark library to manage the data.
3. Data Modelling: Python can communicate with teams as Machine Learning and TensorFlow is involved with this. It uses Keras, Scikit-learn, or PyTorch to do data modelling and hence it can be used to see where data stands with respect to Data Engineer.
4. Data Surfacing: Python can set up APIs so that data can be seen easily and this is done with the help of Flask and Django frameworks. This includes normal report creation as well.
Role of Data Engineer with Python
- Working on Data architecture is important for Data engineers as they should know the working of the system and should plan the work based on the requirements of the organization. Here Python is not much in use as the visualization tools are used here mostly.
- Data collection is another important process in Data Engineering where they collect data from different sources and manipulate the same. Python is used here to collect the data from the source in the form of pipelines and data manipulation with the help of Data Bricks or any other analytics platform.
- Data Engineers should do research about the data and how it has performed in the past years. Graphs can be drawn easily with the help of Python to know the data performance which makes the work quicker and efficient.
- Data Engineers should not rely on one library alone in Python as other libraries have different approaches and faster solutions to the same problem. Data Engineer should learn always and make changes to their approach when the efficient method is figured out.
- After data storage, it is important to identify the data patterns from the same source. Here, Python is useful with its visualization skills. If there are any data anomalies, this can be solved and any IDE such as Jupyter IDE can be used to do data engineering problems.
- Several automation will be needed while creating the data pipelines and here Python will come in handy as it can do all the coding work efficiently.
For beginners who are new to Data Engineering, it is easy to learn Python and do data analysis. Data from all over the world are being processed by Data Engineers followed by Data Scientists and hence the profile of Data Engineer with Python will be in demand for the coming years as well.
This is a guide to Data Engineer with Python. Here we discuss the Introduction, What is Data Engineer with Python, uses cases, role. You may also have a look at the following articles to learn more –
- What is Data Engineering?
- Data Engineer Interview Questions
- Data Scientist vs Data Engineer
- Data Science vs Data Engineering