Data Engineering 102: Introduction to Python for Data Engineering

Python is a computer programming language that is frequently used to create websites and software, automate tasks, and analyze data. Python is a general-purpose programming language, which means it can be used to create a wide range of programs and is not specialized for any particular problem. This versatility, combined with its ease of use for beginners, has made it one of the most widely used programming languages today.

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale.

Companies wanting to use data to enhance business operations are increasingly relying on data engineering. By examining how to utilize Python for data engineering, this post will show how Python has evolved into a crucial component of putting data engineering techniques into practice.

WHY USE PYTHON FOR DATA ENGINEERING

Python has become one of the world's most popular programming languages. It is widely used by data scientists to complete analytics and machine learning/deep learning applications. However, it should come as no surprise that Python is also gaining popularity among data engineers.

Data engineers can effectively process data by using Python Pandas dataframes. Additionally, a great way to better comprehend the needs of data scientists is to use Python programming for data engineering. As many data engineering tools use Python at the backend, it also aids data engineers in creating effective data pipelines. Additionally, Python is interoperable with a wide range of tools on the market, making it easy for data engineers to include them into routine work by simply learning Python programming. Let's now talk about these technologies and how they benefit data engineers in the field.

Advantages

1. A data engineer's job entails interacting with various data formats. Python is the best choice in these situations. Its standard library facilitates simple management. one of the most popular data file types are csv files.

2. It is frequently necessary for a data engineer to use APIs to extract data from databases. Python provides a module called JSON-JSON that can handle this kind of data, and the data in these situations is typically saved in JSON (JavaScript Object Notation) format.

3. A data engineer's duties include both gathering data from various sources and processing it. Apache Spark, one of the most widely used data processing engines, supports Python DataFrames and even provides an API, PySpark, to create scalable big data applications.

4. Directed acyclic graphs are used by data engineering tools like Apache Airflow, Apache NiFi, etc. DAGs are nothing more than task specification codes written in Python. Data engineers will therefore be better able to utilise these technologies by learning Python.

Aside from everything discussed above, Python is well known for being user-friendly and accessible to the general public. It has tremendous support from a vibrant developer community.

Python Libraries used for Data Engineering

pandas
The advantage of utilizing Pandas dataframes is they are exceptionally compatible with two prominent data types . JSON and csv. Dataframe objects also provide a wide range of simple operations that allow data engineers to carry out rapid exploratory data analysis.

ElasticSearch
To handle NoSQL databases (that do not contain data in rows and columns), data engineers usually use Elasticsearch.

BeatifulSoup
This well-known library is used for web scraping and data mining. For the purpose of preparing their data, data engineers use this to extract information from websites and work with JSON/HTML data formats.

PySpark
Apache Spark is one of the most widely used technologies for altering data in streams or batches. Python users can process enormous volumes of data thanks to the PySpark API.

Psycopg2, pyodbc, sqlalchemy
There are several ways of interacting with relational databases .One such tool popular among data engineers is MyPostgreSQL, and Python contains various libraries to connect to MyPostgreSQL, including pyodbc, Sqlalchemy, and psycopg2.

requests
Python's de facto standard for sending HTTP requests is the requests library. In order to let you concentrate on communicating with services and consuming data in your application, it isolates the difficulties of making requests behind an efficient, straightforward API.

A simple pyspark code that creates a dataframe from a list and prints the dataframe

import pandas as pd
import findspark
findspark.init()
# Import SparkSession
from pyspark.sql import SparkSession

# Create SparkSession 
spark = SparkSession.builder \
      .appName("pyspark_example") \
      .getOrCreate() 

data = [('James','','Smith','1991-04-01','M',3000),
  ('Michael','Rose','','2000-05-19','M',4000),
  ('Robert','','Williams','1978-09-05','M',4000),
  ('Maria','Anne','Jones','1967-12-01','F',4000),
  ('Jen','Mary','Brown','1980-02-17','F',-1)
]

columns = ["firstname","middlename","lastname","dob","gender","salary"]
df = spark.createDataFrame(data=data, schema = columns)
df.show()

If a Python developer is familiar with using Python for data engineering, they can become data engineers. In order for data engineers to do ETL procedures, they must be familiar with the various Python libraries and functions.