Python is one of the most popular programming languages ââin the world. It often ranks high in surveys – for example, it ranks first in the Popularity of Programming Language index and second in the TIOBE index.
The main focus of Python has never been web development. However, a few years ago software engineers saw the potential of Python for this specific purpose and the language experienced a massive surge in popularity.
But data engineers couldn’t do their job without Python either. Since they are heavily dependent on the programming language, it is still important to discuss how using Python can make the workload of data engineers more manageable and efficient.
Cloud platform providers use Python to implement and control their services
Everyday challenges that data engineers face are not dissimilar to those of data scientists. The processing of data in its various forms is a central issue for both professions. From a data engineering perspective, however, we tend to concentrate on industrial processes, such as ETL (Extract-Transform-Load) jobs and data pipelines. They have to be sturdy, reliable and ready to use.
The serverless computing principle enables data ETL processes to be triggered when required. After that, the physical processing infrastructure can be shared among the users. This allows you to increase costs and consequently reduce the administrative burden to a minimum.
Python is supported by the serverless computing services of popular platforms, including AWS Lambda Functions, Azure Functions, and GCP Cloud Functions.
In turn, parallel computing is required for the “heavy-duty” ETL tasks in connection with big data problems. Splitting the transformation workflows across multiple worker nodes is essentially the only feasible way to achieve the goal in terms of memory and time.
A Python wrapper for the Spark engine called PySpark is ideal because it is supported by AWS Elastic MapReduce (EMR), Dataproc for GCP, and HDInsight. As far as the control and management of resources in the cloud is concerned, application programming interfaces (APIs) are provided for each platform. Application programming interfaces (APIs) are used when performing job triggering or data retrieval.
Python is consequently used on all cloud computing platforms. The language is useful when performing a data engineer’s job of setting up data pipelines along with ETL jobs to recover (ingest) data from various sources, process / aggregate (transform) it and make it finally available to end users.
Using Python for data ingestion
Business data comes from a variety of sources such as databases (both SQL and NoSQL), flat files (such as CSVs), other files used by businesses (such as spreadsheets), external systems, web documents, and APIs.
The wide acceptance of Python as a programming language leads to an abundance of libraries and modules. A particularly fascinating library is pandas. This is interesting when you consider that it offers the possibility of reading data into “DataFrames”. This can be done from a variety of different formats, such as CSVs, TSVs, JSON, XML, HTML, LaTeX, SQL, Microsoft, open spreadsheets, and other binary formats (which are the results of exports from various business systems).
Pandas is based on other scientifically and computationally optimized packages and offers a comprehensive programming interface with a multitude of functions that are required to process and transform data reliably and efficiently. AWS Labs maintains an aws-data-wrangler library called “Pandas on AWS” that is used to manage known DataFrame operations on AWS.
Using PySpark for Parallel Computing
Apache Spark is an open source engine for processing large amounts of data, which controls the parallel computing principle in a highly efficient and fault-tolerant manner. While it was originally implemented in Scala and natively supports this language, it is now a universally used interface in Python: PySpark supports most of Spark’s features, including Spark SQL, DataFrame, Streaming, MLlib (machine learning), and Spark Core. This makes it easier for Pandas experts to develop ETL jobs.
All of the above cloud computing platforms can be used with PySpark: Elastic MapReduce (EMR), Dataproc and HDInsight for AWS, GCP and Azure, respectively.
In addition, users can accompany their Jupyter notebook to accompany the development of the distributed Python code, for example with natively supported EMR notebooks in AWS.
PySpark is a useful platform for remodeling and aggregating large groups of data. As a result, this makes it easier to use for subsequent end users, including, for example, business analysts.
Use Apache Airflow for job scheduling
By using renowned Python-based tools within on-premise systems, cloud providers are motivated to commercialize these in the form of “managed” services that are therefore easy to set up and use.
This applies, among other things, to Amazon’s Managed Workflows for Apache Airflow, which was launched in 2020 and makes it easier to use Airflow in some of the AWS zones (nine at the time of going to press). Cloud Composer is a GCP alternative for a managed airflow service.
Apache Airflow is an open source Python-based workflow management tool. It allows users to programmatically create and schedule workflow processing sequences and then follow them using the Airflow user interface.
There are several alternatives to Airflow, such as the obvious options from Prefect and Dagster. Both are Python-based data workflow orchestrators with a user interface and can be used to create, run and monitor the pipelines. They aim to address some of the concerns some users face while using Airflow.
Strive to achieve data engineering goals with Python
Python is valued and valued in the software community because it is intuitive and easy to use. The programming language is not only innovative but also versatile and enables engineers to take their services to a new level. Python’s popularity with engineers continues to grow, and support for it continues to grow. The simplicity at the heart of the language means that engineers can overcome any obstacles along the way and get jobs done at a high level.
Python has a prominent community of enthusiasts who work together to improve the language. For example, errors are corrected, thereby opening up new opportunities for data engineers on a regular basis.
Every engineering team works in a fast-paced, collaborative environment to develop products with team members from different backgrounds and roles. Python, with its simple composition, allows developers to collaborate more closely on projects with other professionals such as quantitative researchers, analysts, and data engineers.
Python is quickly coming to the fore as one of the most widely accepted programming languages ââin the world. The benefits for data engineering should therefore not be underestimated.
Mika Szczerbak is a data engineer, STX Next