Using Python for Data Engineering

Using Python for Data Engineering

Using Python for Data Engineering

Python is one of the most popular programming languages ​​around the world. It often ranks well in industry programming popularity surveys, having recently been ranked #1 in both the Programming Language Popularity Index (PYPL) and TIOBE.

Python’s main focus has never been on web development. However, the potential in Python for this very purpose was realized a few years ago by software engineers and the language has seen a massive increase in popularity.

But data engineers couldn’t do their work without Python either, so it’s important to appreciate how it can be used to make your workload more intuitive and efficient.

Cloud platform providers use Python to implement and control services

The normal challenges faced by data engineers are not different from those faced by data scientists, as processing data in its many forms is a major focus of both. However, from a data engineering perspective, there is a greater focus on industrial processes, such as ETL (Extract and Transform Load) functions and data pipelines. It must be robust, reliable and usable.

The principle of serverless computing allows ETL operations to run on-demand data, after which the physical processing infrastructure can be shared by users. This allows them to increase costs, thereby reducing administrative expenses to a minimum. Python is powered by serverless computing services for prominent platforms, including AWS Lambda functionality, Azure functionality, and GCP cloud functionality.

Parallel computing, in turn, is essential for “tedious” ETL tasks related to issues related to big data. Splitting the conversion workflow among multiple working nodes is the only feasible way in terms of memory and time to achieve the goal.

The Python wrapper for the Spark engine called ‘PySpark’ is perfect because it is powered by AWS Elastic MapReduce (EMR), Dataproc for GCP, and HDInsight. As far as controlling and managing resources in the cloud, the appropriate application programming interfaces (APIs) are disclosed for each platform. APIs are used when performing job operation or data retrieval.

Thus Python is used in all cloud computing platforms. The language is useful when performing the job of a data engineer, which is to set up data pipelines along with ETL functions to retrieve data from different sources (ingest), process/collect it (transform) and categorically allow it to become available to end users.

Using Python to ingest data

Business data originates from a number of sources including databases (both SQL and noSQL), static files (such as CSV files), and many other file formats used by organizations including spreadsheets, external systems, web documents, and APIs.

The wide acceptance of Python as a programming language has resulted in a wealth of libraries and modules, including the particularly impressive Pandas library. Pandas are interesting because they have the ability to enable reading of data in “DataFrames”. This can happen from a variety of formats, such as CSVs, TSVs, JSON, XML, HTML, LaTeX, SQL, Microsoft, Open Spreadsheets, and other binary formats that result from exports to different business systems.

Pandas is based on other optimized scientific and computational packages, providing a rich programming interface with a huge palette of functions necessary to process and transform data reliably and efficiently. AWS Labs maintains an aws-data-wrangler library called “Pandas on AWS” that is used to maintain known DataFrame processes on AWS.

Using PySpark for parallel computing

Apache Spark is an open source engine used to process large amounts of data that governs the principle of parallel computing in a highly efficient and error-tolerant manner. While it was initially implemented in Scala and originally supported for this language, it is now a globally used interface in Python – PySpark.

PySpark supports the majority of Spark features – including Spark SQL, DataFrame, Streaming, MLlib (machine learning), and Spark Core – making it easier for Pandas experts to develop ETL functionality.

All of the cloud computing platforms mentioned above can be used with PySpark: Elastic MapReduce (EMR), Dataproc, and HDInsight for AWS, GCP, and Azure, respectively.

Additionally, users can link their Jupyter Notebook to accompany the development of Python code for distributed processing, for example, with EMR laptops supported natively in AWS.

PySpark is a useful platform for reconfiguring and aggregating large sets of data, making it easier for them to be consumed by end users, including business analysts.

Use Apache Airflow to schedule jobs

By having popular Python-based tools inside on-premises systems, cloud providers are incentivized to market them as “managed” services that are easy to set up and use.

This applies to (among other things) Amazon managed workflows for Apache Airflow, which were launched in 2020 and facilitate the use of Airflow in some AWS regions (nine at the time of writing). Cloud Composer is a GCP alternative for managed airflow service.

Apache Airflow is a Python-based, open source workflow management tool. It allows users to programmatically author and schedule workflow processing sequences, and subsequently track them through the Airflow user interface.

There are many alternatives to Airflow, for example, the obvious choices for Perver and Dagster. Both are Python-based data workflow organizers with a user interface and can be used to build, run, and monitor pipelines. It aims to address some of the concerns some users have when using Airflow.

Strive to reach data engineering goals with Python

Valued in the software community for being intuitive and easy to use, Python is not only innovative but also versatile, allowing engineers to take their services to the next level. The simplicity at the heart of the language means that engineers are able to overcome obstacles as they arise.

Powered by a passionate community working together to improve the language, Python’s simple configuration allows developers to collaborate on projects with quantitative researchers, analysts, and data engineers, and they’ll see it remain one of the most accepted programming languages ​​in the world.

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