Python is one of the most popular programming languages worldwide. It often ranks high in surveys: for instance, it claimed the first spot in the Popularity of Programming Language index and came second in the TIOBE index. The chief focus of Python was never web development. However, a few years ago, software engineers realized the potential Python held for this particular purpose and the language experienced a massive surge in popularity. But data engineers couldn’t do their job without Python, either.

Since they have a heavy reliance on the programming language, it’s as important now as ever to discuss how using Python can make data engineers’ workload more manageable and efficient. 

Cloud platform providers use Python for implementing and controlling their services

Run-of-the-mill challenges that face data engineers are not dissimilar to the ones that data scientists experience. Processing data in its many forms is a key focus of attention for both of these professions. However, from the data engineering perspective, we concentrate more on the industrial processes, such as ETL (extract-transform-load) jobs and data pipelines. They have to be strongly built, dependable, and fit for use. 

The serverless computing principle allows for triggering data ETL processes on demand. Thereafter, physical processing infrastructure can be shared by the users. This will allow them to enhance the costs and consequently, reduce the management overhead to its bare minimum.

Python is supported by the serverless computing services of prominent platforms, including AWS Lambda Functions, Azure Functions, and GCP Cloud Functions.

Parallel computing is, in turn, needed for the more ‘heavy duty’ ETL tasks relating to issues concerning big data. Splitting the transformation workflows among multiple worker nodes is essentially the only feasible way memory-wise and time-wise to accomplish the goal.

A Python wrapper for the Spark engine named ‘PySpark’ is ideal as it is supported by AWS Elastic MapReduce (EMR), Dataproc for GCP, and HDInsight. As far as controlling and managing the resources in the cloud is concerned, appropriate Application Programming Interfaces (APIs) are exposed for each platform. Application Programming Interfaces (APIs) are used when carrying out job triggering or data retrieval. 

Python is consequently used across all cloud computing platforms. The language is useful when performing a data engineer’s job to set up data pipelines along with ETL jobs to recover data from various sources (ingestion), process/aggregate them (transformation), and conclusively allow them to become available for end-users.

Using Python for data ingestion 

Business data originates from a number of sources such as databases (both SQL and noSQL), flat files (for example, CSVs), other files used by companies (for example, spreadsheets), external systems, web documents, and APIs.

The wide acceptance of Python as a programming language results in a wealth of libraries and modules. One particularly fascinating library is Pandas. This is interesting considering it has the ability to enable the reading of data into “DataFrames”. This can take place from a variety of different formats, such as CSVs, TSVs, JSON, XML, HTML, LaTeX, SQL, Microsoft, open spreadsheets, and other binary formats (that are results of different business systems exports).

Pandas is based on other scientific and calculationally optimized packages, offering a rich programming interface with a huge panel of functions necessary to process and transform data reliably and efficiently. AWS Labs maintains an aws-data-wrangler library named “Pandas on AWS” used to maintain well-known DataFrame operations on AWS. 

Using PySpark for Parallel computing 

Apache Spark is an open-source engine used to process large quantities of data that controls the parallel computing principle in a highly efficient and fault-tolerant fashion. Whilst initially implemented in Scala and natively supporting this language, it is now a universally used interface in Python: PySpark supports a majority of Spark’s features, this includes Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core. This makes developing ETL jobs easier for Pandas experts.

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

Moreover, users are able to link their Jupyter Notebook to accompany the development of the distributed processing 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 consume for eventual end-users, including business analysts, for example.

Using Apache Airflow for job scheduling 

By having renowned Python-based tools within on-premise systems, cloud providers are motivated to commercialize them in the form of “managed” services that are, therefore, simple to set up and use.

This is, among others, true for Amazon’s Managed Workflows for Apache Airflow, which was launched in 2020 and facilitated using Airflow in some of the AWS zones (nine at the time of writing). Cloud Composer is a GCP alternative for a 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 keeps track of them with the Airflow user interface.

There are various substitutes for Airflow, for instance, the obvious choices of Prefect and Dagster. Both of which are python-based data workflow orchestrators with UI and can be used to construct, run, and observe the pipelines. They aim to address some of the concerns that some users face when using Airflow.

Strive to reach data engineers’ goals with Python

Python is valued and appreciated in the software community for being intuitive and easy to use. Not only is the programming language innovative, but it is also versatile, and it allows engineers to elevate their services to new heights. Python’s popularity continues to be on the rise for engineers, and the support for it is ever-growing. The simplicity at the heart of the language means engineers will be able to overcome any obstacles along the way and complete jobs to a high standard. 

Python has a prominent community of enthusiasts that work together to better the language. This involves fixing bugs, for instance, and thereby opens up new possibilities for data engineers on a regular basis. 

Any engineering team will operate in a fast-paced, collaborative environment to create products with team members from various backgrounds and roles. Python, with its simple composition, allows developers to work closely on projects with other professionals such as quantitative researchers, analysts, and data engineers.

Python is quickly rising to the forefront as one of the most accepted programming languages in the world. Its use for data engineering, therefore, cannot be underestimated. 

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