Python is one of the most popular programming languages ââin the world. It often tops polls – for example, it won first place in the Programming Language Popularity Index and second in the TIOBE Index.
Python’s primary focus has never been web development. However, a few years ago software engineers realized the potential of Python for this particular purpose and the language saw a huge rise in popularity.
But data engineers couldn’t do their jobs without Python either. Since they are heavily dependent on the programming language, it is more important than ever 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
The common challenges that data engineers face are no different from those that data scientists face. The processing of data in its many forms is at the heart of the concerns of these two professions. From a data engineering perspective, however, our focus is more on industrial processes, such as Extract-Transform-Load (ETL) jobs and data pipelines. They must be solidly constructed, reliable and suitable for use.
The principle of serverless computing makes it possible to trigger data ETL processes on demand. Subsequently, the physical processing infrastructure can be shared by users. This will allow them to increase costs and therefore reduce management overheads to their bare minimum.
Python is supported by serverless IT services from leading platforms, including AWS Lambda Functions, Azure Functions, and GCP Cloud Functions.
Parallel computing is, in turn, necessary for the more âheavyâ ETL tasks related to big data issues. Splitting transformation workflows between multiple worker nodes is essentially the only feasible way in terms of memory and time to achieve the goal.
A Python wrapper for the Spark engine named “PySpark” is ideal because it is supported by AWS Elastic MapReduce (EMR), Dataproc for GCP, and HDInsight. When it comes to controlling and managing resources in the cloud, appropriate application programming interfaces (APIs) are laid out for each platform. Application programming interfaces (APIs) are used when triggering tasks or recovering data.
Python is therefore used on all cloud computing platforms. The language is useful when performing the job of a data engineer, which is setting up data pipelines with ETL jobs to retrieve data from various sources (ingest), process / aggregate (transform) and conclusively enable them to become available to end users. .
Use Python for data ingestion
Business data comes from a number of sources such as databases (SQL and noSQL), flat files (for example, CSV files), other files used by companies (for example, spreadsheets). calculation), external systems, web documents and APIs.
The wide acceptance of Python as a programming language results in a plethora of libraries and modules. A particularly fascinating library is Pandas. This is interesting given that it has the ability to allow reading of data in “DataFrames”. This can take place from a variety of different formats, such as CSV, TSV, JSON, XML, HTML, LaTeX, SQL, Microsoft, open spreadsheets, and other binary formats (which are the result of exports from different business systems).
Pandas is based on other scientific and computationally optimized packages, offering a rich programming interface with a wide range of functions needed 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.
Use PySpark for parallel computation
Apache Spark is an open source engine used to process large amounts of data that controls the principle of parallel computing in a very efficient and fault-tolerant manner. Although initially implemented in Scala and natively supporting this language, it is now an interface universally used in Python: PySpark supports the majority of Spark features including Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core. This makes the development of ETL tasks 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.
Additionally, users can link their Jupyter notebooks to support the development of distributed processing Python code, for example with EMR notebooks natively supported in AWS.
PySpark is a useful platform for reshaping and aggregating large groups of data. As a result, it facilitates consumption for potential end users, including business analysts for example.
Using Apache Airflow for scheduling tasks
By having renowned Python-based tools within on-premises systems, cloud providers are motivated to market them as âmanagedâ services that are therefore easy to set up and use.
This is, among other things, true for Amazon Managed Workflows for Apache Airflow, which launched in 2020 and makes it easier to use Airflow in some of the AWS areas (new at the time of writing). 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 track them with the Airflow user interface.
There are various substitutes for Airflow, for example the obvious choices of Prefect and Dagster. Both are Python-based data workflow orchestrators with user interface and can be used to build, run, and observe pipelines. They aim to address some of the concerns some users face when using Airflow.
Strive to achieve data engineering goals, with Python
Python is loved and appreciated in the software community for being intuitive and easy to use. Not only is the programming language innovative, it is also versatile and allows engineers to take their services to new heights. The popularity of Python continues to increase for engineers, and its support continues to grow. Simplicity at the heart of the language means that engineers will be able to overcome any obstacles along the way and finish jobs with a high standard.
Python has a large community of enthusiasts who work together to improve the language. This involves fixing bugs, for example, and thus regularly opens up new possibilities for data engineers.
Any team of engineers will operate in a fast paced collaborative environment to create products with team members from various backgrounds and roles. Python, with its simple makeup, allows developers to work more closely on projects with other professionals such as quantitative researchers, analysts, and data engineers.
Python is quickly establishing itself as one of the most accepted programming languages ââin the world. Its use for data engineering therefore cannot be underestimated.
Mika szczerbak is a data engineer, Next STX