Data forms the crux of business intelligence, and 2022 will be no exception to this rule. Python has emerged as the preferred tool for programming and data analytics. Additionally, the Python ETL framework supports data pipelines, thereby balancing numerous sub-sectors dedicated to data aggregation, wrangling, analytics, amongst others.

Knowing Python’s functionalities and its use in ETL facilitation, you can assimilate how it can ease a data analyst’s job.

What Is ETL?

ETL stands for Extract, Load, and Transform. It is a sequential process of extracting information from multiple data sources, transforming it as per requirements, and loading it into its final destination. These destinations can range from being a storage repository, BI tool, data warehouse, and many more.

The ETL pipeline gathers data from intra-business processes, external client systems, vendors, and many other connected data sources. The collected data is filtered, transformed, and converted into a legible format, before being used for analytics.

The Python ETL framework has long served as one of the best-suited languages for conducting complex mathematical and analytical programs.

Hence, it comes as no surprise that Python’s replete library and documentation are responsible for birthing some of the most efficient ETL tools in the market today.

The Best Python ETL Tools to Learn

The market is flooded with ETL tools, each of which offers a different set of functionalities to the end-user. However, the following list covers some of the best Python ETL tools to make your life easier and smoother.

1. Bubbles

Bubbles is a Python ETL framework used for processing data and maintaining the ETL pipeline. It treats the data processing pipeline as a directed graph that assists in data aggregation, filtration, auditing, comparisons, and conversion.

As a Python ETL tool, Bubbles allows you to make data more versatile, so it can be used for driving analytics in multiple departmental use cases.

Bubbles data framework treats data assets as objects, including CSV data to SQL objects, Python iterators, and even social media API objects. You can count on it to evolve as it learns about abstract, unknown datasets, and diverse data environments/technologies.

2. Metl

Metl or Mito-ETL is a fast-proliferating Python ETL development platform used to develop bespoke code components. These code components can range from RDBMS data integrations, Flat file data integrations, API/Service-based data integrations, and Pub/Sub (Queue-based) data integrations.

Metl makes it easier for non-technical members of your organization to create timely, Python-based, low-code solutions. This tool loads various data forms and generates stable solutions for multiple data logistics use cases.

3. Apache Spark

Apache Spark is an excellent ETL tool for Python-based automation for people and enterprises that work with streaming data. Growth in data volume is proportional to business scalability, making automation necessary and relentless with Spark ETL.

Managing startup-level data is easy; nevertheless, the process is monotonous, time-consuming, and prone to manual errors, especially when your business expands.

Spark facilitates instantaneous solutions for semi-structured JSON data from disparate sources as it converts data forms into SQL-compatible data. In conjunction with Snowflake data architecture, the Spark ETL pipeline works like a hand in glove.

4. Petl

Petl is a stream processing engine ideal for handling mixed quality data. This Python ETL tool helps data analysts with little to no prior coding experience quickly analyze datasets stored in CSV, XML, JSON, and many other data formats. You can sort, join, and aggregate transformations with minimal effort.

Unfortunately, Petl cannot help you with complex, categorical datasets. Nonetheless, it is one of the best Python-driven tools to structure and expedite ETL pipeline code components.

5. Riko

Riko is an apt replacement for Yahoo Pipes. It continues to be ideal for startups possessing low technological expertise.

It is a Python-crafted ETL pipeline library primarily designed to address unstructured data streams. Riko boasts of synchronous-asynchronous APIs, a tiny processor footprint, and RSS/Atom native support.

Riko permits teams to conduct operations in parallel execution. The platform’s stream processing engine helps you execute RSS feeds consisting of audio and blog texts. It’s even capable of parsing CSV/XML/JSON/HTML file datasets, which are an integral part of business intelligence.

6. Luigi

Luigi is a lightweight, well-functioning Python ETL framework tool that supports data visualization, CLI integration, data workflow management, ETL task success/failure monitoring, and dependency resolution.

This multi-faceted tool follows a straightforward task and target-based approach, where every target handholds your team through the next task and executes it automatically.

For an open-source ETL tool, Luigi efficiently handles complex data-driven problems. The tool finds endorsement from on-demand music service Spotify for aggregating and sharing weekly music playlist recommendations to users.

7. Airflow

Airflow has garnered a steady legion of patrons among enterprises and veteran data engineers as a data pipeline set-up and maintenance tool.

The Airflow WebUI helps schedule automation, manage workflows, and execute them through the inherent CLI. The open-source toolkit can help you automate data operations, organize your ETL pipelines for efficient orchestration, and manage them using Directed Acrylic Graphs (DAGs).

The premium tool is a free offering from the almighty Apache. It’s the best weapon in your arsenal for easy integration with your existing ETL framework.

8. Bonobo

Bonobo is an open-source, Python-based ETL pipeline deployment and data extraction tool. You can leverage its CLI to extract data from SQL, CSV, JSON, XML, and many other sources.

Bonobo tackles semi-structured data schemas. Its specialty lies in its use of Docker Containers for executing ETL jobs. However, its true USP lies in its SQLAlchemy extension and parallel data-source processing.

9. Pandas

Pandas is an ETL batch processing library with Python-written data structures and analysis tools.

Python’s Pandas expedite processing of unstructured/semi-structured data. The libraries are used for low-intensity ETL tasks including data cleansing and working with small structured datasets post-transformation from semi or unstructured sets.

Choosing the Best ETL Tools

There is no right one-size-fits-all-ETL tool. Individuals and businesses need to take their data quality, structure, time constraints, and skill availability into account before handpicking their tools.

Each of the tools listed above can go a long way in helping you meet your ETL goals.