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Top 10 Python ETL Tools and Frameworks in 2022

Top 10 Python ETL Tools and Frameworks

Extract, Transform, and Load (ETL) tools are used by organizations to transfer, format, and store data between systems to help them obtain high-performance data. ETL is a vital part of an organization's data stack process. In fact, a good ETL tool can single-handedly define the data warehouse workflow. Python ETL frameworks help streamline the ETL development process and are the foundation for developing Python-written ETL software. They enable organizations to customize and control the pipeline and improve their data source.

This article lists the 10 best Python ETL tools and frameworks for 2022 along with a look at the types of ETL tools that exist and Python’s role in ETL.

What are ETL tools?

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Data is distributed across different applications. Organizations require a warehouse to store everything together and generate significant insights to grow their business. ETL refers to extracting data from different sources and transforming it into a readable and well-organized format.

Organizations use techniques like data normalization, integration, and aggregation with the help of ETL tools. The processed data is then loaded into a warehouse. This makes data management easy and improves data warehousing.

Types of ETL tools

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There are four types of ETL tools based on their supporting organizations and infrastructure: enterprise software, open-source, cloud-based, and custom tools.

Enterprise software ETL tools

Tools developed and supported by commercial enterprises are known as enterprise software ETL tools. They are mature and robust as enterprises were their first champions.

Enterprise software ETL tools offer GUIs that build ETL pipelines and support relational and non-relational databases. They also support user groups but can require extensive documentation. Despite their vast potential, such tools are expensive and demand more training and knowledge of integration services.

Open-source ETL tools

Open-source ETL tools are gaining ground over other options as the world evolves alongside the open-source movement. Free ETL tools are widely available for users which provide GUIs to design the data-sharing process and monitor the data flow.

With open-source ETL tools, organizations can easily access source codes to study the tools’ infrastructure. However, the tools have different functionalities, documentation, and usage requirements that might not support all organizational types.

Cloud-based ETL tools

Cloud computing and integration of cloud technologies are widely available. There are now special ETL tools based on cloud-based infrastructure. The biggest advantage of such tools is efficiency.

Cloud technologies provide availability, elasticity, and high latency allowing computing resources to scale to meet data processing demands. However, a disadvantage of cloud-based ETL tools is that they work only with a cloud service provider and don’t support data processing in other clouds or direct data from any centers without shifting it to the cloud.

Custom ETL tools

Organizations with developmental resources often produce customized ETL tools of their own. The main advantage of this approach is that it provides flexibility to build customized solutions depending on the organization’s workflow and priorities. Python, Java, and SQL are some of the programming languages used to build customized ETL tools.

The major drawback of this method is the huge amount of internal resources needed. Organizations also have to think about the training and documentation support they have to offer new users and developers as they join.

What are ETL frameworks?

A Python ETL framework is a basis for developing ETL software written in Python. It is a reusable collection of modules and packages that intends to standardize the application development process. It does this by providing a common development approach and functionality.

The ETL Python framework was created to help e-commerce businesses perform batch processing on bulk data quantities. With the help of these frameworks, any business can move its data to a target management system. It can run the data through business intelligence tools for thorough real-time insight into operations.

If users employ a top ETL framework, they can define, schedule, and execute data pipelines using Python. They can also perform data extraction, data transformation into correct formats, and ETL job execution without hassle.

What is Python’s role in ETL?

Python is an open-source programming language that can help us code ETL tools and frameworks. It is the best choice for developers who need to build tools from scratch. The language has specific business objectives, technical requirements, and libraries that are compatible with it.

Python is an easy-to-handle tool for indexing data structures and dictionaries that are vital for ETL operations. Using Python, developers can code and filter null values from their data using pre-built Python modules.

ETL tools can be developed with a combination of pure Python coding, externally defined functions, and libraries. Developers can easily create ETL tools since Python APIs, SDKs, and other resources are available to use. For instance, they can use the Pandas library to filter an entire data frame containing null values.

10 best Python ETL tools and frameworks for 2022

The following are the top 10 Python ETL tools and frameworks for 2022.

Luigi

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Luigi is an open-source Python ETL tool used to create more complex pipelines. It offers advantages like failure recovery via checkpoints, CLI, and visualization tools. It helps users state their dependencies differently. They can use the newly created target for different tasks when a task is complete. For instance, when an assigned task consumes a target, it is removed. As a result, the process and workflow become straightforward.

Luigi is the perfect solution for businesses that want to overcome ETL tasks like data logging. Note that not all businesses can interact with the different processes using Luigi. It doesn’t automatically synchronize tasks with workers. It also doesn’t offer scheduling, monitoring, or altering functions.

Apache Spark

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Apache Spark is a Python-based ETL framework-building tool that is in high demand by data scientists and ETL developers. With the help of the Spark API, they can perform the following functions:

  • Conduct data parallelism implicitly.
  • Continue to run ETL systems with Spark’s fault tolerance.
  • Analyze and transform existing data into formats like JSON.
  • Data processing tasks.

pETL

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Known as Python ETL, pETL is useful for processing, extracting, or loading data tables from source types like CSV or XML. It is a general-purpose programming language. The ETL functionality can flexibly apply transformations like joining, aggregating, and sorting data in tables.

Although it isn't possible to process categorical data with pETL, it should still be considered for establishing a simple ETL pipeline. Data can also be extracted from different sources.

Bubbles

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Bubbles is a Python ETL framework that enables users to process data and maintain the ETL pipeline. It treats the data processing pipeline as a directed graph which helps in data filtering, aggregation, comparisons, conversion, and auditing.

As a Python ETL tool, Bubbles allows businesses to make data versatile which helps them drive analytics in different types of use cases. The framework treats data assets like objects which include CSV data in Python Iterators, social media API objects, and SQL objects.

mETL

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Also known as Mito-ETL, mETL is a fast-growing Python ETL development platform that allows for building bespoke code components. These range from flat file data integration, RDBMS data integration, Pub/Sub data integration, and API/service-based data integration.

mETL makes it easy for non-technical people in organizations to create a Python-based, timely, and low code-requiring tool that loads different data forms and generates stable solutions for various data logistics use cases.

It is used by programmers and developers to load any type of data. They can then transform it using quick manipulations and transformations - without requiring high-level programming skills.

Bonobo

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Bonobo is a Python-based, lightweight, open-source ETL framework pipeline tool that helps with data extraction and deployment. The CLI can be used to extract data from CSV, XML, SQL, JSON, and other sources.

Bonobo tackles semi-structured data schemas. It is unique because it uses Docker containers to execute ETL jobs. However, its USP is in the parallel data-source processing and SQLAlchemy extension.

Pandas

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Pandas is an ETL batch-processing library with Python-based data analysis and structure tools. It can expedite the processing of semi-structured or unstructured data. Pandas works with small, structured datasets that are unstructured or semi-structured before the transformation.

Pandas is an accessible, high-performance, and convenient data library. Businesses use it for data wrangling and general data work that intersects with other processes. They do so by manually sharing a machine learning algorithm or prototyping it within a research group to set up automatic scripts. Pandas processes data on real-time interactive dashboards.

Riko

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Riko is an open-source stream processing engine that can process and analyze large amounts of unstructured data. It also has a CLI that supports the following:

  • RSS feeds to help users publish audio, blogs, and news headlines.
  • Parallel execution of data streams through asynchronous or synchronous APIs.
  • Parsing CSV/JSON/XML or HTML files.

Riko boasts asynchronous or synchronous APIs, has RSS/Atom native support, and a tiny processor footprint. It permits teams to conduct operations in parallel execution.

Skyvia

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Skyvia is a cloud-based data platform that helps with code-free backup, data integration, access, and management. It includes and supports an ETL solution for various data integration scenarios like CSV files, cloud data warehouses, cloud applications, and databases. It also has a cloud data backup tool, an OData server, and an online SQL client.

Its features are:

  • Helps advance mapping settings.
  • Can preserve source data relations in the target.
  • Is a commercial, cloud-based solution that offers free plans.
  • Has an automatic schedule for data integration.
  • Can import data without duplication.
  • Is a code-free, wizard-based integration configuration tool that doesn’t require technical knowledge.
  • Supports bi-directional synchronization.
  • Has predefined templates that are common for integration cases.

Hadoop

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Apache Hadoop is an ETL framework designed to support and process large datasets by distributing the computational load across various computer clusters. The Hadoop library is designed to detect and handle defects in the application and hardware layer.

Apache Hadoop gives high performance and resource availability if you combine the computing power of various machines. The YARN module helps the framework support job scheduling and cluster resource administration.

Wrapping up

There you have it: the top Python ETL tools and frameworks. Note that the tools chosen should be based on business requirements, budget, and time constraints. The ones listed here are largely open-source, which makes them easily accessible to everyone.

Author

  • Author

    Aswini R

    Aswini is an experienced technical content writer. She has a reputation for creating engaging, knowledge-rich content. An avid reader, she enjoys staying abreast of the latest tech trends.

Frequently Asked Questions

When compared to other programming languages, Python is the best for building customized ETL tools. However, in some cases, developers use different programming languages for data processing, loading, and ingestion.

Pandas is the Python library that provides analysis tools and data structures. It simplifies the ETL process by adding R-style data frames. However, one needs to know how to code in order to use it.

Yes, Python is faster than SSIS. Combining datasets into use cases will make ETL inefficient and in turn, will make the relational database connect the data.

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