Python is the most used programming language in the data integration space. And why not? It is easy to use, modern and has a large community and cutting-edge features.
However, one of the key reasons for its popularity is the tools it has to offer. Python has many tools acting as frameworks, libraries, and applications for streamlining the ETL (Extract, Load, Transform) process. These ETL tools allow you to use Python for different tasks, including workflow management, data transformation, and moving and processing data.
In this article, you will learn about some of the top Python ETL tools to look at for making the ETL process much easier.
Best Python ETL Tools
Here is a list of the top 6 tools to use while using Python for ETL:
Airflow is an open-source workflow management tool created using Python to programmatically author, schedule, and monitor workflows. Its highly customizable tool allows you to visualize your data pipelines and everything it include, like its dependencies, logs, trigger tasks, and success status. With Airflow, you can also create Directed Acyclic Graphs (DAGs). A DAG defined in a Python script is a collection of all the tasks you want to run in an organized manner within the platform.
Overall, Airflow is the best Python ETL tool if you want to manage complex ETL jobs and keep track of everything within the pipeline.
Key features of Airflow are:
- Airflow Operators: Airflow offers a vast library of pre-built operators. These operators are templates that can handle tasks such as data orchestration, transfer, cloud operations, and even SQL script execution.
- Scheduling: With Airflow, you can schedule your data pipeline workflows and determine their frequency and timing. It allows you to create schedules tailored to your needs using cron expressions, custom triggers, or intervals.
- Visualization: Airflow allows you to visualize your complex data pipeline workflows, which makes data more accessible to technical as well as non-technical users.
pETL (Python Extract, Transform, Load) is a Python package. It is a lightweight library designed for working with tabular data such as Excel spreadsheets, CSV files, and SQL databases. pETL provides many built-in functions for extracting, transforming, and loading data from disparate sources to destinations. These functions streamline the ETL process by streamlining most integration tasks by default without having to write custom code.
Here are some of the ETL functions of pETL:
- fromcsv(): This function extracts data from a CSV file and returns a table.
- fromjson(): This function extracts data from a JSON file and returns a table.
- fromxml(): It extracts data from an XML file and returns a table.
- fromdb(): This function does the same task of returning a table but from an SQL database.
- select(): With this function, you can filter rows from a table based on the condition you provide.
- cut(): This function selects specific columns from the table you provide.
- aggregate(): It performs aggregation tasks such as adding, counting, and averaging in one or more tables.
- join(): This function combines two or more tables based on common keys.
- tocsv(): Loads the provided table in a CSV file.
- tojson(): Loads the provided table in JSON file.
- toxml(): Loads the provided table in an XML file.
- todb(): Loads the provided table in the SQL database.
Pandas is one of the most popular open-source Python libraries for working with data sets. The tool provides built-in functions for analyzing, cleaning, exploring, and manipulating data. Pandas is built on top of two core Python libraries - NumPy for mathematical operations and matplotlib for data visualization. It acts as a wrapper for these libraries and allows you to access both library's methods and functions with less code.
Pandas are well-suited for several kinds of data, including tabular data, time series, arbitrary matrix data, or any other form of statistical or observational data sets. It offers a rich set of functionalities in all these data formats, including data indexing, filtering, aggregation, grouping, merging, joining, analysis, and visualization.
Pandas offers two data structures for tasks like transformation: Series and DataFrames. A series is like a single column of data where each value has an index. Below is an example:
While series are very useful, analysts mostly use DataFrames. DataFrames store data in familiar table formats within rows and columns, much like relational databases. Pandas make a lot of analytical tasks more accessible by providing in-built functions to work with DataFrames. These functions include finding averages per column, the sum of values in a column, and other analytical tasks.
With Pandas, you can implement all kinds of ETL practices, including data extraction, transformation, handling, cleaning, validating, data type conversion, and exporting.
PySpark is a Python API for Apache Spark. Apache Spark is a distributed processing system that performs machine learning and big data tasks on large datasets.
Using PySpark, you can write about Python and SQL-like commands to analyze and manipulate data in a distributed processing environment. It also provides a shell for interactively analyzing your data. PySpark combines Python's ease of use and adaptability with the power of Apache Spark to enable the analysis and processing of data at any size.
Organizations use a framework like PySpark mainly because of the efficiency of processing big data. It is faster than popular libraries like Pandas and Dask. Beyond its efficiency, PySpark supports all Spark features, such as Spark DataFrames, SQL, Structured Streaming, Machine Learning (MLlib), and Spark Core. Learn more about these features from here.
You just have to install Python and Apache Spark on your local machine to get this tool. Then, install the PySpark library just as you would any other library.
Great Expectation (GX) is an open-source Python library that enhances data quality and testing. The tool provides a robust framework for validating, documenting, and profiling your data. GX accomplishes high data quality by providing an easy and intuitive way to define and manage expectations, validate data against those expectations, and alert you to violations of expectations.
GX offers two tools: GX OSS and GX Cloud. The former is an open-source version of GX that you can install and set up within the data stack to fit your needs. However, GX Cloud is a cloud-based service that offers an easy way to use Great Expectations without having to manage infrastructure.
Some of the features of GX include:
- A huge library of predefined expectations allows you to easily define expectations for various data types, such as textual, numerical, and datetime data.
- Flexible and customizable expectation suites allow you to create your own expectations for applying with specific data sets.
- Support for various data sources and formats like Databricks and relational databases.
- The ability to integrate with an existing data pipeline for adding data validation and quality checks to existing workflows.
Airbyte + dbt: a Perfect ETL Setup
Regarding ETL with Python, there's no tool that fits every requirement you have in one place. However, combining Airbyte and dbt can give you a robust setup to make a powerful environment.
Airbyte is an ELT tool (not ETL) that follows a modern approach to data integration to synchronize multiple data sources and destinations. The platform automates the tasks of data ingestion using its extensive library of over 350+ pre-built connectors and intuitive user interface. In its extensive library, if you still haven't found your specific connector to automate synchronization, you can use its connector development kit to build a custom one. In addition, Airbyte allows you to create a particular data pipeline in your desired way. You can use UI, custom coding, and API to build pipelines.
While Airbyte streamlines the extraction and loading process, dbt (data build tool) takes care of your advanced data transformation needs.
dbt is a data transformation tool that you can use to turn raw data into a structured format to deliver it to downstream applications. The platform offers two tools for advanced data transformation: dbt cloud and dbt core. As the name suggests, the cloud is a cloud platform, and the core is a command line tool for managing transformation workflows. In both tools, you'll get support for Python to custom code your advanced data transformation logic.
Airbyte allows you to integrate with dbt to fulfill your data transformation needs. Using a hybrid approach and combining both tools for data integration, you can make a setup that efficiently serves all your data integration requirements.
In this article, you have learned the six best Python tools to perform ETL. Every tool has its use case and advantages. Airflow specializes in orchestration, pETL in basic ETL, Pandas in transformation, PySpark in data processing, GX in data quality, and Airbyte+dbt in data integration. You can use these tools to streamline your Python ETL process.
If you are looking for alternatives to custom coding in Python for data pipelines, we suggest using the ETL tool Airbyte. Airbyte takes care of all your scalability, automation, and efficiency needs without much effort. Join a vibrant community of developers today, and sign up or log in with Airbyte.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What is ETL?
ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.
What is ELT?
More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:
- Faster processing times and loading speed
- Better scalability at a lower cost
- Support of more data sources (including Cloud apps), and of unstructured data
- Ability to have no-code data pipelines
- More flexibility and autonomy for data analysts with lower maintenance
- Better data integrity and reliability, easier identification of data inconsistencies
- Support of many more automations, including automatic schema change migration
Here is our recommendation for the criteria to consider:
- Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
- Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
- Ability to build new connectors: all data integration solutions support a limited number of data sources.
- Support of change data capture: this is especially important for your databases.
- Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
- Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
- Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
- Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
- Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
- Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
- Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.
Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.
What's unique about Airbyte?
Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran?
Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Data
Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch?
Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.
Other potential services
Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.
What's unique about Matillion?
Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.
Here are more insights on the differentiations between Airbyte and Matillion.
Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow?
Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow.
Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend?
What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.
Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho?
What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
Microsoft SQL Server Integration Services (SSIS)
MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.
Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: