Data orchestration coordinates the data workflows of your organization to ensure seamless integration, transformation, and movement of data across disparate systems or environments. It reduces your workload through automation, eliminates data silos, enhances scalability, and enables efficient error management. Thus, data orchestration plays a crucial role in data management for improved operational efficiency and insights.
In this guide, you will learn about 12 free and best open-source data orchestration tools that you can use in 2024 to streamline your data workflows.
What is Data Orchestration?
Data orchestration is the automated process of gathering, transforming, and consolidating siloed data from multiple sources for effective data analytics. It allows you to streamline data workflows, eliminate any discrepancies in your data, and implement an efficient data governance framework. This ensures the availability of reliable data for making informed business decisions.
There are various data orchestration tools that can help automate your data workflows effortlessly. Such tools can reduce your workload by coordinating the repetitive tasks performed during data movement and optimizing the flow of data according to your requirements.
Benefits of Using Data Orchestration Tools
Utilizing data orchestration tools can greatly optimize your data workflows. Some of its benefits include:
- Workflow Automation: Data orchestration automates various tasks within data workflows. This can save significant time and reduce the workload of resources, enabling you to focus on high-value tasks to optimize your organization's output.
- Eliminates Data Silos: Data orchestration aids in data integration and transformation. This results in the consolidation and standardization of your data, thereby eliminating data silos.
- Monitoring: Data orchestration tools can help you monitor the status and progress of your data workflows. By tracking workflows, you can identify and resolve issues quickly.
12 Open Source Data Orchestration Tools
Here is a list of the best open-source tools you can use to orchestrate your data:
1. Apache Airflow
Apache Airflow is a popular open-source data orchestration tool written in Python. It uses DAGs (Directed Acyclic Graphs) to schedule and automate data pipelines. A DAG is a collection of tasks that Airflow executes in a specific order.
Airflow's simple user interface makes it easy for you to visualize your pipelines in production, monitor their progress as they run, and troubleshoot any issues that may arise. It also supports scripting, enabling you to incorporate complex logic required to orchestrate data across various systems and environments. Furthermore, Airflow allows for scaling your data orchestration workflows in parallel through the use of executors like Kubernetes or Celery.
2. Dagster
Dagster is a robust open-source data orchestration tool inspired by Apache Airflow. It is designed to facilitate the development and management of data pipelines. Dagster offers functionalities for monitoring tasks, debugging runs, inspecting data assets, and launching backfills.
The dependencies and execution parameters in Dagster allow you to handle complex workflows. To simplify orchestration further, Dagster introduced the concept of software-defined assets. It is a declarative approach where assets are defined in code, detailing their expected functionalities and typically referencing objects or files in persistent storage.
Dagster's software-defined assets enable the transformation of DAGs into interconnected assets with dependencies; you can define relationships between assets for clear orchestrations. Through this approach, Dagster enables you to define and run data pipelines using the Dagster command-line interface or the Dagster Dagit web interface.
3. Luigi
Luigi is a Python-based data orchestration tool developed by Spotify. It helps you build data pipelines by managing task dependencies, offering visualization, and ensuring robust workflow execution and failure handling. Luigi also supports command-line integration and provides a web interface to visualize your data pipelines effectively.
With Luigi, you can seamlessly integrate various tasks, like Hadoop jobs, Hive queries, or even local data processing tasks. Luigi utilizes dependency graphs to ensure that tasks are executed sequentially based on their interdependencies, which can include recursive task references. Additionally, Luigi offers A/B test analysis, the creation of internal dashboards, generating recommendations, and producing external reports to manage complex tasks.
4. Prefect
Prefect is one of the popular open-source data pipeline orchestration tools that uses Python to automate the construction, monitoring, and management of data pipelines. It facilitates complex data workflow with features such as scheduling, caching, retries, logging, event-based orchestration, and observability.
Prefect’s architecture consists of two components: the execution layer and the orchestration layer. The execution layer operates tasks with agents and flows. Agents are polling services that check for scheduled tasks and execute flow runs accordingly. Flows are defined functions that set up the tasks for execution, allowing you to specify task dependencies and triggers. On the other hand, the orchestration layer manages and monitors data workflows. It uses the Orion API server, a set of REST API services, and Prefect’s UI to execute operations efficiently.
5. Kestra
Kestra is an open-source data orchestration platform for creating and managing complex data flows. Its user-friendly interface facilitates building tailored workflows without the need for programming languages.
Kestra utilizes YAML, a descriptive language, to outline the sequence of tasks and their dependencies. This approach ensures that workflows are easy to understand and maintain.
6. Argo
Argo is an open-source container-native data orchestration tool. It runs on Kubernetes, a container orchestration system for automating software deployment, scaling, and management.
The working of Argo is based on Kubernetes Custom Resource Definition (CRD), which defines Kubernetes workflows using separate containers for each step. It also enables you to model workflows with DAGs. Using DAGs, you can capture dependencies between multiple tasks and define execution sequences.
7. Flyte
Flyte is an open-source data orchestration tool for building robust and reusable data pipelines. It supports built-in multitenancy, which allows decentralized pipeline development without affecting the rest of the platform. This feature enhances scalability, collaboration, and efficient pipeline management.
One of Flyte's key features is its versioning capability, which allows you to experiment with data pipelines within a centralized infrastructure. This eliminates the need for complex workarounds typically associated with version control in pipeline development. It also provides you with user-defined parameters, caching, data lineage tracking, and ML orchestration capabilities to enhance workflow development.
8. Mage
Mage is one of the best open-source data orchestration tools known for its unique hybrid framework. This simplifies building data pipelines for data integration and transformation tasks. Mage allows you to integrate and synchronize data from third-party sources seamlessly; you can build both real-time and batch pipelines using Python, SQL, and R.
The interactive notebook UI of Mage provides instant feedback on the output of your code. The files containing code in Mage are called blocks, which can be executed independently or in the pipeline. Each block of code produces a “data product” when executed. Blocks combine together to form DAGs. A pipeline in Mage is a collection of these blocks of code and charts for visualization.
9. Shipyard
Shipyard is a data orchestration tool known for its user-friendly features that simplify data sharing. It offers functionalities like on-demand triggers, automatic scheduling, and built-in notifications to automate data workflow execution. The platform has a visual interface that allows you to build data workflows, eliminating the need to write extensive code for basic workflows.
Shipyard has built-in observability for monitoring your workflows, along with retry functionalities for failed tasks, and alerting features for quick notification of any issues. Additionally, Shipyard also offers automated scaling to handle fluctuating workloads and end-to-end encryption capabilities for secure data processing.
10. Apache Nifi
Apache NiFi is an open-source data orchestration tool known for its user-friendly interface. This interface allows you to simplify the process of designing data flows, making it particularly suitable for scenarios involving diverse data sources.
NiFi enables data routing, transformation, and system mediation logic within workflows. These functionalities allow you to develop and deploy data pipelines that automate data movement between different systems. Additionally, NiFi also supports various data protocols, allowing you to work with different data sources. Due to this, Apache Nifi has become a go-to data choice for data orchestration tasks.
11. MLRun
MLRun is an open-source data orchestration tool used to manage machine learning-based data pipelines or workflows. Built on a Python-based framework, MLRun enables complete workflow management, allowing you to orchestrate a massive amount of data through its elastic scaling features.
It also enables you to track, automate, and deploy pipelines, which can access and integrate data stored across various repositories. Thus, MLRun significantly reduces the time, resources, and workload related to the deployment of machine learning-based pipelines.
12. Metaflow
Metaflow is an open-source data orchestration tool developed at Netflix. It is designed to build and manage complex data workflows. MetaFlow uses a dataflow programming paradigm and represents programs as directed graphs termed “flows.” These flows consist of operations or steps, which can be organized in multiple ways, including linear sequences, branches, and dynamic iterations.
A key feature of Metaflow is its handling of “artifacts,” or data objects created during the execution of a flow. Artifacts simplify data management, ensuring automatic persistence and availability of data across the different steps of the workflow.
Streamline Data Workflows With Airbyte
Data orchestration becomes more efficient when data resides in a centralized location. Consolidating data from multiple sources, or data integration, plays a crucial role in achieving this. Consider using Airbyte, a cloud-based data integration platform, for effortless data integration.
Airbyte offers a rich library of 350+ pre-built connectors to automate your data pipeline creation. And, if you cannot find a connector of your choice, you can build customized ones using CDK. These connectors play an important role in data integration by establishing a seamless connection between your data sources and your target system.
Some of the ways in which Airbyte can aid data orchestration include:
- Seamless Data Ingestion: Airbyte provides an easy-to-use interface for building data pipelines. This simplifies data ingestion from various sources without requiring extensive coding expertise.
- Change Data Capture: Airbyte's CDC feature allows you to leverage data replication capabilities. It allows you to identify and capture incremental changes in your source data and replicate them in the target system.
- PyAirbyte: With Airbyte, you can take advantage of its recently launched open-source Python library, PyAirbyte. It allows you to extract data from Airbyte-supported connectors programmatically.
- Open-source: As an open-source platform, Airbyte supports a large community of data practitioners and developers. These individuals actively engage and collaborate with others within the community to build and extend Airbyte features.
Conclusion
Data orchestration is an essential approach that can assist you in managing and optimizing your data pipelines. This article provides you with a comprehensive list of the 12 best data orchestration tools for coordinating your data workflows. You can use these tools to build complex data pipelines that facilitate seamless data integration, transformation, and analysis. As open-source solutions, they offer the benefit of community-driven innovation, ensuring you benefit from continuous updates and various customization options.
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Frequently Asked Questions
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.
The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.