Whether you’re working on a new data science algorithm or building a data analytics dashboard, you’ll need a data orchestration tool to prepare the datasets.
But how to pick the right data orchestration tool for your organization?
Read on to find out what the best data orchestration tools have in common, the must-have and must-avoid features, as well as the best-in-class data orchestration tools in 2023:
Keboola
AWS Step Functions
Apache Airflow
Dagster
Azure Data Factory
Google Cloud Functions
Prefect
Before we discover the pros, cons, user satisfaction, and expected costs of each tool, let’s clarify what to expect from a data pipeline orchestration tool.
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Data orchestration tools help you gather data from its data sources, clean and aggregate data, and finally send the data to a data destination such as a data warehouse or a BI tool where it’s ready for data analysis.
Also known as “data pipeline” or “workflow orchestration” tools, they are essential for:
Automation: Declare a data pipeline once, and let the tool run the workflow as orchestrated.
Monitoring: Keep track of your workflows and identify any issues in real time.
Scalability: Easily scale your operations through parallel processing and reusable pipelines.
But what is the difference between data pipeline orchestration and other tools in the modern data stack?
What is the difference between an ETL tool and a data orchestration tool?
ETL tools
Mainly focus on Extracting, Transforming, and Loading data.
Often have a fixed order of data processing (Extract -> Transform -> Load).
Might restrict customization in the data pipeline design.
Various options in designing data pipelines; you can perform EL, ELT, or the classic ETL.
Flexible in terms of the order of data processing
Let’s now explore the best data orchestration tools on the market.
1. Keboola
Keboola is a fully-managed end-to-end data platform as a service. Keboola stands out as not just a data orchestration tool, but a comprehensive, fully-managed end-to-end data platform as a service. It integrates ELT, Storage, Orchestration, and Analysis into one platform.
Pros:
Extensive integration coverage. Keboola offers 250+ pre-built connectors that automate data extraction and loading with a couple of clicks. Even if a popular data source or destination isn’t covered, you can use the Generic Extractor or Generic Writer to build data pipelines yourself.
Customizable orchestration triggers. Keboola’s orchestration engine can be triggered with a DateTime (similar to cron scheduling), manually, programmatically (CLI), or based on events. This gives you a wide range of options for orchestrating your data workflows on business logic or in real time.
Low-code and no-code features. You can interact with Keboola as a data engineer or business expert. Developers can pick from many programming languages (Python, R, Julia, SQL) and backends, while no-coding experts can build data pipeline orchestrations using no-code features such as the Visual Flow Builder or the no-code transformations.
Visual UI or API. Keboola offers many access points. Choose between its visual and intuitive UI or interact programmatically via its API.
Multi-cloud and on-premise. Keboola takes care of the infrastructure so you can focus on building data orchestrations rather than maintenance. Its offerings include fallbacks, dynamically scaled backends, and parallelization.
Features beyond orchestration:
Bring Your Own Data Stack. Keboola integrates with the tools of your choice. So you can plug and play instead of rip and replace your existing architecture.
Observability. Out-of-the-box monitoring and data lineage for every CRUD operation on the platform.
Metadata and artifact store layer. Increased traceability, transparency, and compliance through recorded touchpoints readily accessible to Keboola admins.
Unified user management. The single platform allows you to provision access for all your users and fortify the security while lowering the management overhead of all your tools.
Data science workbox. Put your data to good use by integrating it directly with data science features such as the Jupyter Notebook or advanced AI connectors and APIs.
Data productization. The separate sandbox environment and Streamlit integrations streamline the prototyping and productization of your data into data apps.
Cons:
Error messages are sometimes hard to understand. Luckily, Keboola is developing a new AI solution that will turn error messages into a human-readable format out of the box. It’s already in beta testing. Just login into Keboola and turn it on in your project setting.
Pricing:
Freemium model: You get 60 free compute minutes every month to run all your pipelines (yes, even production ones). After the free quota, you’re charged only for what you use at $0.14 per minute.
AWS Step Functions are a visual workflow system that helps you define and run no-code data pipelines within the AWS ecosystem.
Pros:
Easily scalable. AWS Step Functions are designed to be easily parallelizable, allowing you to process large datasets without detriment to workflow performance.
Event-drive data streaming. AWS Step Functions allows you to declare and run resilient workflows that can stream data in real-time.
No-code. The simple drag-and-drop visual builder helps your non-technical domain experts build data pipelines along your data engineers.
Cons:
Limited to AWS. While AWS Step Functions support over 200 services, they are confined to the AWS ecosystem. This poses a limitation for environments utilizing multiple clouds, hybrid cloud and on-premise, or exclusively on-premise deployments.
Complex and unclear design options. AWS Step Functions are powerful and flexible. Which introduces a lot of confusion as to what is the best way to build them. For example, which AWS storage to use for which function to optimize performance and costs?
Parallelization drawbacks. When you parallelize multiple steps within the same chunk of flow, a failure in one step results in the failure of all.
Pricing:
Pricing is hard to ballpark because it’s based on multiple factors:
Compute region
Workflow type: standard vs express
Number of workflow requests (including test workflows)
Number of state transitions per workflow (aka each step)
Apache Airflow is a Python-based open-source data orchestration tool that allows data teams to schedule and automate data workflows with DAGs (Directed Acyclic Graphs). Data engineers and data scientists use Apache Airflow for multiple use cases: from orchestrating ETL data pipelines to launching machine learning apps.
Pros:
Observability. Airflow’s graphical user interface offers intuitive and real-time monitoring of data flows.
Flexible transformations. You can deploy powerful transformations by relying on Python’s flexibility and associated libraries (for example, you can run big data processes with Spark).
Scalable architectures. Airflow supports multi-node orchestrations through the Kubernetes or Celery executor, allowing you to scale your data orchestrations in parallel.
Complex orchestrations. The DAG architecture allows you to handle complex orchestration scenarios such as dependencies between data pipelines.
Cons:
Confusing and limiting scheduler. Airflow allows only datetimes and manual triggers as an orchestration start. The date scheduler prevents triggering the same data orchestration twice. You’ll have to double your tasks to mimic the logic of a repeating job. Hence, it’s not great for architectures relying on microservices.
Low data governance. Airflow doesn’t preserve the data workflow’s metadata after you delete a job, making debugging hard.
Deployment challenges. You’ll need some DevOps skills to set up your Airflow instance and get it running. For example, Airflow doesn’t run natively on Windows, you’ll have to deploy it via a Docker image.
Limited to Python. Airflow is primarily a Python-based tool. Some of its connectors allow you to deploy other technologies (e.g. Hadoop or SQL operators), but writing workflows depends solely on Python. If you need more scalable languages or technologies, Python will be a limiting factor.
Pricing:
Airflow is an open-source data orchestration tool, so no upfront costs, except for management overhead: the server expenses to run Apache Airflow with its compute runtime and memory as well as the DevOps talent to set up, run, and maintain the Apache Airflow instance.
Alternatively, you can pay for a cloud-based managed Airflow instance. Find it on Google Cloud Platform via Cloud Composer, AWS under Amazon Managed Workflows for Apache Airflow (MWAA), and Microsoft Azure with Docker/Kubernetes deployments. Prices vary between cloud vendors.
Dagster is an open-source, cloud-based data orchestration platform that focuses on complex data pipelines. It’s geared toward difficult data processing requirements, where the data sources are hard to consume and/or transform.
Pros:
Out-of-the-box data lineage and provenance.
Excellent testing architecture. Dagster separates IO and resources from the data pipeline definition logic, making the data orchestration platform much easier to debug than for example Airflow. Additionally, with different environments, you can test data workflows locally before pushing them to production. Making it easier to debug, code review, and assert data quality in general.
Automated backfills. Especially when working with time series data, Dagster enables automatic backfill of any missing data.
Advanced dependencies control. You can specify data pipelines in terms of data asset dependencies such as files, tables, and machine learning models.
Cons:
Steep learning curve. Dagster’s ecosystem is technologically opinionated. Reserve some time for the steep learning curve needed to master it.
Code-heavy. Dagster’s pipelines (called ops) are defined as functions within a graph model. This isn’t just cognitively hard for data engineers used to thinking in less abstract terms, but also involves a lot of code design and overall architectural considerations. Dagster isn’t suitable for an easy-to-write solution.
Limited integrations. Dagster is designed with the data engineer in mind. So it’s mostly integrated with data engineering data sources (GitHub, PagerDuty, PostgreSQL, GCP, …). If you’re looking for a tool to cover all your data integration needs (not just orchestration), Dagster might not be the right choice.
Pricing:
A convoluted pricing model that depends on the infrastructure on which you run Dagster. Dagster’s price changes based on infrastructure, but you still have to pay for the infrastructure yourself.
You’re charged based on the compute minute, but the price of the compute minute decreases as the scale of your operations increases.
G2 reviews:
No reviews across the most popular marketplaces with business user reviews.
5. Azure Data Factory
Azure Data Factory is a fully managed, serverless data orchestration service. They offer 90+ connectors for building ETL, ELT, or ad hoc data pipelines.
Pros:
No-code. Azure Data Factory’s intuitive drag-and-drop UI empowers domain experts to build their own data orchestration without writing any code.
Scalable. Azure Data Factory uses cloud technologies to scale seamlessly and virtually limitlessly.
Cons:
Limited data integrations. The data sources and destinations covered by Azure Data Factory are limited and biased toward Microsoft technologies. Making it harder to use Azure Data Factory as your go-to data orchestration tool if you are a shop using multi-cloud data stacks or data sources not covered by Azure Data Factory.
Click heavy. In the tradition of Microsoft technologies, Azure Data Factory is very powerful, but also click-heavy. Unlike the other tools on this list, configuring simple data pipelines will require you to click a lot around their UI.
Steep learning curve. Although Microsoft provides solid documentation for the basic use cases, many more complex use cases aren’t clearly documented. Expect some time learning the platform before it can unlock its full potential.
Pricing:
The pricing model is convoluted and hard to estimate because it depends on the integration server at runtime and the specific operation performed.
Azure Data Factory charges separately the orchestrations from the data movement, pipeline activity, or even external pipeline activity that are linked to the runtime.
Google Cloud Functions is a serverless data orchestration service. You can write a functional definition of a data pipeline job and execute it via the GCP console or its web user interface.
Pros:
Many runtimes. Google Cloud Functions can run on Node.js, Python, Go, Java, .NET, Ruby, or PHP.
Easy to monitor and debug. Cloud Functions are designed for small, atomic, single-purpose operations. With the GCP logs and alerts, they are extremely easy to monitor and debug.
Real-time and scalable. Cloud Functions use GCP infrastructure so they are easy to parallelize and scale. With events (hook triggers) as inputs, they also offer data streaming.
Cons:
No no-code features. Cloud Functions are primarily aimed at developers, so there is no visual drag-and-drop no-code UI to declare Cloud Functions.
Hard to develop complex data pipelines. Cloud Functions are great for small tasks, but chaining them together into a complex data pipeline with multiple components makes it hard. There is no visualization to assist you in the integration of multiple Cloud Functions into a single end-to-end data workflow.
Pricing:
Cloud Functions offers 2 million free invocations monthly.
Afterward, you pay $0.40 per million invocations, making them extremely affordable.
Additional charges apply for compute runtime, disk, and networking costs, but even these are discounted or low-priced. Check GCP’s pricing calculator for details.
Prefect is an open-source data flow automation platform that allows you to orchestrate and run your data pipelines across different systems including data warehouses, data lakes, and machine learning models.
Pros:
Parametrization. Prefects parametrization features allow you to construct dynamic workflows.
Scalability. The data flow platform can scale through parallelized execution with Kubernetes.
Real-time orchestration. Prefect exposes events as triggers for your data workflow orchestrations, allowing you to build real-time data flows.
UI or API. Data workflows can be orchestrated either via their user interface or via the API, Giving you a wider range of deployment options.
Cons:
Python only. In Prefect, you declare and design all data flows using Python. No other programming languages are available. This can cause performance issues, especially with memory-intense tasks.
No low-code solution. Prefect doesn’t offer features for the non-coders. This leaves a huge chunk of your talent out the door when it comes to data pipeline orchestrations.
Poor observability. The open-source version (versus their paid, Perfect Cloud version) has limited to no observability. Even the UI that allows you to inspect and view data flows is more poorly constructed than alternatives like Airflow.
Limited documentation. Prefect’s documentation is very sparse and doesn’t cover all its features and use cases, which can results in a steep learning curve and additional time spent debugging.
Pricing:
Prefect offers two pricing tears: the free open-source edition with limited features or the paid Prefect Cloud version.
The most affordable minimally workable version of Prefect Cloud will cost you $450/month.
With so many good options, which one should you pick?
How to choose the right data orchestration platform?
Follow these five criteria to pick out the best data orchestration platform for your company:
Integration coverage. Pick a data orchestration platform that covers all the data sources and data destinations you’re currently using. However, also prioritize tools that offer more integrations as this ensures adaptability for future requirements.
Ease of use. Intuitive data orchestration tools will speed up your implementation process and time to market. Look for low-code features that streamline the work of your data engineers, no–code features that help your non-coding experts build data orchestrations, and multi-cloud/cloud-agnostic features to surpass the limits of any deployment option.
Transparent and affordable pricing. The total cost of ownership (TCO) starts with knowing how much a solution will cost you. Prioritize data orchestration tools that are affordable but also transparent about their pricing, helping you to assess the TCO.
Customer satisfaction. Check the reviews for each shortlisted platform. If other data professionals like the product, there is a higher chance you’ll love it too.
The X(tra) factor. Consider any additional benefits that the data orchestration tool might offer, such as integrated data science toolboxes, observability out of the box, user management, and more.
Orchestrate your data with Keboola
Keboola is designed to streamline all your data orchestration tasks. Offering over 250 pre-built connectors, coupled with low-code and no-code functionalities, customizable triggers, and accessibility through both UI and API, Keboola automates every aspect of data orchestration.
Rely on Keboola to also get extra perks: managed and scalable infrastructure, security, user management, observability, and productization features.
What is the difference between a data integration tool and a data orchestration tool?
Data ingestion tools have traditionally covered just the loading of data from data sources to the data warehouse. Leaving out the transformation layer. While data orchestration tools cover all the transformation steps as well.
Similarly to workflow orchestration tools, data ingestion tools traditionally offer metadata and other metrics that help you run data management and data governance over your data operations.
Looking for the best data ingestion solutions on the market? Check our shortlist of the best data integration tools to get inspired.
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