Big data isn't beneficial if it can't go anywhere. This is why data orchestration is of vital importance to businesses of all sizes. That said, no two businesses have access to the same resources—depth of budgets and talent pools vary dramatically.
This is why choosing the right data orchestration tool is critical for smaller businesses and startups. The right choice can provide security, stability, and the ability for team members to focus on what's most important. The wrong choice can turn operations into an expensive and, frankly, scary mess.
So here are 10 different data orchestration tools to consider using in your own business.
1. Shipyard
As we shove off into this list of data orchestration tools, it makes sense to cruise past Shipyard first. Shipyard acts as a command center for data operations, making data orchestration easier as our platform connects the modern data stack from end to end on day one. It's also built for people of all technical backgrounds, offering more than 150 low-code templates.
This ability to streamline pipeline orchestration is a big deal for big data, as it allows data people to quickly launch, monitor, and share resilient data workflows at record speeds. This also keeps small teams from getting bogged down in server and software maintenance and focused on keeping vital data moving without needing engineering or DevOps.
Top features:
- Rapid launching: Shipyard allows you to run SQP, Python, and Node.js right out of the box and offers built-in functionality for processing environmental variables, package installation, and more. You can also deploy Shipyard in days as opposed to weeks or months.
- Always-on monitoring: Automatically generates email and Slack alerts when any of your Vessels run into trouble, allowing the team to tackle workflow problems asap.
- Effortless scaling and security: Server maintenance is taken care of on the Shipyard-side, removing worries about resource utilization, uptime, and migrations. Inputs and configurations are all encrypted in transit with TLS v1.2 and at rest with AES-256. Also, processed data is immediately purged when workflows have finished running cloud integrations.
Pros:
- Supports a high degree of flexibility and scaling.
- Low code encourages accessibility by team members of all technical backgrounds.
- Over 100 cloud integrations are available (and counting).
- A free tier is available.
Cons:
- Data must pass through Shipyard, which may not align with organizations that require their data to remain on-premises.
Pricing: Paid tiers start at $250 per month.
2. Apache NiFi
An open-source dataflow solution, ApacheNiFi provides a web-based interface for designing, managing, and monitoring data flows. In addition to functioning as a data orchestration tool, Apache NiFi enables job scheduling, monitoring, and data routing through a web-based interface.
Thanks to this drag-and-drop interface, data team members with little or no coding experience (e.g., data analysts) may find Apache NiFi user-friendly. And the fact it also supports a wide range of data protocols and formats may make it appealing to small businesses that deal with a wide variety of data sources.
Top features:
- Browser-based user interface: Apache NiFi’s functionality centers on a good experience for design, control, and data monitoring.
- Extensive configuration options: Coupled with its user-friendliness, NiFi offers data teams runtime modification of flow configuration, back pressure control, and dynamic prioritization.
- Extensible design: Apache NiFi supports iterative testing and rapid development thanks to its component architecture for custom processors and services.
Pros:
- Well-suited for designing popular workflows.
Cons:
- Some users feel the use-case templates are too general for some business applications.
- Overall stability may be an issue.
Pricing: Free
3. Google Cloud Data Fusion
Another code-free option for data orchestration is Google Cloud Data Fusion, which offers a wide array of visual interfaces, connectors, and abstractions centered around business logic, all with the goal of lowering total operating costs (TOC).
This cloud-native, fully managed data integration platform may be the logical choice to handle data orchestration for businesses that already leverage Google Cloud services.
Top features:
- Google Cloud integration: Ensures “always on” data for analysis and enhances security and compliance.
- Open core: Built with open source project CDAP to simplify data portability.
- Graphical, no-code interface: Removes barriers to entry for data team members with little or no coding skills, like data analysts or less-experienced data scientists.
Pros:
- Open cloud helps businesses avoid vendor lock-in as they scale.
- Google works to offset its operations (i.e., your workload) with ongoing investments in renewable energy.
- A free trial is available.
Cons:
- Some users feel that implementation could be simpler and less time-consuming.
Pricing: Three editions (tiers) are available, the lowest starting at $1.80 per cloud data fusion hour.
4. Talend
Talend is an open-source data integration platform that leverages automation to handle data integration, transformation, and mapping. Talend is also a cloud-independent solution, meaning users can work with on-premises data or across any cloud provider without restrictions.
As part of Talend’s overall Data Fabric, this data integration tool offers a unified set of tools to integrate, clean, mask, and profile data for your organization.
Top features:
- Unified data integration: Convenient ELT/ETL tools and change data capture (CDC) simplify data ingestion, be that batch or streaming.
- Pipeline designers: Enables users to compose source-agnostic reusable pipelines, connecting them to any chosen data warehouse destination.
- Data Inventory: A lightweight and simple-to-use solution for data proofing within Talend Cloud, providing on-demand snapshots of data health.
Pros:
- Supports a commendable amount of data connections.
- Users feel the platform makes data pipeline creation and testing easy.
- A free trial is available.
Cons:
- Some users find some of Talend’s nested features frustrating as they require repeated steps.
- Large reported overhead costs may be an issue for some organizations.
Pricing: At the time of publication, Talend does not disclose pricing information on its website.
5. Microsoft Azure Data Factory
Billed as offering hybrid data integration at enterprise scale, Azure Data Factory is also worth considering for startups and smaller organizations. This integration platform is serverless, which helps reduce overhead, and its seamless integration with other Azure services makes it (perhaps) a no-brainer for those already using the Azure ecosystem.
But its user-friendly visual interface is also a bonus for those tasked with designing data pipelines. Businesses can also take advantage of Azure’s “pay-as-you-go” pricing, which provides monthly amounts of cloud-based services for free.
Top features:
- Hybrid data integration: Azure functions as a fully managed, serverless service that offers 90+ built-in maintenance-free connectors, by default.
- Exhaustive data governance: Boasting more certifications than any other cloud provider, Azure holds ISO/IEC, GDPR, CSA/CCM, ITAR, HIPAA, and IRS 1075 compliance, among others.
- Azure Synapse Analytics: Powered by Data Factory, Azure allows users to ingest data from both on-premises and cloud sources, transforming it with powerful data flows.
Pros:
- Extensive learning materials are available from Microsoft.
- Pay-as-you-go pricing requires no upfront commitment and can be canceled at any time.
- A free trial is available.
Cons:
- Some users feel the time needed to create, loan, and access resources is prohibitive.
- Configuring all of Azure’s available functionalities can be time-consuming.
- May make less business sense without a tertiary investment in the overall Azure ecosystem.
Pricing: At the time of publication, Microsoft does not list Azure’s pricing on its website. However, it does advertise a price-match guarantee against any comparable Amazon Web Service (AWS) product.
6. AWS Glue
Speaking of Amazon Web Services, AWS Glue is another serverless data integration platform that small business owners can consider, especially if they’re looking to reduce infrastructure management overhead.
Like Microsoft’s Azure, AWS Glue aims to make data orchestration simpler, cheaper, and faster, automatically discovering, categorizing, and moving data between various data stores. Glue users can connect to 70+ diverse data sources and also offers a visual interface for creating, running, and monitoring ETL pipelines as they load data into data repositories, like data lakes.
Top features:
- Interactive sessions: Gives data engineers hands-on interactivity to explore and prepare their data in an integrated development environment (IDE).
- Automatic schema discovery: Glue employs crawlers that connect to source data and automatically determine that data’s scheme, then authors the metadata needed during the ETL job authoring process.
- No-code data normalization: DataBrew offers a no-code, point-and-click interface for visualizing, cleaning, and normalizing data.
Pros:
- Automatically scalable regardless of data requirements.
- Offers multiple built-in dynamic frame transformations.
- A free tier is available.
Cons:
- Some users feel that Glue limits the control they have over their data architecture.
- Integrations with non-AWS products may be problematic.
- Not available for off-premises applications.
Pricing: At the time of publication, Amazon does not list AWS Glue’s pricing on its website.
7. Fivetran
Fivetran is another data orchestration option for small businesses and startups looking to keep overhead low. Fivetran offers a large library of 300+ pre-built connectors and automated schema management. These make it a low-maintenance data processing solution.
Fivetran streamlines data orchestration and normalization by leaning heavily into automation and performant change data capture. Additionally, performant change data capture increases the efficiency of data movement, while minimizing its impact.
Top features:
- Real-time database replication: Fivetran supports multiple replication methods, including change data capture, powering low-impact data movement in real time.
- Quickstart data models: Allow users to transform data into analytics-ready tables without needing code, dbt project, or 3rd party tools.
- Pre-built data models: Saves critical data resources by syncing with upstream connectors while preparing, aggregating, and enriching data automatically.
Pros:
- Data transformation requires no external scripts or tools.
- Works extremely well with low-volume data structures, potentially eliminating the need for an engineer.
- Both a free trial and a free plan are available.
Cons:
- Some users feel Fivetran’s pricing and billing structure could be clearer.
- Overall costs to use this data orchestration platform may be prohibitive for some smaller businesses.
- Connector stability issues can impact real-time data functionality.
Pricing: While specific pricing isn’t available on its website, its pricing example lists the Starter tier at $6,056 annually.
8. Apache Airflow
Apache Airflow is another open-source workflow management platform designed for data engineering tasks. But, unlike other entries we’ve covered on this list, Airflow users leverage Python features to maintain flexibility while dynamically generating tasks.
This need for coding knowledge makes Apache Airflow a great choice for small businesses that need a highly customizable data management and orchestration solution, especially when there’s significant in-house technical expertise.
Top features:
- Python-powered: While Python is a must-have for Airflow users, creating complex workflows requires only a basic level of knowledge.
- Flexible integrations: Airflow can be used with AWS, Azure, and Google Cloud (GCP), the industry’s major cloud providers.
- Deferrable operators and triggers: Along with asynchronous operation, Airflow reduces the resources required by long-running tasks.
Pros:
- Versatile and well-rounded for technical users.
- Users appreciate features specific to DAGs and script monitoring.
- Supported by a strong open-source community.
Cons:
- Airflow’s user interface (UI) may be complicated for less technical users.
- Data quality monitoring may be limited, depending on business needs.
Pricing: Apache Airflow is open-source and free of charge.
9. K2View
Another entry among no-code data orchestration tools is K2View, which emphasizes the ability to quickly unify, transform, and enrich data from any source system and deliver it to any target application.
In the case of K2View, users accomplish this via their patented data product platform. In real time, this platform can create and manage complete and compliant datasets for every business entity on demand. K2View ensures datasets remain in sync with their underlying sources while maintaining the ability to adapt to changes in source structures and/or provide instant access to authorized data consumers.
Top features:
- Micro-Databases™: Each acting as mini data lakes, K2View manages data for each business entity in its own high-performance, compressed, and encrypted database. These databases are instantly accessible by authorized data consumers in any way they choose (e.g., SQL, API, CDC).
- No-code data orchestration and transformation: Data ingested into K2View’s Data Product Platform can be easily manipulated using its embedded data orchestration tools, including data masking and tokenization.
- Enhanced security via Data products: Since K2View manages and encrypts within its own Micro-Database™, the risk of a mass breach is eliminated.
Pros:
- Users appreciate K2View’s training materials, which help ensure maximum platform utilization.
- Easy to set up and ensures a high level of data security.
- Interface and ease of use were cited as positives.
Cons:
- Necessitates a learning curve compared to other data orchestration tools/platforms.
- Some users feel the data analytics could be clearer/more intuitive.
- Cost may be an issue for some small businesses and startups.
Pricing: At the time of publication, K2View does not list pricing on its website.
10. Prefect
As the final recommendation on our list, Prefect positions itself as a workflow orchestration tool, one that enables users to orchestrate and observe their dataflows using Prefect’s open-source Python library.
Prefect’s Python framework also affords flexibility, allowing users to easily combine tasks into workflows, then deploy, schedule, and monitor their execution through Prefect’s UI or API.
Top features:
- Dynamic, code-driven workflows: Ensures that any flow written in Python is compatible with Prefect.
- Refined UI: Engineered for ease of use, even for those with little technical experience.
- Asynchronous task support: Concurrent tasks can run independently, improving the performance of both flows and blocking.
Pros:
- Some users feel Prefect’s developer experience is notable.
- Supported by an active community of users.
- A free tier is available.
Cons:
- Some users feel Prefect’s documentation could be simpler.
- Language support may be lacking.
Pricing: Paid tiers begin at $450 per month.
Choosing the right platform: When in doubt, orchestrate it out
It goes without saying that each option we’ve listed above has its pros and cons depending on your specific needs. So, yes, it’s important to evaluate each contender based on the needs of your business. But, considering so many of these tools offer free trials, tiers, and demos, we highly recommend you roll up your sleeves and give some of them a try yourself.
Wait! Did you actually just roll up your sleeves a little bit? Gotta love that attitude!
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