Before we get to the best DataOps platforms, let's start here. Data has been termed the new water of the global economy. The analogy is derived from the use of terms like “data pipelines” and “data lakes” and the fact that data, like water, is everywhere. What used to be scarce like oil is now present in abundance, and companies need to figure out the best way to leverage it.
That’s where DataOps comes in.
DataOps is the process of integrating teams, processes, and data across an organization to shorten the life cycle between data acquisition and analysis. It breaks down silos and gathers fragmented data in a well-structured manner that’s agile, fast, and scalable.
DataOps platforms power this entire process by orchestrating data pipelines.
There are many DataOps platforms on the market—but how do you get started? What are the features you should look for? Which tool will drive the most value for your organization based on your specific needs?
In this article we’ll explore the best DataOps platforms, their key features, pros and cons, and who should use them, so you can find just the right fit for your organization.
Best DataOps platforms in 2023
Shipyard is a cloud-based DataOps platform that lets you build automated data pipelines at record speeds. It provides a drag-and-drop visual interface that makes it easy for business users to leverage the platform’s automation capabilities and optimize workflows for various use cases.
Seasoned users like data engineers and data analysts can further customize data pipelines using whatever language serves them best—Shipyard is an agnostic platform. Whether performing data ingestion from multiple sources or loading data into desired destinations like data warehouses, data lakes, etc., Shipyard lets you do it easily and quickly.
Shipyard is one of the best DataOps platforms when it comes to flexibility and scalability. It’s ideal for fast-growing teams that want to build mission-critical data pipelines and automate as much of their data ecosystem as possible to get insights quickly.
If you have data silos and want to improve collaboration within your organization for better decision-making, Shipyard can seamlessly connect various projects and stakeholders, reducing the time taken from data acquisition to deriving insights.
- Intuitive interface: Shipyard provides an easy-to-use visual interface and comes with pre-built low-code templates that are ready to use.
- Automation: It provides features like advanced scheduling, on-demand triggers, and webhooks so you can easily automate different data pipelines.
- GitHub integration: It ensures you are constantly in sync with GitHub and provides continuous version control, easy deployments, and up-to-date code.
- Reporting: Shipyard offers you granular reports that give you end-to-end performance and accurate insights to verify your real-time data operations are running seamlessly.
- Monitoring: It provides features like automatic retries and cutoffs that take care of your workflow resiliency with minimal intervention.
- Integrations: It connects to a range of enterprise data sources and can help you extract, transform, and load (ETL) data into destinations at a high speed.
- Shipyard has zero learning curve—an easy-to-use visual interface makes it simple for anyone to use the platform.
- It allows you to run workflows on demand, on a schedule in the background, or via webhook, depending on your needs.
- Real-time monitoring and instant notifications help you stay on top of errors so you can quickly fix them.
- It provides granular user management and audit logs so you can have more control over your infrastructure.
- You only pay for what you use, which makes Shipyard one of the most affordable DataOps platforms out there.
- There’s no API access to update or create in bulk.
- It can't export or store your logs externally.
- Credentials have to be input every time you set up a new workflow.
- There are no pre-built connectors for ingesting data from SaaS tools.
DataKitchen’s DataOps platform helps users automate workflows like data development and production, making cross-functional team collaboration easier.
It enables data blending, data cleansing, data integration, data migration, data management, and data warehousing to help users meta-orchestrate data pipelines. With features like parallel execution and parameterized testing, DataKitchen helps data teams save time on high-volume processing.
DataKitchen is better suited for businesses that are passionate about test-driven development and want to use their existing data infrastructure with a DataOps tool to bring speed and scalability to the entire process.
- Testing is baked directly into the platform.
- The platform lets you reuse logic through the use of "recipes".
- Multi-environment management.
- Many different integrations with the common data storage vendors.
- Platform visuals are more dated and clunky to find the relevant information.
- Requires a level of technicality to get started.
DataKitchen’s pricing isn’t available on their website, but you can request a demo to get started.
StreamSets is a fully managed cloud-first DataOps platform that lets users perform data integration to build smart data pipelines for streaming data. It provides a single design experience for all pipelines—including build batch, streaming, CDC, ETL, ELT, and machine learning—using a single user interface. It also includes pre-made connectors for data ingestion, and users can develop their own connectors by programming them if needed.
StreamSets is a good option for enterprises that are looking to transform large amounts of streaming big data from multiple data platforms, data catalogs, and apps into efficient data pipelines.
- StreamSets Data Collector provides a drag-and-drop UI.
- It lets users build fully instrumented “smart” data pipelines that let you monitor data in-flight and handle drift with built-in detection and handling capabilities.
- It lets users modernize data lakes and data warehouses with minimal coding skills to continuously feed their data analytics platforms.
- It integrates with Amazon Key Management Service and AWS Secrets Manager for one-step authentication and security.
- Its visual interface makes it fast and easy for users to design data pipelines.
- It allows users to add and modify data sources on their own.
- It gives visibility into all pipeline operations across any cloud and on-premises.
- It supports 50+ data sources, including database and streaming sources such as MapR and Kafka.
- Users need to purchase additional components like Control Hub, which requires users to manage, patch, and upgrade 16 databases.
- It can be challenging to sift through the logging and error messaging to diagnose issues.
Rivery is a modern, fully managed DataOps platform that lets users automate and orchestrate data processes. It supports native Python to give data engineers flexibility so they can create complex data workflows without any hassle.
Its log-based change data capture (CDC) instantly captures and syncs source data changes with cloud data warehousing through stream processing. What’s more, it integrates with 190+ fully managed data connectors, and even lets you add custom data sources via REST API and webhooks.
Rivery is well-suited for teams that have dedicated ETL engineers, data analysts, and data scientists looking to build custom environments for different teams or projects. It might also be highly useful in a DevOps environment, allowing users to create testing and deployment environments to enhance data quality.
- Rivery’s data connectors come with a plug-and-play functionality that enables data ingestion in just a few clicks.
- It lets users prepare, clean, and transform raw data assets into structured data with full control by performing multifaceted SQL-based transformations inside a cloud data warehouse.
- It provides industry-leading security and privacy standards such as single sign-on (SSO) and fine-grained user access control.
- Rivery is a no-code, auto-scalable, fully managed DataOps platform, so users can focus on mission-critical tasks rather than maintenance.
- It provides auto-migration to automatically perform a full database migration using standard extraction via batch processing.
- It’s compatible with all leading data analytics systems, BI platforms, and data analysis technologies.
- Users can aggregate all the right data from internal databases and third-party platforms into a centralized dashboard.
- It allows users to modify pipelines programmatically, access version control, and proactively monitor the health of the data pipelines.
- It has a steep learning curve for beginners and non-technical users.
Which DataOps platform is best for you?
As time goes on, your data needs will change and you’ll need a solution that’s scalable and powerful. In short, you need a DataOps platform that lets you build automated data pipelines, improves cross-functional team collaboration, and boosts overall productivity.
Shipyard gives modern business teams the agility, flexibility, and scale they need to create and manage data pipelines for their unique needs. Whether you want to transfer data between BigQuery databases or transfer Snowflake data to FTP, Shipyard has you covered. To get started, sign up for our free version.