Discover the best DataOps tools in the market to scale your data operations.
Bugged down by another data quality issue? Jumping on yet another meeting with data analytics to figure out how to add a dataset into your main data processing workflow? Are your fingers itching to try a new tool but you’re unsure how it will play with your data stack?
When you spend more time putting out fires rather than engineering new features, it’s time to find a tool that automates your workflows.
In this article, we’ll take a closer look at some of the best DataOps tools to consider for your organization:
We'll look at the main features of each tool, its pros and cons, its best use cases, and user reviews to help you make the right choice.
Best for: Growing companies and data teams that need to scale data operations fast without losing control.
Keboola is a self-service data operations platform that streamlines the heavy lifting behind DataOps. From 250+ pre-built connectors that simplify data integration to dynamic backend sizing that helps you scale your operations to big data and beyond, Keboola uses automation for every data operation to save you time.
Keboola also offers user-friendly, no-code features, enabling business professionals to self-serve data without relying on data engineers. This includes ready-to-use data templates, visual flow builder, and no-code transformations.
Keboola offers a freemium, usage-based pricing model. You receive 120 free computational minutes for your first month, followed by a monthly refill of 60 additional free minutes.
If you exhaust your free minutes, you can purchase more at a rate of 14 cents per minute.
Unlike other DataOps tools, Keboola has transparent pricing and supports automated budget notifications, so you can stay in control of your spending.
G2 reviews: 4.7 out of 5 based on 90 reviews
“Instead of separately selecting, acquiring, configuring and integrating an endless list of technologies to build your data stack, Keboola gets you there in one platform.” - Robert C., Head of Product at GymBeam
"Keboola allowed us to focus on our data entirely without spending time on infrastructure and operations. It is simple to use and comes with excellent documentation. With its API-first approach, it also allowed us to build custom components." - Patricia M., Senior Manager Engineering
Best for: Small-scale DataOps without extensive data lineage or data science features.
Rivery is a cloud-based ETL data platform that simplifies the creation of data flows. It allows you to ingest data from various data sources into a data lake or cloud data warehouse of your choice, while also transforming your data using SQL or Python.
Pricing is usage-based. Rivery charges customers using Rivery Pricing Unit (RPU) credits. There are three tiers (starting at $0.75/RPU, limited to 2 users), and within them, workloads have varying prices based on source (database vs API), operation complexity, and frequency.
The $ price roughly translates to the bytes of data transferred/changed during data operations. Estimate your costs using their pricing estimator.
Rivery offers a limited free trial, for 14 days or 1,000 free RPU credits, whichever expires first.
G2 reviews: 4.7 out of 5 based on 84 reviews
“As a data analyst, I find the tool really easy to use; it's intuitive how you connect to the different data sources and create your data pipelines. I like that you can group your pipelines to organize the displays and also the suggestions offered.” - Raquel A., Data Visualization Engineer
Best for: Data professionals who prefer hard-coding solutions with Python.
Apache Airflow is a Python-based open-source data orchestration tool that allows data teams to author, schedule, monitor, and automate data flows using Python scripts. Because it’s Python-based, data engineers and data scientists use it for multiple use cases: from ETL data pipelines to running machine learning apps.
Airflow is open-source, so it's free to use. However, you'll need to invest in server setup, compute resources, and skilled DevOps personnel for maintenance.
For an easier option, consider cloud-based managed Airflow services like Google's Cloud Composer, AWS's Amazon MWAA, or Microsoft Azure's Docker/Kubernetes setups. Note that costs vary by provider.
G2 reviews: 4.3 out of 5 based on 80 reviews
“Handles dependency management like a charm. From dataflow to complex branching, task retry, catchup runs, etc., it has everything” - Aditya V., Data Scientist II
Best for: Data teams at enterprises who need their data operations to be extremely customer-centric (viz-a-viz financial reporting across the group, product analytics, etc.).
K2View is an enterprise-grade data fabric platform for real-time data integration. K2View organizes data into micro-databases (one for each customer), making customer-centric data analytics easy.
K2View charges for three separate activities:
The cheapest option will cost you a couple of hundred dollars up to thousands of dollars per month. Check their standard cloud pricing.
G2 reviews: 4.4 out of 5 based on 20 reviews
“I was impressed with the product's functionality to be able to connect to various types of databases and consolidate the information in one place. And how easy it is to develop the features that don't come with the installation, using Java functions.” - Carlos C., Data Architect
Best for: Small startup data engineering teams.
Tengu is a DataOps platform that focuses on providing low-code and no-code features to orchestrate your data flows.
Tengu offers two pricing tears: TENGU.CORE (self-managed) and TENGU.PLUS (Tengu-managed), both as 1-year PaaS licenses. Additionally, the team behind the DataOps platform offers consultancy services called TENGU.DEV.
The pricing of the licenses and services is bespoke and varies from customer to customer. Expect to pay approximately $1500/year for TENGU.CORE and more for the other products and services.
G2 reviews: 4.3 out of 5 based on 16 reviews
“It can be easily integrated with other applications, and with basic programming knowledge, developers can customize it for their business process to be automated.” - Yogesh S., Delivery Module Lead
Best for: Enterprise DevOps teams trying to increase observability across their multi-cloud or hybrid deployments.
Unravel is an observability data platform for the modern data stack. Instead of building data operations within Unravel, you create and deploy them elsewhere and use Unravel to monitor and understand your DataOps.
Pricing is usage-based on top of your data platform, for example, DBUs for Databricks and the number of queries for Snowflake. The pricing isn’t transparent, you’ll have to talk to sales to get a quote. But you can expect the costs to be in the ballpark of a couple of thousand dollars per year.
G2 reviews: 4.3 out of 5 based on 25 reviews
“Unravel Data provides a full stack observability platform which helps data-driven organizations maximize business value from data. Also, it uses AI to provide insights into the data stack.” - Sanya S., Chief Operating Officer
Best for: Tech team or solo dev at a startup looking for a managed service
Mozart Data is a data platform that offers many data operation features: from pipeline orchestration to data cataloging and data lineage. Though it presents itself as a product, don’t be mistaken. It’s a centralized UI that integrates with other tools (such as Fivetran, Snowflake, dbt, …) to perform common data operations.
Mozart Data offers a freemium subscription model. The costs start at $1000/month (+ one-time $1000 implementation fee) for 1,750M monthly active rows and 135 compute hours. They also offer a free tier, which is limited to 500k monthly active rows and 25 compute hours. Due to these limits, you can think of the free tier as a free trial.
G2 reviews: 4.5 out of 5 based on 65 reviews
“This is an excellent tool that automates in an excellent way the work that data engineers must traditionally perform in order to speed up and have a better execution of a modern data stack, this software manages an excellent data warehouse while maintaining the extraction processes, transformation, and loading, also providing a very easy-to-use interface to program and supervise data transformation” - Jose L., Software Engineer
Best for: Prototyping data products.
Nexla is a data engineering platform designed to automate the creation of Data Products.
Nexla’s pricing is usage-based, but the company keeps the exact dollar amount hidden. You’ll have to contact sales to get a quote. A 15-day free trial is available via the AWS marketplace.
G2 reviews: 4.7 out of 5 based on 33 reviews
“Business and data teams get a collaborative, developer-friendly experience to integrate, transform, provision, and monitor data at scale.” - Deepthi P., Associate Software Engineer
Best for: Enterprises handling substantial data loads.
Designed for data extraction and loading, Fivetran performs well at the initial stages of data movement. While it's not as robust in data transformation (relies on dbt instead of having its own transformations), it compensates with over 400 connectors for efficient data handling.
Pricing is based on Monthly Active Rows (MARs), which are rows of data that get inserted, updated, or deleted by Fivetran’s connectors. Each row is only counted once, irrespective of the number of monthly changes.
Fivetran also offers a free tier with up to 500,000 monthly active rows.
G2 reviews: 4.2 out of 5 based on 361 reviews
“Fivetran makes data engineering processes easier by replicating and automating our data warehouse complete with historical data. Using Fivetran's tables accelerates our time to publish data visualizations and help our chief-level staff perform using data-driven decisions.” - Ben A., Data Insights Analyst
Best for: Technical and non-technical teams within medium to large enterprises
Talend Data Fabric is an enterprise-grade no-code drag-and-drop data ingestion platform.
Not publicly disclosed; contact sales for a quote. However, some reviewers have noted that their pricing tends to be on the higher end compared to other alternatives.
G2 reviews: 4.4 out of 5 based on 11 reviews
“It has an intuitive interface which makes data integration and management very easy. It also has a lot of connectivity options giving me flexibility in working with many different data sets.” - Michelle W., Full Stack Software Developer
To select the best DataOps tool for your needs, use the following questions as a guide:
This helps you assess the buy vs build dilemma, which is common when comparing free and open-source solutions versus vendor software.
Keboola checks all the boxes: it offers out-of-the-box connectors, robust governance, observability, seamless collaboration, scalability, and automated workflows. Streamline your data workflows and find new revenue-generating use cases.
Interested? Keboola offers an always free tier, so you can start automating your data operations without breaking the piggy bank.