10 Best DataOps Tools for Teams That Need to Scale Fast (Free & Paid)

1. Keboola

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. 

Pricing

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. 

Pros: 

• Keboola’s modular pre-built components help you simplify the creation and customization of data flows.
• Keboola automatically collects Telemetry Data throughout the data lifecycle, making observability available at all times across your data assets.
• Use data streaming to build event-based messaging systems or real-time monitoring and analytics without the infrastructure overhead.
• Improves collaboration across silos by simplifying data access with the Data Catalog and bringing non-technical people to the table with no-code features like the visual flow builder and no-code transformations.
• Scales easily with features like dynamic backend sizing, efficient replication using change data capture, and shareable code across data transformations and apps.  
• Bring-your-own-stack (BYOS) with Keboola’s plug-and-play architecture.
• Turn raw data into full products using data apps.

Cons: 

• While Keboola provides over 250 ready-to-use connectors, it might not have one for every data source you use. In such cases, you can use Keboola's Generic Extractor or Writer, but these options require some coding skills. If you're not comfortable with coding, you can request new features from Keboola’s data engineering team.

Reviews 

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 

2. Rivery

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: 

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.

Pros: 

• Kits (pre-built workflows) are templates that are easily deployed to streamline common data operations, like sending Microsoft Teams Alerts for observability. 
• 200+ pre-built connectors for data integration speed up the creation of ETL data pipelines.
• DevOps functionalities from syncing pipeline logic to version control systems like GitHub to running pipelines programmatically via CI/CD processes.
• Fast data replication using change data capture (CDC). 
• Customizable orchestrations with loops and conditional logic.

Cons

• Data destinations are focused on cloud data storage (Snowflake, Amazon S3, BigQuery, etc.). If you need a different end-point, you’ll have to request a new integration from the team or build a custom connector yourself.
• Limited support for data sharing (e.g. no Data Catalog) or data governance (no data lineage).
• Python (and data science) features are limited to enterprise tiers. 

Reviews 

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

3. Apache Airflow

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. 

Pricing: 

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.

Pros: 

• Python and its libraries (like Spark) for big data processes, machine learning models, data testing and validation, or extend it to new use cases.
• Custom orchestration scenarios and data pipeline dependencies management.
• User-friendly interface for real-time observation and monitoring of data flows.
• Large-scale data operations using Kubernetes or Celery executors for efficient scaling.
• A supportive community with numerous online resources and tutorials.

Cons

• Deleting jobs erases data workflow metadata, making debugging, data governance, and compliance challenging. 
• Its complex deployment requires DevOps expertise, and setting up Airflow on Windows calls for Docker knowledge.
• Workflow execution is bound by Python, potentially limiting performance for low-latency requirements. 

Reviews 

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

4. K2View

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.

Pricing: 

K2View charges for three separate activities:

• Micro-database reads and writes.
• Data transfers and storage (in GB).
• Development/production environments.

The cheapest option will cost you a couple of hundred dollars up to thousands of dollars per month. Check their standard cloud pricing.

Pros: 

• Supports multiple data ingestion methodologies, including streaming, messaging, and API extraction.
• Enables compliant customer data sharing and exposure through data masking.
• Organizes data at the customer level for easy metrics and a 360 view.
• Generates synthetic data for testing, machine learning, and prototypes.

Cons

• Complexity and limited documentation require dedicated training. 
• Users complain about the difficulty of transforming integrated data, especially when real-time CDC replication syncs override any changes.

Reviews 

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

5. Tengu

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. 

Pricing: 

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.

Pros: 

• No-code features.
• Drag-and-drop interface to create data pipelines.
• Tengu assistant that provides automatic guidance for non-technical users.

Cons

• Lack of supporting material for first-time users.
• Complexity that might overwhelm non-technical users.
• Users complained of problems when scaling. For example, monitoring, orchestration, and even pipeline execution can freeze when working with large data sets. 
• It offers limited advanced features like running predictive analytics or working on complex data lineage features (e.g. automatically merging master data with production).

Reviews 

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

6. Unravel

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: 

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.

Pros: 

• Agnostic to deployment environment: works on Azure, AWS, GCP, or on-premise. 
• AI-driven recommendations for optimizing analytics, reliability, and efficiency. 
• Comprehensive observability insights, including budget and spend metrics across your infrastructure.
• A wide array of observability metrics. 

Cons

• Expensive for smaller organizations.
• Complexity not suitable for small-to-medium operations.
• Scarce official documentation and small community; expect to spend more time on support and troubleshooting.

Reviews 

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

7. Mozart Data

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.

Pricing: 

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.

Pros: 

• 400 pre-built no-code connectors simplify data integrations.
• Managed stack using off-the-shelf solutions like Fivetran, Snowflake, and dbt allows access to top data tools without having to integrate them by yourself. 

Cons

• Costs are offloaded to other tools. 
• Connectors are developed via a 3rd party, making the connector quality variable and bug resolution troublesome. 
• UI can be clunky, especially when writing queries.

Reviews 

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

8. Nexla 

Best for: Prototyping data products.

Nexla is a data engineering platform designed to automate the creation of Data Products.

Pricing: 

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. 

Pros: 

• Universal connector pulls data from various API-based and file-based sources.
• Nexsets simplify data sharing by encapsulating different schemas, formats, and actual data.
• Automated data profiling helps you with data testing.
• No-code features for business users. 

Cons

• Limited extraction abilities for less known or standard data sources. 
• Limited documentation.
• Non-intuitive UI/UX complicates simple tasks.
• Despite offering many no-code features, tool setup, and usage require developer involvement. 
• Weak error management makes bug tracking difficult.

Reviews 

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

9. Fivetran

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: 

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. 

Pros: 

• Optimized for ELT workflows.
• Supports enterprise data sources like Oracle, SAP, and Workday. 
• Efficient at data replication with robust CDC.
• Pre-built queries for connectors with complex data models save time.

Cons

• Monthly active row pricing can lead to escalating costs as you scale your datasets and data operations. 
• Limited ability to build custom connectors means you must ensure your data requirements align with its available connectors.
• Specializes in ELT, but lacks features like data cataloging, business intelligence, and advanced orchestration.

Reviews 

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

10. Talend Data Fabric

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.

Pricing: 

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.

Pros: 

• Boasts a range of specialized tools within its ecosystem, including Open Studio Big Data (for Hadoop component integration), Stitch Data Loader (featuring a graphical interface for ETL pipeline creation), and Data Quality.
• Provides over 900 pre-built components. 

Cons

• Lack of comprehensive documentation for its features.
• Administrative tasks can be complex, requiring lots of effort for version upgrades and configuration changes.
• There is a steep learning curve; significant time is needed to fully understand and utilize the platform, even for simple data ingestion. 

Reviews 

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

Key things you should consider when buying the right DataOps tool 

To select the best DataOps tool for your needs, use the following questions as a guide:

1. Data integration: Can the tool easily connect with all essential data sources and destinations?
2. Data governance: Does it offer features to manage data ownership, lineage, and access permissions?
3. Data management: Does the tool support data cataloging and metadata management to simplify data discovery, sharing, and documentation?
4. Data orchestration: Can the tool efficiently automate and manage complex data workflows (batch and real-time) and dependencies?
5. Observability: Does the tool offer monitoring and insights into the performance and health of data pipelines? Can it alert you to critical issues or anomalies in real-time?
6. Collaboration: Does it allow multiple team members, both technical and non-technical, to collaborate on data projects and share resources easily? 
7. Scalability: How well can the tool adapt to increasing data volumes and demands over time?
8. Pricing: What would be the total cost of ownership of implementing this tool? 

This helps you assess the buy vs build dilemma, which is common when comparing free and open-source solutions versus vendor software.

9. Value: What business value will the tool add? Evaluate its value from two angles: cost-reduction through workflow efficiency and the potential for creating new opportunities, such as enabling new data use cases.

Scale your data operations without losing control with Keboola 

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. 

Book a demo with our sales or try Keboola for free (no credit card required).