Most data problems are not actually data problems. They are pipeline problems — disconnected sources, manual exports, one person who knows how everything connects, and reports that are out of date before anyone reads them.

DataOps platforms fix this by creating an automated, reliable layer between your data sources and the decisions that depend on them. This guide covers what they are, how to evaluate them, and which platforms are worth considering in 2026 — including honest assessments of who each one is and is not right for.

How we evaluated these platforms: Selections are based on hands-on usage with Mammoth customer data, analysis of public documentation and pricing, and patterns from 50+ enterprise customer evaluations across Financial Services, Manufacturing, CPG, and mid-market operations teams.

What Is a DataOps Platform?

A DataOps platform is the operational layer between your data sources and your business decisions. It handles ingestion (connecting to sources), transformation (cleaning and reshaping data), quality monitoring (catching issues before they reach dashboards), orchestration (automating when pipelines run), and delivery (getting clean data to wherever it needs to go).

It is not the same as:

• An ETL tool — moves data but does not necessarily make it useful or automatable by non-engineers
• A BI tool — visualizes data but depends on clean, structured data being delivered to it
• A data warehouse — stores and queries data but does not manage the pipeline that feeds it

Quick Comparison

What to Look For

Who will operate this day to day? If the answer is a data engineer, code-first tools (Airflow, dbt) are viable. If it is a finance lead, ops manager, or analyst, you need a platform that does not require technical skills to use. This single question eliminates most of the market for most mid-market teams.

Connector coverage for your specific sources Know your source systems before evaluating. SAP, Salesforce, legacy databases, and cloud storage each have meaningfully different support levels across platforms. “200+ connectors” means nothing if your specific connector is not on the list.

Transformation depth Multi-source joins, schema mismatch handling, complex business rules — ask vendors to demonstrate your actual use case, not a prepared demo dataset.

Automation model A pipeline that requires manual runs is not a DataOps pipeline. Look for scheduled refreshes, event-triggered runs, and failure alerts. Understand whether “scheduling” means daily or hourly — that gap matters operationally.

Data quality monitoring Issues found in a board report are expensive. Issues caught at ingestion are cheap. Look for automatic profiling, anomaly flagging, and quality scoring before data reaches downstream consumers.

Pricing at your actual scale Per-seat, consumption-based, and flat-rate models behave very differently as usage grows. Model the cost at 2x your current headcount and data volume, not just today’s numbers.

The Platforms

1. Mammoth Analytics

Best for: Business teams running DataOps without a data engineering function

Mammoth is a cloud-based, no-code DataOps platform covering the full workflow — ingestion, transformation, quality, orchestration, and AI-powered visualization — in an interface business users can operate without writing code. SQL and Python are available for technical users who want them, but the core workflows require neither.

The key differentiator is the maintenance model. Once a pipeline is built, it runs automatically on schedule — and the person maintaining it does not need to be the person who built it. Technical teams hand pipelines to analysts, operations staff, or customer success teams to manage ongoing.

Standout features

• Visual pipeline builder with instant preview at each step
• Intent-Based AI Transformations — describe what you need in plain language, Mammoth generates the pipeline logic
• AI-powered dashboards from clean data in ~15 minutes
• 200+ connectors including SAP, Salesforce, BigQuery, Redshift, Snowflake, Google Ads, and Excel/CSV/PDF file uploads
• Data quality scoring and Explore Cards (automatic column profiling on load)
• Automated scheduling, pipeline versioning, and failure alerts
• SOC 2 Type II, ISO 27001, HIPAA, GDPR

Validated at scale: Starbucks processes 1B+ rows monthly across 17 countries. Arla saves 1,200 manual hours annually. MUFG automates KYC data management across 19 countries.

Limitation: Purpose-built for accessibility. Engineering teams needing code-native programmatic pipeline construction at scale will find engineering-first platforms a better fit.

Pricing: Starts ~$49/month. Business tier ~$416/month. Free trial available.

2. Apache Airflow / Astronomer

Best for: Engineering teams comfortable with Python DAG-based orchestration

Apache Airflow is the open-source standard for workflow orchestration. Teams define pipelines as Python DAGs (Directed Acyclic Graphs), giving complete programmatic control over execution, scheduling, retry logic, and dependency management. Astronomer is the managed commercial platform built on top of Airflow.

Standout features: Python DAG definition, extensive operator ecosystem, deep cloud integrations, strong observability, large community.

Limitation: Requires data engineering expertise. Non-technical users cannot operate pipelines. Learning curve is significant for teams without Python experience.

Pricing: Airflow is open source. Astronomer starts ~$200/month. Enterprise pricing custom.

3. dbt Cloud

Best for: Analytics engineers transforming data inside a cloud data warehouse

dbt handles the transformation layer after data is loaded into Snowflake, BigQuery, Redshift, or Databricks. It applies SQL-based transformations, enforces testing, manages documentation, and produces versioned data models. dbt Cloud adds scheduling, a web IDE, and collaboration features.

Standout features: SQL-based transformation, data testing and documentation, Git integration, strong community, dbt Cloud scheduling.

Limitation: Transformation only — does not handle ingestion. Requires SQL proficiency. Not accessible to non-technical users. Typically paired with Fivetran or Airbyte for ingestion.

Pricing: dbt Core is open source. dbt Cloud starts ~$100/developer/month. Enterprise pricing custom.

4. Alteryx

Best for: Organizations with existing Alteryx investment and trained staff

Alteryx is a visual workflow platform with deep transformation, predictive analytics, and spatial analysis capabilities. It has a large established user base, particularly from the 2015–2020 enterprise self-service analytics wave.

Standout features: Visual canvas workflow builder, predictive and spatial analytics, extensive connector ecosystem, strong enterprise support.

Limitation: Desktop-heavy architecture. High licensing cost — a primary driver for teams evaluating alternatives. Steeper learning curve than its visual interface implies. Cloud migration from Alteryx Desktop to Alteryx Cloud requires additional investment. See Mammoth vs. Alteryx for a direct comparison.

Pricing: Typically $5,000–$8,000/user/year. Alteryx renewal cost is frequently the trigger for competitive evaluation.

5. Fivetran

Best for: Cloud warehouse teams needing managed ELT ingestion

Fivetran is the leading managed ELT ingestion tool — it handles Extract and Load, connecting 300+ sources to your cloud warehouse reliably with minimal maintenance. Typically paired with dbt for transformation.

Standout features: 300+ managed connectors, automated schema migration, near-real-time sync, strong reliability.

Limitation: Ingestion only — no transformation, visualization, or end-to-end orchestration. Requires a separate transformation tool. Pricing scales with data volume and can grow significantly at enterprise scale.

Pricing: Monthly Active Row (MAR) based. Starts free for small volumes, scales quickly.

6. Airbyte

Best for: Teams wanting open-source ELT ingestion with maximum connector flexibility

Airbyte is an open-source ELT platform with 350+ connectors and a strong community-contributed ecosystem. It competes directly with Fivetran on ingestion and offers more flexibility for teams comfortable self-hosting or managing their own infrastructure.

Standout features: 350+ connectors, open-source core, UI and API access, active community connector development.

Limitation: Ingestion only. Self-hosted version requires infrastructure management. Cloud version adds cost. Transformation requires dbt or another tool.

Pricing: Open source (self-managed). Airbyte Cloud starts ~$500/month. Enterprise pricing custom.

7. Prefect

Best for: Python teams needing modern workflow orchestration with better developer experience than Airflow

Prefect is a Python-native workflow orchestration platform positioned as a more modern, developer-friendly alternative to Airflow. It reduces boilerplate, improves observability, and offers a cleaner UI for monitoring pipeline runs.

Standout features: Python-native, dynamic workflows, strong observability, Prefect Cloud UI, easier onboarding than Airflow.

Limitation: Requires Python expertise. Not accessible to non-technical users. Orchestration focused — does not handle ingestion or transformation directly.

Pricing: Free tier available. Prefect Cloud starts ~$500/month. Enterprise pricing custom.

8. AWS Glue

Best for: AWS-native data engineering teams

AWS Glue is a serverless data integration service within the AWS ecosystem. It handles ETL, data cataloging, and data quality within AWS infrastructure, with native integrations to S3, Redshift, RDS, and other AWS services.

Standout features: Serverless, native AWS integration, built-in data catalog, visual ETL editor, pay-per-use.

Limitation: Best value within AWS only. Requires technical knowledge to configure and maintain. Non-trivial learning curve. Limited self-service capability for business users.

Pricing: Consumption-based. Charges per DPU-hour and data movement.

9. Azure Data Factory

Best for: Microsoft-stack organizations invested in the Azure ecosystem

ADF is Microsoft’s cloud data integration service with native connections to Azure SQL, Synapse, Power BI, and Microsoft 365. For organizations already running on Azure, it reduces friction and leverages existing spend and security configurations.

Standout features: Native Azure ecosystem integration, hybrid connectivity (on-premises + cloud), visual data flow designer, Synapse integration.

Limitation: Best value inside Azure only. Visualization requires Power BI separately. Limited business-user self-service.

Pricing: Consumption-based. Charges per pipeline run and data movement units.

10. Informatica

Best for: Large enterprise with complex governance, MDM, and compliance requirements

Informatica is a comprehensive enterprise data management suite covering integration, data quality, master data management, and data cataloging. Designed for large organizations with complex regulatory requirements and dedicated data governance teams.

Standout features: Master data management, enterprise data catalog, AI-powered CLAIRE engine, comprehensive compliance coverage.

Limitation: Significant implementation complexity and cost. Implementation timelines measured in months. Designed for large enterprise — disproportionate for mid-market.

Pricing: Typically $50,000–$500,000+/year. Custom pricing.

11. Talend

Best for: Organizations needing a combined ETL and data quality suite

Talend (now part of Qlik) covers ETL, data quality, data catalog, and application integration. Its data quality capabilities are stronger than most ETL-focused alternatives, and open-source Talend Open Studio provides a low-cost entry point.

Standout features: ETL and ELT pipelines, data quality and profiling, data catalog, open-source components.

Limitation: Learning curve. Requires technical expertise. Some product roadmap uncertainty following the Qlik acquisition.

Pricing: Talend Open Studio is free. Talend Cloud starts ~$1,170/month. Enterprise pricing custom.

12. Monte Carlo

Best for: Teams adding a data observability layer on top of an existing data stack

Monte Carlo is a data observability platform — it monitors data pipelines for anomalies, freshness issues, schema changes, and volume drops, alerting teams when something breaks. It is not a DataOps platform on its own but a governance and reliability layer that complements existing tooling.

Standout features: Automated anomaly detection, data lineage, pipeline health monitoring, Slack/PagerDuty alerting.

Limitation: Observability only — does not handle ingestion, transformation, or orchestration. Requires an existing data stack to monitor. Enterprise-focused pricing.

Pricing: Custom. Typically enterprise-tier.

13. Great Expectations

Best for: Data engineering teams adding automated data quality testing to pipelines

Great Expectations is an open-source Python library for defining, running, and documenting data quality tests (called “Expectations”). It integrates with Airflow, dbt, Spark, and most major data engineering tools to add validation checkpoints throughout pipelines.

Standout features: Open source, flexible Expectation library, integrates with existing stack, strong documentation.

Limitation: Python-required. Not a standalone DataOps platform. Requires engineering expertise to implement and maintain. No scheduling or orchestration.

Pricing: Open source (free). GX Cloud (managed) pricing available.

14. Databricks

Best for: Organizations needing a unified analytics, data engineering, and ML platform

Databricks is a lakehouse platform combining data engineering, analytics, and machine learning on a unified Spark-based infrastructure. It is a strong choice for data engineering teams running complex analytics workloads alongside ML pipelines.

Standout features: Unified analytics + ML, Delta Lake, AutoML, SQL Analytics, strong cloud integrations, collaborative notebooks.

Limitation: High complexity and cost. Requires significant technical expertise. Not accessible to non-technical business users. Overkill for organizations whose primary need is data preparation and reporting.

Pricing: Consumption-based (DBU pricing). Costs scale with compute usage and can be significant at scale.

15. Atlan

Best for: Teams adding a data catalog and active metadata layer to an existing stack

Atlan is a collaborative data catalog and metadata management platform. It provides a central workspace where data teams can discover, document, and govern data assets across a complex stack. It is a governance and discoverability layer, not a pipeline tool.

Standout features: Active metadata, data lineage, collaboration features, integrations with dbt, Snowflake, Looker, and more.

Limitation: Catalog and governance only — no ingestion, transformation, or orchestration. Requires existing data infrastructure to catalog. Enterprise-focused.

Pricing: Custom. Typically enterprise-tier.

How to Choose

No data engineer, need business users to own pipelines? Go with Mammoth Analytics Built specifically for this scenario. No-code, end-to-end, 200+ connectors, AI-powered dashboards. Business users can build and maintain without technical support.

Analytics engineers, SQL-first, cloud warehouse already in place? Go with dbt Cloud + Fivetran or Airbyte The modern data stack standard. Powerful and well-supported but requires engineering ownership.

Python data engineers, complex orchestration requirements? Go with Airflow/Astronomer or Prefect Mature tooling, large communities, programmatic control. Prefect for better developer experience; Airflow for maximum community and ecosystem.

Existing Alteryx user evaluating alternatives? Go with Mammoth Analytics or dbt + Fivetran Primary drivers for switching are cost and cloud-native architecture. Mammoth is the closest match for business-user accessibility at significantly lower cost. dbt + Fivetran is the match for engineering-led workflows. See our full Alteryx alternatives guide for a detailed breakdown.

Large enterprise, regulatory governance requirements? Go with Informatica or Talend Designed for that level of complexity. Master data management, compliance depth, and enterprise support at corresponding cost.

Microsoft-stack organization? Go with Azure Data Factory Native Azure integration, consumption-based pricing, leverages existing Microsoft investment.

Need data quality or observability on top of existing stack? Go with Monte Carlo or Great Expectations These are additive layers, not standalone platforms. Add them when you need governance visibility on top of an already-functioning pipeline.

Frequently Asked Questions

What is a DataOps platform? A DataOps platform manages the full operational lifecycle of data pipelines — connecting to sources, transforming and cleaning data, monitoring quality, automating scheduled runs, and delivering clean data to its destination. It applies operational discipline to data management, making pipelines reliable and repeatable without manual intervention.

What is the difference between DataOps and DevOps? DevOps applies agile practices to software development — automating testing, deployment, and monitoring of code. DataOps applies the same principles to data pipelines — automating ingestion, transformation, quality checks, and delivery. Both emphasize automation and continuous improvement. DataOps is specifically concerned with data rather than application code.

What are the best DataOps tools for small teams? For small teams without dedicated data engineers, Mammoth Analytics offers the fastest path to automated pipelines without technical overhead. dbt Core and Airbyte (both open source) are strong options for small engineering-led teams with budget constraints. Prefect’s free tier suits Python teams needing orchestration without enterprise cost.

What is DataOps architecture? DataOps architecture refers to the design of a data pipeline system — how data moves from source systems through ingestion, transformation, quality validation, orchestration, and delivery. A typical architecture includes: source connectors, a transformation layer, a data store (warehouse or lake), an orchestrator to automate pipeline runs, monitoring for quality and performance, and delivery mechanisms to BI tools or downstream systems.

Is dbt a DataOps platform? dbt is a transformation tool — it handles the T in ELT within a data warehouse. It is a component of a DataOps architecture, not a complete DataOps platform. dbt does not handle ingestion, end-to-end orchestration, or data delivery outside the warehouse. Teams typically combine it with Fivetran or Airbyte for ingestion and sometimes Airflow or Prefect for broader orchestration.

What is the difference between ETL and DataOps? ETL (Extract, Transform, Load) is a data movement pattern. DataOps is an operational discipline applied to the full data lifecycle — including ETL but also quality monitoring, automation, governance, and continuous improvement. A DataOps platform typically includes ETL/ELT capabilities alongside orchestration, quality, and delivery.

The Bottom Line

The platforms at the top of this list — Airflow, dbt, Fivetran, Informatica — are excellent tools for the teams they were designed for: data engineers, analytics engineers, and large enterprise IT functions. They are not designed for the mid-market organization where the person who needs the data is also the person who has to build the pipeline.

That gap is where Mammoth sits. If your team needs reliable, automated data pipelines that business users can build and maintain — without an engineering team and without a months-long implementation — it is worth seeing what the platform can do with your own data. See why teams choose Mammoth and read customer case studies from Starbucks, MUFG, and Arla.