DataOps Platforms Resources
Articles, Discussions, and Reports to expand your knowledge on DataOps Platforms
Resource pages are designed to give you a cross-section of information we have on specific categories. You'll find articles from our experts, discussions from users like you, and reports from industry data.
DataOps Platforms Articles
What Is Happening in the Data Ecosystem in 2022
G2 Launches New Category for DataOps Platforms
DataOps Platforms Discussions
What is Lakehouse in Databricks?
I want to start a discussion focused on DataOps platforms for large-scale deployments that teams are actually using and finding value in. While some tools lean enterprise, there are several with capabilities and integrations that make sense for organizations operating at massive scale.
These are some of the top-rated options on G2’s DataOps Platforms category:
- Databricks Data Intelligence Platform: Lakehouse-based platform for unified engineering, governance, and AI with jobs/orchestration designed to scale. Has it simplified your large-scale pipeline automation and cross-team collaboration?
- Monte Carlo: End-to-end data observability that detects freshness/volume/schema issues and speeds incident resolution at scale. Has it improved your SLAs and reduced time-to-detect across complex estates?
- Acceldata: Data Observability Cloud that monitors pipelines, infrastructure, and costs with AI-driven anomaly detection—used by large enterprises and banks. Did it help you control cloud spend while maintaining reliability?
- IBM StreamSets: DataOps platform for designing and operating batch/streaming/CDC pipelines with drift protection across hybrid and multicloud. How well does it maintain performance and transparency at enterprise scale?
If you’ve implemented any of these (or others), I’d love to hear what worked well, what didn’t, and which platforms were surprisingly helpful for large-scale DataOps.
I was also looking into this set of software tailored to enterprises! https://www.g2.com/categories/dataops-platforms/enterprise


