Data Center Management Software

I think the main downsides of Ninja are around Remote Desktop and reporting. With the Remote Desktop feature, you can’t save credentials per user; it’s only per organisation, which is terrible. We should not have shared credentials for obvious Cyber Essentials reasoning.

Reporting also isn’t ideal. I wanted to report on groups of devices with a software count, but you can’t do this, for example, in a report I’d like to see that 5 devices have Software_X. It seems like you can only view this from the dashboard page for all devices at once or an organisation, and even there the filtering isn’t very useful.

Lastly, our previous RMM solution had some helpful features that I miss: a preview/screenshot function so you can see the state of a device before connecting; a chat function to initiate contact before you connect (not only once you’ve already connected); and a network node feature where you can select a device as a network node and VPN tunnel to it remotely. That way, from your local machine you can access other devices and services on their network without needing to physically remote onto the customer’s device just to reach something on their network. Review collected by and hosted on G2.com.

Ninja allows us to remotely support thousands of devices whenever remote support is needed. Review collected by and hosted on G2.com.

The biggest pain point for me is the lack of possibility to merge alerts from metric monitors into one incident. We often have an issue that triggers many alerts, and we have to manage each alert separately, even though all have the same root cause. Since metric monitors are the backbone of Monte Carlo, it's really frustrating. This has been the case for a year and a half now. Another issue is the too fast and too big changes; I expected more stability at this stage. It's really difficult to keep up with paradigm shifts. For example, the change for Table monitors caused confusion. I recently ingested a big data set only to learn that tables are now monitored by default upon ingestion, which was contrary to previous behavior where you had to set up monitoring manually. Review collected by and hosted on G2.com.

Monte Carlo helps notice missing or improper data. It's easy to use, integrates with tools like Slack and Jira, and has a user-friendly UI. Before we haven't had real monitoring, so it's a game changer for us Review collected by and hosted on G2.com.

Snowflake requires careful management of compute costs. At the same time, it provides some optimized ETL functionality within Snowflake itself and helps keep the information stored in the database seccure. Review collected by and hosted on G2.com.

Snowflake helps me tackle infrastructure and server challenges by bringing data warehousing, analytics and data collaboration together in one place. It supports external data sharing and real time processing and it can also be used to work with customer support data. It makes scaling easier as data grows, gives clearer visibility into the underlying infrastructure and supports practical use cases such as fraud detection. Review collected by and hosted on G2.com.

I don't think there is anything I don't like, maybe they need to work on estimated cost feature while running any query, sometime it doesn't show the memory associated with that and as its analytical warehouse, so real time update is not possible like transactional database, maybe in future they can add those features as well Review collected by and hosted on G2.com.

In current scenario, all our data sinks are stored in Bigquery or external tables linked with Bigquery becasue its so easy todo any analysis on top of Bigquery and also further it seamlessly connect with Looker for detailed analysis. Now days, we also started to leverage their model creation capability on the data stored in Biglake managed table or Bigquery table. Ultimately it really helps to build end to end pipeline without worrying about storage and scalling. Review collected by and hosted on G2.com.

One thing I dislike about Databricks is the platform can feel complex for new users, especially when managing clusters and configurations. Pricing can also become expensive with larger workloads if resources are not optimized carefully. While integrations and AI features are powerful, the onboarding process and support documentation could be more beginner-friendly. Review collected by and hosted on G2.com.

Databricks helps solve the challenge of processing and analyzing large amounts of data efficiently in one platform. It combines data engineering, analytics and AI workflows, which reduce the need for the multiple separate tools. This improves collaboration, speeds up data processing, and helps generate insights much faster. Review collected by and hosted on G2.com.

What I dislike most about Atera is that some of its more advanced features still feel less mature than those in larger, enterprise-focused RMM platforms. Reporting and analytics have improved over time, but they can still feel limiting when you need highly customized dashboards or deeper business insights.

The PSA module is convenient because everything is integrated, but certain workflows and automations aren’t as flexible as they are in dedicated PSA solutions. Ticketing can also become cumbersome in more structured service desk environments.

Patch management is another area that could use improvement, particularly around visibility and consistency in larger or more complex environments. At times, troubleshooting automation failures or agent-related issues requires more manual intervention than I would expect.

Mac management also lags well behind the Windows experience. Feature parity isn’t there yet: monitoring, scripting, patching, and device management for macOS feel more limited and less reliable than on Windows endpoints. For MSPs supporting mixed environments, this creates operational inconsistencies and often means relying on additional tools to properly manage Apple devices.

Finally, while the platform evolves quickly, some UI changes and new features can feel like they’re released before being fully refined. Review collected by and hosted on G2.com.

Atera helps centralize and simplify day-to-day MSP operations by bringing RMM, PSA, remote access, patch management, monitoring, and billing together in a single platform. Rather than juggling multiple disconnected tools, technicians can work from one interface, which boosts efficiency and helps reduce operational overhead.

The biggest benefit for me is the time saved through automation. Automated monitoring, patching, alerting, and scripting cut down on repetitive manual tasks and let the team focus more on higher-value work and customer support. Having centralized visibility across all client environments also makes it easier to spot issues proactively before they become critical.

The per-technician pricing model is another major advantage, since it supports growth without worrying about endpoint-based cost increases. That makes scaling more predictable and financially sustainable for an MSP.

Atera also helps improve response times and the overall customer experience by streamlining ticket management and remote support, enabling faster troubleshooting and quicker resolution for end users. Review collected by and hosted on G2.com.

One downside of SAS Viya is that it can have a steep learning curve for new users, especially those without a strong analytics background. Additionally, the cost can be relatively high compared to other tools, and some advanced features require extra configuration, which can slow down initial setup. Review collected by and hosted on G2.com.

SAS Viya is solving the challenge of handling large, complex datasets and turning them into actionable insights quickly. Before using it, we struggled with slow processing and fragmented tools, but now we can perform advanced analytics, forecasting, and reporting in one unified platform. This has improved our decision-making speed, reduced manual effort, and increased overall efficiency across teams. Review collected by and hosted on G2.com.

It is good. Review collected by and hosted on G2.com.

DQLabs simplifies data quality management with its easy-to-use platform, advanced capabilities, and rich features like real-time anomaly detection and seamless integration with various applications, making data navigation and analysis more efficient. Review collected by and hosted on G2.com.

1. UI / UX

Limitations: The UI feels less intuitive and somewhat unpolished. There’s a learning curve, and in some situations you may need scripting knowledge to really get the most out of it.

2. Integrations

Limitations: API-based integrations with external enterprise tools can be somewhat limited.

3. Performance

Limitations: Performance can dip on slower networks, especially during large-scale patch deployments.

4. Pricing / ROI

Limitations: Pricing feels a bit higher.

5. Support / Onboarding

Limitations: The initial setup and configuration can take time.

6. AI / Intelligence

Limitations: It’s not as advanced as more AI-first platforms. Review collected by and hosted on G2.com.

IT teams often end up using different tools for patching, security, asset tracking, and remote support. That fragmentation creates unnecessary complexity, drives up costs, and leaves poor visibility across the environment.

Endpoint Central addresses this with unified endpoint management (UEM) from a single console, letting teams manage Windows, Mac, Linux, and mobile devices together. From a UI/UX perspective, having everything in one place makes day-to-day administration more consistent and easier to follow.

A common challenge is simply not knowing what devices exist or what they’re running, which leads to unmanaged endpoints and, ultimately, security risks. The solution here is real-time asset inventory and monitoring, with full visibility into hardware, software, and usage mapping. The AI/intelligence angle is the real-time insights and analytics that help turn inventory data into something actionable.

Unpatched systems are the #1 cause of breaches & ransomware, and manual patching is both slow and error-prone. Endpoint Central’s approach is automated patch management for both the OS and applications, supported by AI-driven threat detection plus vulnerability scanning and remediation. With AI/intelligence focused on predictive threat detection, the practical outcome is faster patch cycles and less downtime.

When devices are spread across locations, networks, and users, remote management and support becomes difficult. The platform’s remote monitoring and control works across LAN, WAN, cloud, and remote setups, and includes secure remote troubleshooting tools. A central dashboard improves remote management from a UI/UX standpoint, while support and onboarding benefit from faster remote issue resolution. Performance-wise, this translates into less downtime for employees.

Many routine tasks—patching, software deployment, and reporting—are repetitive and time-consuming. An automation-first platform helps by automating patch deployment, software installation, and reporting workflows, which improves performance through faster execution and fewer manual errors.

There’s also the ongoing difficulty of meeting standards such as GDPR and ISO. Built-in compliance reports, policy enforcement, continuous monitoring, and audit-ready data help reduce the manual effort needed to stay aligned.

Finally, multiple tools plus manual work typically means higher cost, and low efficiency leads to poor ROI. Consolidating tools and leaning on automation improves asset utilization and helps bring costs under control. Review collected by and hosted on G2.com.

It’s fast and quickly provides a mail address. Review collected by and hosted on G2.com.

It helps my sales team identify fake people and get the correct ID. Review collected by and hosted on G2.com.

I thoroughly enjoy using Data8 and have no dislikes regarding it. Review collected by and hosted on G2.com.

Data8’s Data Quality Solutions help us maintain strong data integrity by accurately identifying goneaways and deceased records. This is crucial for us, as it ensures our communications are appropriate, up‑to‑date, and respectful. By preventing misdirected outreach, we’re able to protect and uphold our reputation with our alumni and donor communities. The resulting confidence in our data allows us to engage more effectively and maintain the high standards expected of our institution. Review collected by and hosted on G2.com.

I understand it has less capabilities on the AI automation and integration parts as the tool takes a lot of time to process the information and it is a lot slow so we need to improve on that part.

Secondary concern is regrading the tool cost around the cost and the learning curve for high-level logic needs to be taught to every developers while using the solution Review collected by and hosted on G2.com.

Manual works is converted to automated workflows.

It integrate agents, and cyber security teams, and tools with speed and control. Review collected by and hosted on G2.com.

The file-based discovery in ServiceNow IT Operations Management doesn't seem to be streamlined properly, and its configuration can be confusing initially, especially as you need to execute it multiple times to get the desired results. The documentation is not complete and makes certain assumptions. The procedure should be added to reflect the actual steps to be taken to get the software installs. Review collected by and hosted on G2.com.

I use ServiceNow IT Operations Management for automated CMDB discovery, software asset management, event ingestion, and alert triaging. It enriches the CMDB without building custom patterns. Review collected by and hosted on G2.com.

The learning curve is very steep, especially if you come from SQL-based or traditional independent data warehousing systems. It takes some time for you to be able to operate effectively once you know how models, spaces, and connections work.

Another difficulty lies in the fact that debugging problems in the data may involve navigating many levels of abstraction. If the result obtained does not correspond to what was expected, determining at which level there is a problem may take some additional time. Review collected by and hosted on G2.com.

Before the introduction of SAP Datasphere, data from multiple sources had to be joined and processed before analyzing, causing inefficiencies and inconsistencies.

However, in SAP Datasphere, the data integration layer is consolidated, meaning that reporting and analytics can now happen in a location close to the data governance models. This ensures fewer inefficiencies in preparing data for analysis and reporting. Review collected by and hosted on G2.com.

The record limit is one of the constraint that is always a concern to keep to our license subscription. Review collected by and hosted on G2.com.

It is helping by giving us a cleaner data by reducing duplicates, data consistency, verification and completeness of information. Review collected by and hosted on G2.com.

Very unreliable. Easy to get the UI to break doing pretty basic flows. Server often errors either silently or with cryptic errors, reflecting poorly on our product as it leaves us to take the blame Flatfile's instability. Their status page professes a very high uptime rate, but anecdotally after interacting with their API on a near-daily basis for the last several months, I would take their uptime claims with a grain of salt; what's the use of the server being online if it responds with 500s anyway on what should be a valid network request? Lacks adequate customizability. Review collected by and hosted on G2.com.

It's supposed to solve the problem of ingesting CSVs but is more of a headache than it's worth. I would much rather write the functionality myself. There isn't anything Flatfile does that really makes them unique or gives them a moat IMO. Review collected by and hosted on G2.com.

Red Hat Smart Management exhibits strong integration with Red Hat products, which may pose challenges if considering a transition to alternative vendors down the road. Additionally, apart from these overarching concerns, a number of users have expressed criticisms about specific aspects of Red Hat Smart Management:

1.Navigating and utilizing the reporting system can be somewhat challenging. 2.While capable, the performance monitoring features may not match the robustness found in some competing products.

3.The support for non-Red Hat systems is somewhat limited. Review collected by and hosted on G2.com.

Improved security: Red Hat Smart Management can help organizations to improve their security posture by providing a centralized view of all RHEL systems and by automating patch management.

Increased compliance: Red Hat Smart Management can help organizations to comply with industry regulations by providing compliance reporting capabilities.

Improved performance and reliability: Red Hat Smart Management can help organizations to improve the performance and reliability of their RHEL environments by providing performance monitoring and troubleshooting capabilities. Review collected by and hosted on G2.com.

that we only just started using it, haha! Review collected by and hosted on G2.com.

Helping us move away from cold leads towards HOT leads Review collected by and hosted on G2.com.

The support and onboarding phase has been much heavier than I anticipated. This is not a plug-and-play application. It really requires a dedicated Salesforce Administrator to map out the full data architecture and territory rules before you even start working in the software. The initial learning curve is also steep, especially when you’re setting up complex, weighted round-robins.

On the performance side, because it’s such a robust managed package, deploying large routing flows from our Full Copy Sandbox into Production isn’t always completely seamless. When we run large bulk data loads (for example, importing 50,000+ leads from a trade show), we sometimes need to temporarily pause the routing flows to avoid Salesforce CPU timeout errors, and then run the routing in batches. Review collected by and hosted on G2.com.

We implemented Traction Complete to solve two major problems: “speed-to-lead” delays and poor CRM data hygiene (especially orphaned leads). Before this tool, inbound demo requests would sit in a general queue while an operations analyst manually checked whether the lead belonged to an existing Enterprise account.

The pricing and ROI were justified within the first two quarters. By automating our lead-to-account matching, it saves our RevOps team roughly 15–20 hours per week that used to go to manual data merging, deduplication, and troubleshooting. On top of that, because leads are instantly routed to the correct Account Executive, our response times have dropped from 24 hours to under 5 minutes, which has directly improved our pipeline conversion rates. Review collected by and hosted on G2.com.

Downside is, Overall , in view of compliance sometimes PII uploaded to s3 can be identified with Macie. But when its an image or pdf containing photo copies of such personal sensitive information its hard to identify. It might be stored on the ec2 or s3. But figuring out it is complex. We do have OCR techniques in some services, which can trigger ec2 playbooks. Yes , it can be achievable with right problem solving technique and using the required services. Review collected by and hosted on G2.com.

Security Patches, Trigger based actions on infrastructure. Logging for IOC identifications. Many more. Review collected by and hosted on G2.com.