Data Center Management Software

So far, we have yet to come across a dislike. There is one small Feature request we would like to see come to life and be apart of their platform, but we have yet to get it approved. Review collected by and hosted on G2.com.

They are adding more and more features so you don't have to work with difference vendors or software to get it all done for your client. Review collected by and hosted on G2.com.

The main friction we've run into is monitor upkeep as our data models evolve — when a table gets moved, renamed, or restructured upstream, monitors pointing at the old location start erroring out until someone manually reassigns them to the right domain. It's not a dealbreaker, but it means someone has to periodically audit for stale or broken monitors rather than the system flagging that drift proactively.

We've also ended up with some overlapping monitors over time as we iterated on configurations — nothing that breaks anything, but it means occasional cleanup to keep things tidy. A clearer "this monitor is now redundant with that one" nudge would help, similar to how tuning suggestions already help with noisy alerts. Review collected by and hosted on G2.com.

Before Monte Carlo, catching data issues meant waiting for someone downstream, often a business user, to notice a report looked off, we trace it back to the source. Monte Carlo flips that: freshness, volume, and data quality issues get caught automatically, often before anyone outside the data team even notices.

It also extends into our transformation layer as we get visibility into dbt test failures and warnings directly, so problems in our modeling jobs surface as soon as they happen rather than being buried in a job log someone has to go dig through. That's saved us from a fair number of "silent" failures that would otherwise have quietly degraded a report.

The overall benefit is trust and speed: our team spends less time firefighting and more time building, because we're not manually auditing pipelines or reacting to complaints after the fact. When something does break, we know quickly, we know where, and the right people get notified without anyone needing to go looking. Review collected by and hosted on G2.com.

One downside is that troubleshooting can sometimes be difficult when a pipeline fails, as the error messages aren't always detailed enough and you may need to dig through multiple logs to find the root cause. The platform also has a lot of features, so it can take some time for new users to become comfortable with everything. While serverless is convenient, costs can increase if compute usage isn't monitored properly. I'd also like to see faster UI responsiveness in some areas, especially when navigating large job histories or catalogs. Review collected by and hosted on G2.com.

Databricks has helped me and my team simplify our data engineering workflow by providing a single platform for data ingestion, transformation, governance, and analytics. We use it to process data from Bronze to Silver and Gold layers, which has made our ETL pipelines more reliable and easier to manage. Features like serverless compute, Unity Catalog, and built-in monitoring have reduced operational effort, improved collaboration across teams, and helped us deliver data products faster. Review collected by and hosted on G2.com.

The biggest drawback is cost management. While Snowflake's pay-as-you-go model is flexible, it's easy for compute costs to grow if resources aren't monitored carefully or workloads are not optimized. Heavy dependence on cloud infrastructure means organizations with strict on-premises requirements may have fewer deployment options. Review collected by and hosted on G2.com.

Snowflake solves the challenge of storing, managing, and analyzing large volumes of data without the complexity of maintaining traditional data warehouse infrastructure. Its cloud-native architecture enables organizations to scale compute and storage independently while supporting analytics, data engineering, and AI workloads from a single platform. Centralizes data from multiple sources into a unified platform for analytics. Separates compute and storage, allowing workloads to scale independently. Delivers fast query performance for large datasets without extensive infrastructure management. Enables secure data sharing across teams and external partners without duplicating data. Integrates easily with BI tools, ETL pipelines, and machine learning platforms. In my workflow, Snowflake helps simplify data analysis by providing a reliable and scalable environment for querying and processing large datasets. Instead of spending time managing database infrastructure, I can focus on building reports, analyzing data, and supporting data-driven decision-making. The biggest benefit is improved scalability and faster analytics. Snowflake reduces operational overhead, accelerates data processing, and enables teams to generate insights more efficiently while adapting to changing business demands. Review collected by and hosted on G2.com.

hile Atera worked well for our core RMM needs, some areas felt less mature as our requirements grew. Reporting and customisation could be more flexible, and patch management did not always provide the level of detail or control we wanted. We also found that some newer functionality received significant focus while parts of the core platform could still benefit from refinement. Overall, it remains a capable platform, but there are areas where larger or more complex ICT environments may start to feel its limitations. Review collected by and hosted on G2.com.

Atera is helping us centralise device monitoring, patch management, remote support and IT automation across our environment. Having this information in one place gives our ICT team better visibility of device health and lets us respond to issues without needing to physically access the device.

The biggest benefit has been reducing manual administration. Automated patching, software deployment, monitoring alerts and scripting allow us to manage a large number of endpoints more consistently and proactively. This saves technician time and helps us identify issues before they become larger support problems. Review collected by and hosted on G2.com.

Sometimes when building a job in the builder tool, some of the modules can't be attached to others and I don't know why this might be? Some documentation on how to build 'good' jobs might be a worthwhile read - or perhaps an AI job builder in the future? Where I can describe the type of job I want and it's able to build the job for me would be really handy! Review collected by and hosted on G2.com.

Data quality is a big talking point for us all and this gives us the tools now to begin to shape this narrative, and bring more expertise in house. We may even look into using the mailing house features in the future, though admittedly we're not quite there yet. Review collected by and hosted on G2.com.

ServiceNow ITOM has a few notable drawbacks, starting with its high cost, particularly if you rely on advanced modules such as Discovery, Service Mapping, and AIOps. In large scale implementation projects, the implementation process can be complex and time-consuming, often requiring skilled resources as well as ongoing tuning to keep everything working as expected. In more complex environments, Discovery and service mapping may not always be fully accurate, which can lead to gaps or inconsistencies.

The platform also comes with a steep learning curve. On top of that, the AI/AIOps capabilities depend heavily on having high-quality data and the right configuration; early on, they may generate false positives or miss certain issues. Finally, performance can be impacted when large volumes of events are being processed, especially if the environment is not properly optimized. Review collected by and hosted on G2.com.

ServiceNow ITOM helps address several common challenges in IT operations, such as limited visibility, fragmented toolsets, alert overload, and overly reactive incident handling. By offering unified monitoring and service mapping, it provides clearer insight into infrastructure dependencies and supports faster root-cause analysis.

Thanks to its strong integration capabilities, ITOM can consolidate data from multiple monitoring tools into a single platform, reducing silos and improving coordination across teams. It also remains reliable under high event volumes, which helps teams process alerts efficiently and focus attention on what matters most.

With its AI/AIOps features, ITOM further reduces noise and inefficiency by correlating events, detecting anomalies, and predicting potential issues before they affect users. This helps shift operations from reactive firefighting to a more proactive approach.

From a pricing and ROI standpoint, the platform delivers value by reducing downtime, minimizing manual effort, and improving overall operational efficiency, which can translate into cost savings over time. In addition, structured support and onboarding make adoption smoother and help teams accelerate time to value.

The UI/UX is intuitive and well-structured, making it easy to navigate between events, service maps, and incidents, which improves efficiency and reduces the learning curve for operations teams. Review collected by and hosted on G2.com.

One area I dislike about ManageEngine Endpoint Central is that the interface can sometimes feel cluttered and overwhelming, especially when navigating through advanced features. Some configurations require multiple steps, which can make routine tasks more time-consuming. Improving the UI, simplifying navigation, and providing clearer guidance for certain settings would make the overall experience more user-friendly and efficient. Review collected by and hosted on G2.com.

ManageEngine Endpoint Central helps solve the challenge of managing and securing multiple devices from one centralized platform. It makes tasks like software deployment, patch management, remote troubleshooting, and device monitoring much easier. This saves time, reduces manual work, and helps ensure that endpoints stay updated, secure, and compliant. Review collected by and hosted on G2.com.

The transcription accuracy can be hit or miss, especially when there are multiple speakers or crosstalk on a call. It also sometimes struggles to distinguish between speakers correctly. The search and filtering within the platform could be more intuitive — finding a specific moment in a long call takes more effort than it should. Review collected by and hosted on G2.com.

It eliminates the need to rely on memory or messy handwritten notes after sales calls. Having a searchable recording with AI-highlighted key moments means I can quickly pull up what a prospect said about their pain points, timeline, or budget without re-listening to the entire call. It also helps with coaching — my manager can review snippets instead of sitting in on every call. Review collected by and hosted on G2.com.

It’s mainly familiar with BigQuery, since its native environment is Google Cloud. Sometimes queries run slowly, especially when working with complex tables. By default, we can only see the first 50 rows, and it really should show more. Also, when we copy the output, we’re only able to copy some of the records instead of the full result set. We can append records, but we can’t update or delete them. Review collected by and hosted on G2.com.

I use it for my ETL testing, since we ingest data from Mongo into BQ, and then the main fact tables in the analytical layers are used in Databricks. It benefits me because it runs in the cloud and doesn’t require any hardware space on my side. Overall, the queries work well for my testing needs. Review collected by and hosted on G2.com.

One aspect I find challenging about SAS Viya is its tendency to be resource-intensive, often demanding substantial CPU, memory, and storage to operate efficiently, particularly in larger environments. The process of installation and upgrading can also be quite complicated, as it typically involves coordinating multiple services and dependencies. Furthermore, troubleshooting can be a lengthy process, since it sometimes requires in-depth knowledge of both SAS components and the underlying infrastructure. I also think that some users might perceive the interface and integration with third-party tools as less intuitive when compared to more modern, lightweight analytics platforms. Review collected by and hosted on G2.com.

SAS Viya is solving the problem of managing, analyzing, and visualizing large volumes of data efficiently across different environments. It provides a unified platform for data integration, advanced analytics, and machine learning, which helps streamline workflows and reduce the complexity of moving between tools. By enabling collaboration between data scientists, analysts, and business users, SAS Viya improves productivity and decision-making. Its scalability and ability to run on cloud or on-premises infrastructure also allow organizations to handle growing data demands with flexibility and performance. 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.

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.

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.

While Acho offers no-code transformation, some users might require more complex data manipulation. For these users, Acho could benefit from offering advanced scripting options or integration with external data science tools like Python libraries. Review collected by and hosted on G2.com.

If you find yourself spending a lot of time moving data between spreadsheets or struggling to get your data into a usable format, Acho can be a lifesaver. It simplifies data wrangling and allows you to focus on getting the most out of your information. Acho empowers even non-technical users to access and manipulate data. This makes it possible for them to generate reports and answer basic data questions without relying on IT support. 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.

Simple operations such as deduplication are easy to perform, but implementing sophisticated hierarchy rules or customized lead routing may seem tricky.

The return on investment is quite apparent for larger organizations, but for small teams, the pricing might seem too high relative to their Salesforce data.

The AI-driven enrichment works well, but sometimes it normalizes data inappropriately, such as consolidating similar business names which should not be combined. Review collected by and hosted on G2.com.

Traction Complete eliminated duplicate chaos, made account hierarchies clear, streamlined lead routing, and enhanced data. Its advantages include neat dashboards, efficient workflow, and even revenue generation. This is the type of application that turns Salesforce(Integration) from being a hindrance to becoming an engine for growth. Review collected by and hosted on G2.com.

Not really something to dislike but as a potential improvement point if this is relevant, probably adding an MDM capability would prove beneficial Review collected by and hosted on G2.com.

Definitely accelerates data deduplication, enrichment and cleansing prior to an implementation from legacy system(s) - and helps monotiring and managing data quality in business as usual thus setting-up the frame for AI au automation to deliver expected outcomes Review collected by and hosted on G2.com.

While OpsRamp is a powerful platform, one area that could be improved is the learning curve for new users. Because it offers a wide range of monitoring, automation, and IT operations capabilities, it can take some time for teams to fully understand and utilize all its features effectively. Additionally, initial configuration and customization may require careful planning, especially in complex enterprise environments. However, these challenges are common with comprehensive enterprise observability and IT operations platforms, and the benefits generally outweigh the initial effort required. Review collected by and hosted on G2.com.

For me, the biggest benefit is improved visibility and faster issue resolution. By consolidating monitoring data into a single pane of glass, OpsRamp helps identify problems proactively before they impact users. Its automation capabilities also reduce repetitive operational tasks, allowing teams to focus on higher-value work. This leads to improved system reliability, reduced downtime, and more efficient IT operations. As someone working in observability and infrastructure monitoring, having a unified latform helps simplify troubleshooting enhances overall operational efficiency. Review collected by and hosted on G2.com.

If I had to nitpick, the initial UI for the truly deep engineering stuff can be a little much for new users, but once you get past the initial setup (which is covered well by Customer Support and training), the daily use is very intuitive. The Interactive Sessions feature is also amazing for cutting down on Spark job startup time, making my day-to-day work way more efficient. Review collected by and hosted on G2.com.

ILUM solves a few major headaches that were common in the big data world. The main problem is fragmentation—trying to stitch together separate, expensive data lakes and data warehouses, which leads to messy data silos, inconsistent results, and a lot of unnecessary work. Older proprietary systems like Cloudera or Databricks are costly and lock you in. ILUM gets rid of those problems. It's an open-source based Data Lakehouse platform that unifies everything.

For me as a user, this translates to tangible benefits. First, it is much faster. The interactive Spark sessions eliminate those 20-40 second job start times, and processing can be up to twice as fast overall. This means I'm not waiting on the infrastructure; I'm actually doing my job. Second, it's cheaper and more efficient. The platform is zero-license, Kubernetes-native, and runs anywhere (cloud, on-prem, hybrid), which drastically cuts infrastructure costs—we talk about 60% savings for clients. That budget can go to actual development, not just licensing fees. Third, it provides clarity and governance with features like automated Data Lineage and a central Data Catalog, so I can trust the data's quality and know exactly where it's been. In short, it lets me focus on the challenging and valuable work of data science and analytics instead of wrestling with complex, slow, and expensive tools. Review collected by and hosted on G2.com.