Data Center Management Software

One drawback of NinjaOne is that some of its advanced features can feel a bit limited compared to more robust enterprise tools, especially when it comes to deeper customization and reporting.

The reporting, in particular, could be more flexible and detailed, which can make it a bit challenging when you need very specific insights for clients or internal use. Review collected by and hosted on G2.com.

One of the main challenges it solves is managing endpoints across different locations. Instead of switching between multiple tools or systems, everything can be monitored and managed from a single dashboard, which saves a lot of time and effort.

It also makes it easier to automate routine tasks like patching, updates, and basic troubleshooting. This reduces manual work and helps keep systems secure and up to date without needing constant attention. Review collected by and hosted on G2.com.

To be completely honest, this is the best tool I have used for data observability and large-scale data quality checks. However, if I had to mention one drawback, it would be the extra features that come with the integrations. For example, MC attempts to display traces from our Airflow integration in several areas, but I have noticed that the information is not always accurate in some places. I have observed a similar issue with the dbt integration as well. Review collected by and hosted on G2.com.

This is one of my favorite technology I have ever used. I really love it's out-of-the-box ML monitors that provide us alerts whenever an anomaly is detected and in majority of the cases it's a true positive. Data quality is critical for any organization and being able to manage it across the organization without spending a lot of time on it is something really great. Monte Carlo empowers us to do this in the most efficient and optimized way. It has a wide range of standard monitor templates using which we can quickly create table monitors and also provides customization to the level where we can define monitors using YAML code! It's helping us detect any data quality issues very quickly and also provides a nice lineage and the impact analysis. Review collected by and hosted on G2.com.

The pricing model can become expensive for large-scale queries without proper optimization and cost monitoring. The learning curve for advanced features and query optimization techniques requires time investment. Limited support for certain data types and occasional complexity in debugging nested queries could be improved for better developer experience. Review collected by and hosted on G2.com.

BigQuery has solved our massive data processing bottlenecks by enabling real-time analysis of terabytes of data that previously took hours to process. This has accelerated our decision-making process, reduced infrastructure costs by eliminating the need for on-premise data warehouses, and empowered our team to run complex analytical queries without waiting for IT support. The serverless model has transformed how we handle data at scale. Review collected by and hosted on G2.com.

UI/UX: The notebook editor still feels behind dedicated IDEs. No native multi-file search, limited refactoring support, and the git integration UI is clunky for teams managing dozens of notebooks across workflow bundles. We ended up doing all real development in VS Code and treating the Databricks workspace as a deployment target, which adds friction. The workspace file browser also doesn't handle folder structures well when you have 50+ notebooks organized by domain there's no filtering, tagging, or favorites.

Integrations: Databricks Asset Bundles (DAB) are a step forward, but the documentation has gaps for complex multi-bundle deployments. We run a shared Global_Utilities bundle that other workflow bundles depend on, and getting cross-bundle references to work reliably across dev/qa/prod targets required significant trial and error. The ADF-to-Databricks integration works, but debugging failed pipeline runs means jumping between the ADF monitoring UI and Databricks job runs with no unified view. A tighter handshake between orchestration and compute monitoring would save hours of troubleshooting.

Performance: Cluster cold-start times remain a pain point for development workflows. Spinning up a single-node Standard_D4ds_v5 cluster takes 4-7 minutes, which breaks flow when you're iterating on notebook logic. Serverless compute helps but isn't available for all workload types yet, and the cost premium is hard to justify for dev/test environments.

Pricing/ROI: The DBU pricing model is opaque for capacity planning. Estimating monthly costs for a project with 30+ scheduled jobs, interactive development clusters, and SQL warehouse queries requires building custom spreadsheets because the built-in cost management tools don't give you a clear forecast by workflow or domain. We've been surprised by cost spikes from jobs that ran longer than expected with no easy way to set per-job budget alerts.

Support/Onboarding: Enterprise support response times are inconsistent. Critical issues with Unity Catalog permissions during our migration took 3-5 business days for initial triage, which stalled our deployment timeline. The community forums are helpful for common patterns, but for Unity Catalog edge cases (cross-catalog lineage, complex permission inheritance), the knowledge base is thin.

AI/Intelligence: Genie is promising but still rough for production use. It struggles with joins across more than 3-4 tables, sometimes generates incorrect SQL against our Gold layer, and there's no easy way to curate or correct its responses to improve accuracy over time. Our business users got excited, tried it, hit wrong answers on moderately complex questions, and lost trust. A feedback loop where domain experts can flag and correct Genie's outputs would make it genuinely production-ready. Review collected by and hosted on G2.com.

Data Governance Fragmentation → Unified Catalog We struggled with a Hive Metastore environment where table ownership, access control, and lineage were managed through a patchwork of manual documentation and custom scripts. After implementing Unity Catalog, we now have centralized governance across 4 catalog layers (bronze, silver, gold, platinum) spanning 6 business domains. What used to take a full-time data steward to track manually is now enforced automatically through catalog-level permissions and lineage. This cut our access provisioning time from days to under an hour per request.

Siloed ETL Logic → Standardized Medallion Architecture Before Databricks, our ETL pipelines were inconsistent — different teams wrote transformations differently, with no shared utilities or patterns. We built a standardized framework (Batch_Utilities.py) with reusable functions for schema validation, merge operations, data quality checks, and audit column management. Every notebook across all domains now follows the same 7-cell structure. This reduced new notebook development time from 2-3 days to roughly 4 hours, and onboarding a new developer to the pattern takes a single afternoon instead of a week.

Costly Report Refresh Failures → Reliable Pipeline Orchestration We had recurring issues with Power BI reports pulling stale or incomplete data because upstream jobs failed silently. With Databricks Jobs and metadata-driven pipeline tracking (pipeline status, start/end timestamps logged per run), we now catch failures at the transformation layer before they propagate to reports. Report data freshness issues dropped by approximately 80%, and our finance team stopped scheduling "data verification" meetings that used to consume 3-4 hours per week.

Multi-Environment Deployment Chaos → Asset Bundles Deploying notebooks across dev, QA, and production used to involve manual file copies and environment-specific config edits — error-prone and slow. Databricks Asset Bundles gave us declarative databricks.yml configs with variable substitution per target. A deployment that took 45 minutes of manual steps now runs in under 5 minutes via CLI. We deploy with confidence because the same bundle definition is validated before it hits production.

Self-Service Analytics Gap → Genie + Platinum Layer Business analysts in supply chain and finance were fully dependent on the data team for any ad-hoc analysis. By building denormalized Platinum tables optimized for reporting and exposing them through Genie Spaces, we enabled self-service querying in natural language. Early adoption has reduced ad-hoc report requests to the data team by roughly 30%, freeing up engineering capacity for new feature development.

Cost Visibility → Right-Sized Compute We were over-provisioning clusters because we had no clear view of actual utilization. By standardizing on Standard_D4ds_v5 nodes with environment-specific worker counts (1 for dev/QA, 3 for production) and single-user security mode, we reduced our monthly compute spend by approximately 25% compared to the shared cluster model we ran in V1. Review collected by and hosted on G2.com.

One downside of Snowflake is that costs can rise quickly if usage isn’t monitored closely. The pricing model can also feel a bit complex to understand at first. In addition, there’s a learning curve for new users, especially when it comes to optimizing queries and managing warehouses efficiently. Review collected by and hosted on G2.com.

Snowflake helps us manage and analyze large volumes of data efficiently without having to worry about the underlying infrastructure. We can scale resources up or down on demand and run multiple workloads in parallel without running into performance issues. As a result, our data processing is faster, maintenance takes less effort, and we’re able to make quicker, data-driven decisions. Review collected by and hosted on G2.com.

One thing I dislike about Atera is that some advanced features feel limited compared to more complex RMM platforms. Occasionally the interface can feel a bit slow when managing a large number of devices. However, overall the platform still delivers great value for the price and continues to improve with updates. Review collected by and hosted on G2.com.

Atera helps me solve the challenge of managing multiple clients and devices efficiently from a single, centralized platform. With it, I can monitor systems in real time, automate patch management, and provide remote support quickly without having to juggle multiple tools. As a result, downtime for my clients is reduced, my response times improve, and my overall productivity increases. It also makes it easier to scale my IT services in a more controlled way while keeping costs predictable. 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.

The product interface feels a bit clunky and never quite achieves a “premium” or “modern” aesthetic. That said, it isn’t a major issue, since the interface is mainly used by administrators.

Out of the box, there’s an intimidating wall of tasks that need to be set up and configured before everything is running smoothly. This includes patch management settings (auto-approving and deploying with test groups), OS deployment (creating a golden image and configuring networking for PXE boot), and software configuration plus the self-service portal. The latter has been significantly streamlined over time thanks to the addition of thousands of templates that Manage Engine have built out since we first implemented the solution. Review collected by and hosted on G2.com.

Endpoint Central boasts an incredibly versatile toolset that addresses more thzn I can fit into a single review. It completely automates and streamlines patch management, software deployment and self-service, provides end-user workstation support and toolsets (remote capabilities, remote command line, inventory details and reporting, and more), OS deployment and configuration, etc.

For the purpose of this review I'll hone in on the end to end deployment and configuration of workstations, which has undoubtedly saved our organisation hours of otherwise menial and repetitive configurations and management.

Using the OS deployment tool, we are able to apply a golden image that already has the Endpoint Central agent installed - using this we can deploy to a blank machine with the major prerequisites already in place; i.e agent installed, domain joined, and an administrator account already pre-loaded for any situations in remote support where cached credentials may be required (Think domain-trust issues and such). This deployment allows the workstation to be already named and loaded into a specific OU in active directory.

The task configurations can vary from browser configurations, software deployments, certification installs, firewall settings, registry changes and more; but most importantly these can be grouped together into custom task configurations that can be targeted based on criteria such as the OU that the PC is based in. This allows us end to end configuration and complete targeted build of the workstation, bar the provisioning of the end-user profile, and frees up what would otherwise be hours of turnaround for staff onboarding/offboarding configurations. 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.

While SAS Viya is powerful, the transition from traditional SAS 9.4 can sometimes feel challenging, especially when certain procedures or workflows do not behave exactly the same way. Some advanced features require additional configuration or licensing, which can limit access depending on the setup. In addition, for new users, the interface and ecosystem can feel a bit overwhelming at first, particularly when navigating between different applications (e.g., Visual Analytics, Model Studio, programming). A smoother learning curve and clearer guidance for users migrating from earlier SAS versions would be helpful. Review collected by and hosted on G2.com.

SAS Viya helps solve the challenge of analyzing large, complex datasets efficiently while maintaining reproducibility and transparency. It provides scalable computing power, which is especially valuable when working with mixed models, high-dimensional data, or collaborative research projects. The integration of visual tools with programming also streamlines the workflow from data management to modeling and reporting. For me, this improves efficiency in both research and teaching, allowing me to demonstrate advanced methods clearly while handling real-world datasets more effectively. Review collected by and hosted on G2.com.

One of the things I dislike about Tines is that when working with run script events, I really miss having a more advanced integrated development environment (IDE). I understand that Tines is not intended to function as a traditional development tool, but having a more robust script editor would be very helpful when building more complex workflows. Features like autocomplete, more advanced syntax highlighting, or better error handling could significantly enhance the experience of writing and debugging code directly within the platform Review collected by and hosted on G2.com.

Tines helps us solve the challenge of integrating various tools and platforms into one seamless workflow. We use Tines to connect our messaging platform with other tools, allowing us to centralize communications and actions. For example, we can create comments in external tools directly from our messaging platform, ensuring that everything stays in one place. Additionally, we automate task creation for our teams, assigning tasks through Tines by simply filling out a form. One of the biggest benefits is the ability to generate PDF reports from our tools automatically, eliminating the need for manual report creation.

Tines has also enabled us to integrate and automate workflows between tools that don't have native integrations. This flexibility has significantly improved our efficiency across multiple areas of our work, allowing us to streamline processes, reduce manual tasks, and improve overall productivity. 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.

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.

I’ve been using Demand Tools for over a year, but recently, as a Salesforce developer/Admin, I noticed a major gap when comparing it to Salesforce Inspector. In Salesforce Inspector, we can perform all DML operations directly, and it automatically detects the primary key while mapping data. It also displays detailed errors next to each individual record during operations, making troubleshooting easier. In contrast, Demand Tools still only shows an overall error summary, without record-level error details — a feature I believe it’s still missing. I dislike about this implementation missing in Demand Tools. Review collected by and hosted on G2.com.

Demand Tools makes bulk data work in Salesforce so much easier. I use it for updates, uploads, and cleaning up duplicates, and it’s saved me a ton of time compared to other tools like data loader. It’s faster, less error-prone, and takes a lot of the hassle out of managing large data sets. 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.

If not configured correctly, settings applied through Nerdio may conflict with scripts, registry entries, Group Policy Objects, or other policies. While this is largely a user-related issue, having visibility into any downstream overrides would be an extremely useful feature. Review collected by and hosted on G2.com.

Nerdio offers robust yet straightforward configuration options for auto-scaling, which help boost performance and lower costs. The intuitive tools make it easy to automatically stop and start resources that aren't needed around the clock, using different demand criteria. In our experience, this has led to Azure cost reductions ranging from 15% to as much as 80%, depending on the specific use case. 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.

There isn't anything for me to bring out. Review collected by and hosted on G2.com.

Overcame poor confidence in data and even worse - no understanding of sets of data. Review collected by and hosted on G2.com.