Report Writing Software

Overall, I had a very positive experience with Canva, and I didn't face any major issues. The only minor drawback is that it can sometimes take a little time to find specific icons, symbols, or design elements because of the large library available. However, once you locate the right assets, the design process is smooth and efficient. This is a small inconvenience and doesn't affect the overall user experience. Review collected by and hosted on G2.com.

Canva helped me create professional-looking graphics quickly without needing advanced design skills. It saved me time by providing ready-made templates, making it easier to create social media posts, marketing materials, and visual content efficiently. Review collected by and hosted on G2.com.

It's a breeze to set up and works well with our current infrastructure, SQL databases, cloud data warehouses like Snowflake and BigQuery, and even SaaS apps like Salesforce. Review collected by and hosted on G2.com.

Tableau runs on a scalable, n-tier client-server architecture, which includes Tableau Desktop, Server, and Cloud. Its unique VizQL Server converts drag-and-drop operations into a visual query language, and produces interactive visualisations on the fly. The Hyper Data Engine is another high-performance in-memory data engine that further optimises data processing, whether it's live from a variety of data sources or by using in-memory data extracts for speed. Tableau Pulse gives us insights and anomaly alerts that are tailored to the individual, and Einstein Discovery introduces real predictive modelling to our toolbox, making these more than just add-on features. Review collected by and hosted on G2.com.

I don’t really have any major complaints, but there is definitely a learning curve—especially once you start working with DAX, data modeling, and more advanced reports. Building simple dashboards is straightforward, but creating more complex reports takes some practice and time to get comfortable.

At times, refreshing large datasets can take a while, depending on the data source. Also, the desktop version includes so many options that new users may initially find it a bit overwhelming.

Aside from these minor points, it’s been a reliable reporting tool for me. Review collected by and hosted on G2.com.

It helps turn large amounts of data into information that’s easy to understand. Rather than manually building reports in Excel or putting together presentation slides every time, I can create dashboards that update automatically and clearly highlight trends, progress, and key metrics.

For a QA or automation team, it’s especially useful for showing sprint progress, automation coverage, execution status, defect trends, and other project metrics in a visual format. This makes sprint reviews and management updates much smoother, because everyone can quickly grasp the current status.

The biggest benefit for me is the time savings and better decision-making. Instead of spending hours preparing reports, I can focus on analyzing the results and discussing the next steps. Review collected by and hosted on G2.com.

The visualizations in Looker feel a little dated, as they have not kept up with the options provided by competitors for visualizing data. There is very little to no AI functionality out of the box, which feels like a miss. The pricing for our use cases grew significantly from 2022 onwards, increasing from mid-5 figures to 6-figures in GBP per year. Review collected by and hosted on G2.com.

Looker scales data transformation to 3500 users, ensuring centralized governance. The LookML semantic model allows intuitive drag-and-drop report creation, making dashboard embedding easy and enabling complex analyses like currency conversion seamlessly. Review collected by and hosted on G2.com.

While Databricks has been reliable for our data engineering workloads, there are a few areas where I think it could be improved. One challenge I've experienced is cluster startup time. When I only need to test a small code change or validate a transformation, waiting for a cluster to start can interrupt the development flow. It's not a major issue for scheduled production jobs, but during active development and debugging, those extra minutes add up.

Another limitation is cost management. Since compute resources are tied to cluster usage, it's important to monitor cluster configurations and ensure they are shut down when not needed. We've had situations where development clusters remained active longer than expected, resulting in higher cloud costs. The platform provides tools to manage this, but it still requires teams to establish good governance and usage policies. I also found that some configuration settings for jobs, permissions, and clusters have a learning curve, especially for new team members who are unfamiliar with the Databricks environment.

From a day to day perspective, debugging distributed Spark jobs can sometimes be challenging. While the logs provide useful information, identifying the exact cause of failures often requires navigating through multiple execution logs and Spark UI details. For straightforward issues this isn't a problem, but troubleshooting more complex pipeline failures can take time. Despite these limitations, none of them outweigh the benefits of the platform, and most challenges can be managed with proper cluster configuration, monitoring, and team practices. Review collected by and hosted on G2.com.

Databricks has helped address one of the biggest challenges in our data engineering workflow: processing and transforming large volumes of data efficiently. Before the data reaches reporting or downstream applications, we need to ingest data from multiple sources, apply business rules, clean inconsistent records, and create curated datasets. Databricks provides a single platform where we can develop, test, and run these data pipelines using PySpark and SQL instead of managing multiple disconnected tools. This has made our development process more consistent and easier to maintain.

A practical example is one of our daily ETL pipelines that processes data from different source systems before loading it into curated tables for reporting. We use Databricks notebooks during development to validate transformations on sample data and then schedule the same logic as production jobs. If a pipeline fails, the job history and execution logs help us identify the stage where the failure occurred, making troubleshooting more efficient than manually tracing scripts across different servers. Having notebooks, job scheduling, and cluster management in one platform has reduced the effort required to manage these workflows.

From a business perspective, the biggest benefit is faster availability of reliable data for reporting and analytics. Our team spends less time managing infrastructure and more time implementing business logic and improving data quality. While optimizing Spark jobs and monitoring cluster costs still require attention, Databricks has streamlined our daily workflow by providing a scalable environment for developing, testing, and running data pipelines. This has improved collaboration within the team and made it easier to deliver data that downstream users can trust. Review collected by and hosted on G2.com.

There’s a bit of a mindset shift in how you approach analysis. Instead of building queries from scratch each time, you rely more on the pre-defined models and structures.

Once you get used to that way of working, it actually makes analysis much more straightforward across the team. Review collected by and hosted on G2.com.

Our biggest challenge was working with large volumes of data without slowing down analysis. Queries used to get expensive and time-consuming, especially when multiple people were exploring data at the same time.

With Kyvos, we’re able to interact with data much more freely. It’s easier to explore trends, compare performance, and answer follow-up questions without worrying about query delays. Review collected by and hosted on G2.com.

The design side has a few limitations. The basic charts and cards look clean, but getting everything to align with our exact brand guidelines or achieving tighter control over layouts, can be frustrating. I often end up making tweaks in Figma and then going back to adjust things again in Domo. Also, once you have a lot of DataFlows and datasets (we’re over 70 now), the workspace can start to feel cluttered unless you’re very disciplined about naming conventions and archiving. Review collected by and hosted on G2.com.

It’s really helped bridge the gap between the design and data teams. I no longer feel like I’m constantly waiting on someone else to make the data usable, and that’s made my design iterations faster and let me work more independently. Review collected by and hosted on G2.com.

While Agency Analytics offers a solid range of integrations, I'd love to see even more options available. Given their current offerings, I'm not particularly optimistic that requesting a new integration would yield results. It's worth noting that what they do have is still quite impressive, though. Review collected by and hosted on G2.com.

Agency Analytics has streamlined my marketing reporting workflow. The platform significantly reduces the time I spend on reporting while enabling me to demonstrate my expertise to clients with polished, professional reports. Data syncs seamlessly from other platforms in real time, and the intuitive interface requires minimal effort to navigate. Their AI-powered insights feature accelerates my analysis process, allowing me to make data-driven decisions quickly and confidently. Best of all, the competitive pricing combined with responsive, knowledgeable support makes it an exceptional value. Review collected by and hosted on G2.com.

Honestly, there’s not much to dislike. If I had to mention anything, it’s that building out complex dashboards can be a bit involved at first, especially with lots of moving parts like paid and organic social. But the support team makes this process much easier and is always available to help. Review collected by and hosted on G2.com.

Helping visibility of data 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.

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.

One thing I don’t like about Alteryx is that it can be expensive, especially for smaller teams. It also takes time to learn all of its more advanced features, and when workflows get large, they can sometimes become difficult to manage. Aside from those drawbacks, it has been a reliable tool for data preparation and analytics. Review collected by and hosted on G2.com.

Alteryx helps me solve the problem of manual, time-consuming data preparation. It makes it straightforward to clean, combine, and transform data from different sources all in one place. This saves me a lot of time, reduces errors, and lets me get to the analysis much faster. As a result, I can spend more time finding insights instead of spending hours just preparing the data. Review collected by and hosted on G2.com.

The biggest drawback is performance with large-scale analyses. While Hex handles most workflows well, resource-intensive notebooks and large queries can occasionally lead to slower execution and require optimization. Some advanced collaboration, governance, and enterprise features are only available on higher-tier plans. Customizing highly interactive dashboards is not as flexible as in dedicated BI tools. Review collected by and hosted on G2.com.

Hex solves the challenge of bringing data exploration, analysis, visualization, and collaboration into a single platform. Instead of switching between SQL editors, Jupyter notebooks, BI tools, and presentation software, Hex provides one workspace where technical and business users can work together. Combines SQL, Python, visualizations, and dashboards in one environment. Simplifies collaboration by allowing multiple team members to work on the same project. Makes it easy to create interactive reports that stakeholders can explore without technical knowledge. Connects directly to cloud data warehouses and databases for real-time analysis. Reduces the effort required to move from raw data to shareable business insights. In my workflow, Hex helps streamline data analysis and reporting by keeping everything in one place. I can query data, perform Python-based analysis, build visualizations, and share interactive dashboards without exporting results across multiple tools. The biggest benefit is improved collaboration and faster decision-making. Hex shortens the analytics workflow, making it easier to deliver insights, collaborate across teams, and turn data into actionable outcomes more efficiently. Review collected by and hosted on G2.com.

Some corners of the UI/UX still have room to improve. A few of the admin and settings screens feel denser than they need to be, and there’s a learning curve in places. That said, they’ve taken big, visible steps over the last year—a refreshed dashboard UI, themes, and a much cleaner AI chat experience—and the trajectory is clearly moving in the right direction.

With AI, you still need to double-check the results, since it can sometimes get things wrong. However, they make it easy to validate during conversations that the AI has computed the right results. Review collected by and hosted on G2.com.

We moved to Knowi from a well-known cloud BI platform. The previous tool required pushing all of our data into its own store before we could visualize anything, which meant ETL pipelines to maintain, sync lag, duplicate storage costs, and our NoSQL data getting flattened along the way.

With Knowi, we unified analytics across all of our datastores without moving the data and launched customer-facing embedded dashboards much faster. Row-level security and secure embeds ensure each customer sees only their own data. The semantic layer keeps metrics consistent across every surface. And with the new AI and agentic capabilities embedded in our product, customers can self-serve answers that used to come in as support tickets and custom report requests, so our team can spend its time on the product rather than on one-off reporting. Review collected by and hosted on G2.com.

Honestly I don't see any so far: If at all, it requires relatively high technical skill. It cannot be used effectively by a "plug-n-play" BI Analyst.

If the price is an issue, one can consider self-hosting. Review collected by and hosted on G2.com.

I started off liking Tableau as a BI tool better than PowerBI.

Then, I could see the compute downsides and missing git repo in Tableau compared to Looker.

Then, I can see the issue that Looker is very expensive and that Looker is not dbt integrated, i.e. you constantly need to maintain two disconnected repos and make changes in two places.

Lightdash is by far the best BI tool out there today. Review collected by and hosted on G2.com.

Honestly, I can't find any faults with it at all; everything seems to be working perfectly without any issues. The quality and performance have exceeded my expectations, and I have thoroughly enjoyed the experience. It's rare to come across something that meets all my criteria so well. Overall, I'm genuinely impressed and satisfied with it. Review collected by and hosted on G2.com.

Customer service for my clients. I have all of my clients weekly SEO reports come to my inbox and it allows me to take actions when needed. My clients also love being kept in the loop. Review collected by and hosted on G2.com.

I would love to see certain Instagram features like daily likes and sometimes I find that certain data points that clients are asking for are not available. Review collected by and hosted on G2.com.

I use Swydo for client reporting and analytics allowing us to avoid extra phone calls. It integrates various platforms, servicing clients well. Setup is quick and reports are timely, with shared links that don't change. Review collected by and hosted on G2.com.

The manual could be in Portuguese to facilitate learning and searching for information. Review collected by and hosted on G2.com.

I use dataPARC for quick and automatic response, didactic data visualization with dashboards and email alerts, support in data comparison, and monitoring of the production process. Review collected by and hosted on G2.com.

local data agent doesen't necessarily supoort the lateset JRE builds Review collected by and hosted on G2.com.

It helps us create a single source of truth for all of our disparate information sources. Review collected by and hosted on G2.com.

Gets a bit laggy when workbooks have a lot going on lots of elements, big datasets, that kind of thing. Some of the advanced features aren't super intuitive at first, you kind of have to figure them out on your own. Not a dealbreaker but could be smoother. Review collected by and hosted on G2.com.

We were spending way too much time building one-off reports for people. Now with Sigma, teams can build their own dashboards and trackers we use it for migration tracking, client project reporting, and internal operations tracking. The Snowflake connection was easy to set up and stays reliable, so everyone has access to real-time data whenever they need it. People don't need to come to us for every little thing anymore, saves a ton of time. Review collected by and hosted on G2.com.