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How to Analyze Data with Metabase? A Comparison To 5 Most Popular Analytical Services

How to Analyze Data with Metabase? A Comparison To 5 Most Popular Analytical Services
Hostman Team
Technical writer
Infrastructure

What is Metabase? How to connect it to your database and use it for your analytics? What are the most popular Metabase alternatives and how do they compare? Read this article to find out everything about Metabase.

What is Metabase and how does it work?

Without the right tools, a database can be as impenetrable as a medieval dungeon crawling with carnivorous spiders.

But fear not, brave adventurer — Metabase is here to help you find and unlock all of the riches in your database.

With its intuitive UI, Metabase is your master key to accessing, presenting, and analyzing everything that lives inside your databases. Metabase is the open-source application that unlocks the full potential of your databases, allowing you to access, search, and share data in the easiest way possible. 

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It is like having an intelligent, proactive and efficient digital analyst who’s always on the alert, and who can help you process and retrieve any of your data quickly and painlessly.

The simple and intuitive UI makes it possible to query even the tiniest piece of data in your database. More importantly, it presents the information in a clear and understandable way, so that you and your team can get the full benefit from the results of your query.

What makes Metabase such a popular tool?

I.T. professionals are known for their logical and analytical thinking. So when they get excited about something like Metabase, you can bet they have solid arguments to back it up.

Here are just a handful of features that have made Metabase the tool of choice for so many I.T. professionals:

  • Advanced query system that is equally effective with generic searches and laser-targeted database interrogations. Accessing data is as simple as asking a question about anything in your database. The Metabase query builder will serve up information you need in a way that is easy to digest for both analysts and non-technical users.

  • One-time-setup automated report generation. Metabase will automatically create reports about data changes in your database. Set it and forget it.

  • Intelligent tracking of important data changes with alerts. Set up alerts to keep owners up to date on changes in key data for which they are responsible.

  • Charts and dashboards that are as useful as they are visually appealing. With a strong focus on UI and UX, Metabase excels at presenting data and changes in a style that is clear and immediately understandable.

  • Craft dedicated embedded analytics. Metabase can also be used very effectively as a full-fledged data collector and manager for your clients.

How to set up Metabase

Before you can start working with Metabase, you need to follow a simple deployment and setup procedure.

Here’s everything you need to know.

Deploying

There are many ways to launch Metabase on your production platform.

The simplest way is to use cloud services that automated all the processes. All you need to do is to sign up to the service, select Metabase, and it will create an instance of the application on a fast and reliable server. Next, you will need to configure Metabase after the deployment is over.

Another way to install Metabase is to use the dedicated JAR file:

  1. Download the file from the official Metabase website

  2. Run the following command: java -jar metabase.jar

Alternatively, you can use the Docker image of Metabase If you’re used to working with containers.

There are also other methods for running Metabase. You can find them in the official Metabase documentation.

Setting up

Once you’ve set up Metabase on your server, you’ll be able to access it via localhost:3000.

Just open that address in your browser to begin.

Metabase will ask you to create an admin account. You’ll need to insert the standard personal details — name, last name, email, password, etc.

The next step is to connect your database. To do so, you’ll have to specify:

  • the hostname of the server with the database

  • the port to connect to the database

  • the database name

  • the username and password for accessing the database.

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And, that’s it. Once you’ve connected your database, you can check out the Metabase interface and start exploring all of its exciting functionality.

How to ask Metabase questions

Asking Questions is a key element of the Metabase system. It is like “queries on jet fuel” for your database software.

As an analyst, formulating the right Metabase Questions will be one of your main activities. They are the tool that will help you extract all of the important insights from the data you’re inspecting. While Metabase Questions are extremely powerful, creating them is an incredibly simple and intuitive process.

Let’s say you have a table with order data. It contains columns for subtotal, tax, total, etc., and you want to find all the orders with a tax of more than 5 dollars.

Using the filter system, you can ask Metabase to check the orders table for how many rows there are with a tax exceeding 5 dollars. To do this, you click on the Filter button, choose a column, choose the criteria to filter, and then click “Add filter”. Next, you might want to use the “Summarize” option to add up all of the rows with a tax of more than 5 dollars.

Filters in Metabase allow you to pick out the necessary data and get direct answers to your questions.

To help you get the most out of Metabase, we’ve prepared for you an in-depth Metabase query syntax tutorial.

How to visualize data

Presenting your data in a way that is appealing and easy to digest, is one of the key features of Metabase. All of the numbers, columns, rows, and cells are organized in a logical manner to facilitate understanding and data-driven decision-making.

Both visualization tools in Metabase are optimized for analyzing and monitoring any volume of data.

Creating charts

Metabase Charts depend heavily on the questions you ask. You can use built-in query editors to visualize data as charts.

To create a chart, you need to choose the “Visualize” option. Next, you’ll be able to choose one of the chart views that will present the data it gets from the question you ask. Finally, you will need to formulate the question.

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Let’s say you have an orders table with various categories of goods that your company sells. You can ask Metabase to filter some categories, summarize their performance characteristics and Visualize as a histogram.

Most importantly, you’ll be able to drill deeper into the data presented in your chart. You can click through to find exactly the number you need, and zoom in to get closer to the information around a certain period of time, or vice versa.

Creating dashboards

Business intelligence dashboards help you monitor the outcome of your actions so that you can make informed decisions about the further development of your company or product.

Dashboards are visually similar to charts. However, instead of focusing on a few specific elements, dashboards allow you to present an array of different types of information in different visual forms on one screen. This approach helps to monitor sensitive performance indicators on one screen. Metabase dashboard filters will help out on this task. And all the data in a dashboard will always be up to date.

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In Metabase, you can find many ready-made dashboards for efficiently presenting different data collections. These dashboards are made by other Metabase users. And since they’re based on real-world scenarios, you’re likely to find something that closely fits your use case in no time.

Metabase API

There are many platforms out there that are great at what they do, but fail miserably when it comes to integrating with your environment.

That’s why Metabase comes with its own API for integrating its features into other products.

The API allows you to ask for any data that is passed through Metabase via a different application. You can also create custom queries and pass them into Metabase by means of the API.

Moreover, developers can use curl requests to set users, groups, and permissions; even generate reports.

You’ll find a ton of API use cases in the official Metabase documentation.

How does Metabase compare with similar top industry solutions?

Metabase is a great tool but it’s neither the first nor the only one of its kind.

There are many other business intelligence tools that help businesses collect and analyze data. But Metabase isn’t afraid of competition. In fact, in the next section, we’re putting Metabase toe-to-toe with some of the best, most powerful and most popular data analysis platforms.

Punches will fly, but you’ll find that Metabase puts up a strong show of force.

Metabase vs Tableau

These two platforms have a lot in common. Both were created for the purpose of presenting a large amount of data via the most visually comprehensive tools.

Tableau launched in 2003. By 2021 it had earned the trust and admiration of many businesses.

By comparison, Metabase is a relatively recent addition to the scene. While it doesn’t have the huge exposure and reputation that Tableau has built over the years, Metabase has the advantage of having been built on the lessons learned from other platforms (including Tableau).

You could say Metabase stands on the shoulders of giants, but reaches higher because of that.

Metabase vs Superset

Superset is a free alternative to Metabase. It is a quite popular tool made by developers of Airbnb and now belongs to Apache. It is open source too and in many cases functionally similar to Metabase.

People love Superset due to its easy migrating system. If you’re migrating to Superset, the process is painless and straightforward.

Superset users are particularly fond of a feature called “Time Dimensions”, which allows you to monitor data from several time segments without having to update the whole dashboard at the same time.

While it’s a brilliant tool, Superset suffers in the documentation department. This becomes a real problem when dealing with some of the more advanced or obscure functionality.

On the flip side, Metabase boasts clear and detailed documentation. More importantly, we’ve placed huge emphasis on UI/UX, to the extent that most functions can be performed without spending too much time digging through documentation. Metabase’s easy query system and intuitive charts and dashboards have won over many users from Superset, simply because they were tired of all the guesswork.

Metabase vs Redash

One of Redash's main claims to fame is that it supports JSON files as a data source. In other words, it can be connected to NoSQL databases like MongoDB, which many users consider an asset.

Metabase and Redash also have a number of useful features in common, such as the “Query Snippet” function, which helps to create reusable bits of SQL queries to quickly recreate requests to the database.

In Redash it is easy to set up query parameters. Therefore, it is simpler to pass the arguments and data sources into SQL and NoSQL requests.

Unfortunately, Redash falls short when it comes to the visual element of the applications. In a side-by-side comparison, you’ll see that Metabase’s charts and dashboards are much better presented and more informative (hence, more useful) than the ones that Redash provides.

Metabase vs Looker

True to its name, Looker is a very well-presented tool that is loved by thousands of users. Its main focus is data modeling and it is actually good at it.

Metabase is also very good at data modeling. In fact, Looker and Metabase have a lot of strong points in common. Where Metabase outclasses Looker, is in performance. Put the two head-to-head and you’ll find Metabase much faster and more comfortable to use.

Many Looker users love it because of its LookML language — a proprietary syntax that is used to pass queries to databases. It has quite a steep learning curve, but many businesses consider it to be the most powerful and efficient way to work with a large amount of information. Unfortunately, it’s also pretty expensive.

By comparison, Metabase is free as long as you host it yourself, and still brings very powerful features bundled with a well-designed UI/UX.

Metabase vs Power BI

Power BI is Microsoft's business intelligence tool, created for those who primarily work within Microsoft’s ecosystem.

It is a feature-rich and massive product, but its power comes with an equally steep learning curve. As a result, the product is very hard to penetrate, which means that most users will rarely be able to get the full benefit of its powerful features. Just getting Power BI up and running is a mammoth of a task, requiring a considerable investment in time, effort and money to get it to work efficiently.

Just like many other Microsoft products, Power BI has its niche of users for whom it's an excellent fit. But it’s definitely not for everyone.

On the flip side, Metabase was designed with a very low barrier to entry. The intuitive UI makes it easy to deploy and start using within minutes. And of course, it’s not lacking in powerful features either.

The best way to try out Metabase

Metabase is a powerful tool that will dramatically change the way you work with databases. But you shouldn’t take our word for it. That’s why we recommend that you try out Metabase for yourself and come to your own conclusions.

How do you do that?

With Hostman.

As part of its suite of hosting services, Hostman has just launched a Marketplace where administrators and developers can find a variety of tools such as OpenVPN, Docker, Metabase and many more, which can be deployed in one click.

All you have to do is:

  1. Visit the Metabase page in the Hostman Marketplace.

  2. Click “Deploy to Hostman”.

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Nothing else is necessary.

You won’t need to download Java and JAR files, or create Docker containers. Everything will be set up for you. 

The Hostman Marketplace also carries loads of other exceptional tools that you can easily deploy and use. You can try any of them for free for 7 days. And if you like what you see, you can continue to use it for just 5 dollars per month.

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PostgreSQL and MySQL are among the most popular relational databases. In this article, we will examine the functional differences between them and compare their performance so that you can choose the database that is suitable for your business. PostgreSQL vs MySQL Despite the increasing similarity in features between PostgreSQL and MySQL, important differences remain. For example, PostgreSQL is better suited for managing large and complex databases, while MySQL is optimal for website and online-application databases because it is oriented toward speed. This follows from the internal structure of these relational database systems, which we will examine. Data Storage in PostgreSQL and MySQL Like any other relational databases, these systems store data in tables. However, MySQL uses several storage engines for this, while PostgreSQL uses only a single storage engine. On one hand, this makes PostgreSQL more convenient, because MySQL’s engines read and write data to disk differently. 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It supports different index types, allowing work with multiple data types. This database also supports regular expressions in queries. However, new fields in PostgreSQL can only be added at the end of a table. Parallel data processing is better organized in PostgreSQL because the platform has a built-in implementation of MVCC (multiversion concurrency control). MVCC can also be supported in MySQL, but only if InnoDB is used. Concerning replication, PostgreSQL supports logical, streaming, and bidirectional replication, while MySQL supports circular replication as well as master-master and master-standby. Replication refers to copying data between databases located on different servers. PostgreSQL and MySQL: Performance Comparison Testing is fair only when comparing two clean, “out-of-the-box” systems. Indexed testing provides the following results: Insertion: PostgreSQL is more than 2.7× faster, processing a 400,000-record database in 5.5 seconds versus 15 seconds for MySQL. 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PostgreSQL and MySQL Comparison For clarity, the main features of both systems can be presented in a table:   PostgreSQL MySQL Supported OS Solaris, Windows, Linux, OS X, Unix, HP-UX Solaris, Windows, Linux, OS X, FreeBSD Use cases Large databases with complex queries (e.g., Big Data) Lighter databases (e.g., websites and applications) Data types Supports advanced data types, including arrays and hstore Supports standard SQL data types Table inheritance Yes No Triggers Supports triggers for a wide range of commands Limited trigger support Storage engines Single (Storage Engine) Multiple As we can see, several features are implemented only in PostgreSQL. Both systems support ODBC, JDBC, CTE (common table expressions), declarative partitioning, GIS, SRS, window functions, and many other features. Conclusion Each system has its strengths. MySQL handles horizontal scaling well and is easier to configure and manage. However, if you expect database expansion or plan to work with different data types, it is better to consider implementing PostgreSQL in advance. Moreover, PostgreSQL is a fully free solution, so companies with limited budgets can use it without fear of unnecessary costs.
24 November 2025 · 6 min to read
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How to Speed Up Development with Cloud Technologies

From the point of view of a software-development company, the computing infrastructure needed for developing and supporting software can be of two types: Servers owned by the development company The application developer physically hosts the server equipment directly in their office or data center, maintains it themselves, and ensures data security. This approach has become less popular over the years. Developers prefer using a provider that offers computing power on its own equipment on a paid basis. Advantages: Full physical control over the servers High data security and confidentiality Disadvantages: Financial costs for server hardware Financial costs for maintenance (specialists, equipment) Need for experience and expertise to support data centers Servers managed by another organization The application developer chooses a provider from whom they remotely rent computing resources. Today, this is the most optimal option for most companies. 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There are also cases when the cloud provider offers isolated server resources for a specific client, while other users use the public part of the cloud. Hybrid A combination of a private cloud and a public one. Usually, business logic containing sensitive data is placed in the private cloud, while “peripheral” computing is performed in the provider’s public cloud. Advantages of Cloud Technologies Simplified Development Using any of the existing cloud models clearly simplifies the development process. The closer a chosen model is to SaaS, the easier it is to interact with the cloud, but the more standardized and typical the solution becomes. The closer a chosen model is to IaaS, the more precisely the cloud can be configured, but the more complex it becomes to maintain. Therefore, the cloud model is selected individually for every case. A standardized solution may not fit someone if the product being developed requires a more unique technical implementation. For example, using cloud databases (DBaaS) provided by Hostman removes many responsibilities from the developer regarding managing their own storage. While deploying a database cluster can be done fairly quickly and easily, using it properly “in the long run” requires certain expertise. Thus, to stay focused on development, it makes sense to entrust this function to an experienced provider with established methodologies, expertise, and the necessary technical solutions. Cost Optimization In the cloud, user expenses match the amount of resources used in their work tasks. This payment model, "pay-as-you-go", is fundamental to cloud computing: you pay only for the resources you use. If, at some point, more resources are needed, they are allocated quickly and increase costs. Conversely, extra resources can be “returned” to the provider, reducing expenses. In other words, the user’s financial expenses correlate with the number of resources consumed. Allocation and release of resources happen instantly. Additionally, delegating cloud-infrastructure management to the provider eliminates the developer’s costs associated with maintaining their own data center: Hiring specialists and administrators Purchasing server equipment Renting premises Faster Deployment Instant access to cloud resources and the ability to configure them flexibly significantly reduces the time: From the start of MVP development of a new product to its deployment in a production environment From the start of developing new features to adding them to the finished product For example, the development process of a new service might look like this: Managers formalize requirements for the future product. Developers determine the technology stack. The development company rents the necessary computing resources from a cloud provider, including cloud servers, Kubernetes clusters, and object storage. Developers write code, which is automatically deployed in the provider’s cloud environment. The new product is tested, and user feedback is collected. Based on the collected data, managers decide whether further support is justified. If the product does not meet expectations, the allocated resources are returned to the provider. If successful, the computing resources can be expanded. In some cases, a developer may use multiple clouds from completely different providers, forming what is called a multi-cloud. 1. Cloud Server Cloud servers differ from dedicated servers in that the “physical” configuration is adjusted within a certain range. For example, Hostman cloud servers can be parameterized using a configurator. In this case, the rental cost corresponds to the capacities selected in the account panel. Accordingly, when workloads change, a cloud server can be scaled: CPU cores, RAM, disk space, or connection speed can be increased. 2. Cloud Databases In addition to the computing power provided by the cloud server, most projects require a specialized database. Hostman provides most popular database engines as a separate cloud service: MySQL PostgreSQL Redis and others As with servers, computing resources for a database can be configured based on several parameters: Number of cores RAM NVMe disk size Rental period When ordering a database, you don’t need to configure hardware, track software updates, or administer the database: the infrastructure is managed by Hostman. 3. Kubernetes A crucial component of cloud development is automation of building, deploying, and testing using CI/CD pipelines. CI/CD is the basis of the DevOps methodology, whose goal is to automate changes to software. Cloud services provide the needed capabilities for this. For example, GitHub CI/CD and GitLab CI/CD infrastructures have special cloud tools that simplify creating CI/CD pipelines, including test environments, storage, notification functions, and databases. Modern applications consist of microservices placed in containers. Kubernetes, the standard for container orchestration, is used for management and monitoring. Kubernetes clusters from Hostman can be a suitable solution. In this case, the developer avoids the costs associated with maintaining their own infrastructure as the execution of Kubernetes applications is handled by the provider. Conclusion In this article, we reviewed what efficient development using cloud services looks like, and also mentioned the most popular developer tools: Cloud servers Databases Monitoring tools (CI/CD, Kubernetes) While creating your own data centers requires time, expertise, and significant financial investment, the cloud is provided by a provider in a refined and secure form. A ready-made cloud helps developers immediately focus on the business logic of the application, thus speeding up development and integration of innovations. Today, public cloud services are available even in areas that were previously predominantly non-cloud. Clouds are being implemented even in enterprises unrelated to IT. Therefore, cloud technologies not only accelerate development but also accelerate digital transformation as a whole.
24 November 2025 · 8 min to read
Infrastructure

Microservices Architecture: What It Is, Who It’s For, and Tools to Use

Every developer strives to speed up product development while maintaining enough flexibility and confident control over the process. Microservices application architecture helps solve these tasks and, over the past 10 years, has begun actively competing with the traditional monolithic approach. To begin, let’s look at the difference between them. Microservices Architecture vs. Monolith The difference between these two software development approaches is easiest to illustrate with an example. Let’s imagine two online stores: one implemented as a monolith and the other as microservices. A monolithic online store is a single, indivisible structure that combines all components: databases (catalog, customer data), shopping cart, order and payment forms. All of these elements are tightly interconnected and located on the same server. In a microservices system, each component is an independent module that developers can work on separately. And naturally, nothing requires hosting these modules on a single server. Thus, microservices architecture is a kind of constructor that allows you to add new elements painlessly as you scale the application. A monolith, on the other hand, can be compared to a solid wall: scaling here is only possible by adding another identical monolith. It’s worth adding that microservices are sometimes mistakenly perceived as a set of very small services. This is not the case: for example, the database of a large online store may contain millions of records and occupy tens of gigabytes, yet still be just one of the modules within the microservices architecture of the whole application. Comparing Microservices and Monoliths by Key Criteria Now let’s look at the main characteristics of microservices technology in comparison with a monolith and see how both approaches solve the same developer tasks. Release Cycles Development speed and frequency of updates increase with microservices thanks to modularity: changes are made not to the entire codebase but to individual modules. With a monolith, however, the whole platform must be updated first, which increases testing and debugging time. As a result, development slows down and updates are released less frequently. Technology Stack The microservices model offers significantly greater flexibility because each service can be written in its own programming language and may use different libraries and data-storage technologies. With a monolith, the situation is different: changing the technology stack is nearly impossible. Developers are forced to stick to the initial tools. Developer Onboarding Each module in a microservices architecture is self-contained, making it possible to bring in programmers who are familiar with the functionality of a specific service. This substantially lowers the onboarding threshold. With a monolith, new developers must dive into the code of the entire application, understand the functions of every block, and only then begin productive work. Thus, maintaining a monolith is more dependent on specific team members. Optimization Characteristics The modularity of microservices architecture also positively affects optimization, since developers can optimize each service separately. Optimizing a monolithic structure is more difficult because the team must account for links between indivisible blocks, and updating any one of them inevitably affects the entire application. Application Scalability The distributed structure of microservices and their ability to run on separate servers make scaling fast and easy. In monoliths, scaling one component inevitably requires scaling the entire application as a whole. Fault Tolerance Because services are hosted on different servers and have a modular structure, microservices architecture achieves independence of each module. This significantly increases system resilience: a failure in one service does not cause the entire application to fail. With a monolith, the situation is different: all components are tightly interconnected, so the failure of one module can make the entire application inoperable. Do I Need to Switch to Microservices Right Now? As we’ve seen, microservices have advantages in many key areas. But does this mean you need to abandon monoliths as an outdated approach and immediately switch to microservices? The answer depends on the current state of your project. And let’s say right away: rushing to adopt microservices is not always the right choice. Distributed architecture also has its drawbacks. First, microservices require ensuring network connectivity between modules. If a network connection is unstable, this leads to delays and data inconsistencies, which create potential problems in application behavior. Second, each module of a microservices system requires separate testing and health monitoring. Additionally, you will need to allocate cloud resources for each module, which may increase costs. Third, with the microservices approach, teams responsible for different modules may encounter interaction issues. This means you may need a connecting link in the form of DevOps specialists who can streamline collaboration and speed up development. All the factors listed above allow us to conclude that the transition to microservices must be timely. Usually, during the early stages of a project, this is unnecessary, especially if developers have limited human or financial resources. Switching to a microservices architecture makes sense when there is a clear need for significant scaling, and scaling a monolith has already become difficult. Microservices may be right for you if: You have a large team. In this case, it makes sense to divide the team into separate, independent groups, each responsible for its own service; You have a complex, branched application. In this case, it’s far more convenient to update and maintain modules separately than to rebalance the entire system each time; Your application traffic is highly variable. For example, you see sharp spikes in load during certain periods. Microservices' distributed structure allows quick scaling during peak loads, after which you can easily return to normal capacity levels; Your application is frequently updated. Working with separate modules in this scenario is much simpler, and new releases will be significantly faster. If your project meets at least one of these criteria, this is a reason to consider breaking it into independent elements. However, if your application is relatively small and does not require frequent updates, it is reasonable not to rush away from monolithic architecture. Useful Tools for Organizing Microservices A modern development approach requires a containerization platform. In most cases, developers use Docker for these purposes. Docker tools allow them to isolate the application from the infrastructure, meaning they can work with it equally well locally or in the cloud, which is very convenient for development. Once containers become numerous, an orchestrator becomes essential for managing and organizing groups of containers. Kubernetes is most commonly used as an orchestrator due to its strong compatibility with Docker. Another necessary tool is a load balancer, which ensures even distribution of network traffic across all cloud resources. This significantly increases the application’s fault tolerance.
21 November 2025 · 6 min to read

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