Show HN: Metabase, an open-source business intelligence tool

I've done some minor stuff in MS PowerBI and PowerQuery/PowerPivot that I think meets your need. It's pretty nifty.

https://powerbi.microsoft.com/ http://www.powerpivotpro.com/what-is-power-pivot/

Some BI tools can connect to multiple databases, however the right solution is to put all your data into a single data warehouse and run the analysis off of that. Query performance is one reason, and here's a blog post I've written with some more:

http://dataducketl.com/blog/every-tech-company-needs-a-data-...

The solution is to use an ETL (Extract, Transform, and Load) tool to copy data to a warehouse.

You then point the BI tool at the warehouse.

There are lots of tools for this already, and even more custom solutions and other things. CSV files tend to rule here... scheduled exports that are picked up by the ETL tools.

The transformations that take place are usually normalisation of data from systems of differing schema or different types of data (perhaps changing strings into corresponding numbers, i.e. Risk Level = Orange could be normalised as 50% in the data warehouse).

Businesses have taken this approach for a long long time as the databases that drive applications, services, and now micro-services have always been tuned for the performance of the production use-case and not for ad-hoc reporting.

I've worked in this space a lot and in my experience, the only true solution is some sort of layer below your reporting tool to aggregate the data. That being said, it's not always a database that physically stores the data. It simply needs to be a semantic layer that models the way in which the data will be queried. Additionally, your OLTP systems are not optimized for reporting and you'll quickly choke yourself out trying to perform complex analysis on data structures attempting to solve a problem for which they weren't designed.

You will have N databases no matter what. Almost every company has some legacy systems around, or simply uses different tools for different jobs.

However, you still want to copy & combine your data into a single database. The relevant Fowler patterns are:

http://martinfowler.com/bliki/DataLake.html

http://martinfowler.com/bliki/ReportingDatabase.html

More precicely, it is a common pattern to split the analtics into three parts: 1) Collecting the data (using lots of adapters) into a "data lake", 2) filter and preprocess that data lake into a uniform data structure whose structure is determined by the analysis goal rather than the operational system, 3) analyze that uniform data with statistical and other tools.

It usually makes no sense to combine those phases into a single overall tool. First, these are very different tasks, where different specialized tools will evolve anyway. Second, you want to keep the intermediate results anyway - for caching as well as to have an audit trail and reproducibility of the results.

For example, you don't want the performance of operational system be affected by for how many analysis tools it is used at a point in point. Also, you don't to work on a always-modifying dataset while fine-tunung your analysis methods.

(Full disclosure, co-founder of Periscope Data here.)

Periscope Data supports cross-DB joins. We cache the data in our own clusters so queries run really fast, and also so you can do things like join across databases and upload CSVs.

More info: http://www.periscopedata.com. I'm harry at periscopedata.com

Tool-level data integration is supported by tools like Spotfire, but for analyzing high volume of data you need to mantain a data warehouse using ETL code to integrate the data from all the diferent sources.

For better performance you may also need to implement an OLAP tool.

So in practical terms, you'll eventually want to bring everything back in and create a analytics DB or data warehouse. Depending on what your schemas look like in each microservice, you might just be able to add each DB into Metabase and hit them up.

We don't do cross DB joins in the query builder. You can still use something like PG foreign data wrappers to create an uber-db with all the tables from each microservice.

Regardless of whether you use MB or another tool, there will be some legwork. Part of the tradeoffs involved in the great monolith vs microservices decision.

This is why data warehousing exists. That way you can use many different BI tools against a consistent set of data and pre-calculate a lot of commonly-used summary data.

ETL also isn't something you can just do automagically. It requires an understanding of the data and your goals, because you essentially have to build both your data model and your reporting requirements into your ETL process. You could probably do it automagically for some simple cases, but for most real-world scenarios it's just going to be easier to write a Python/Perl script to run your ETL for you.

BI / reporting requires a lot of plumbing to work correctly. You have to set up read-only clones of your DBs for reporting (because you don't want to be running large queries against production servers) and generally an ETL process that dumps everything into a data warehouse. From there, you can push subsets of that data out to various BI tools that provide the interface.

This is something we saw at Chartio very early on. It's why we built the concept of layers directly into our product to support cross-DB networked joins:

https://support.chartio.com/quick-start/#layers

Feel free to reach out to me (aj at chartio.com) for more info!

Disclosure, I am a Drill committer.

If you are looking for an open source technology to meet this need, Apache Drill is a distributed SQL engine that can talk to multiple datasources and make them available in a single namespace. Joins across sources are supported and can actually perform well when you use enough nodes for your data volume.

Because Drill exposes ODBC and JDBC interfaces, it can very easily be used with BI tools to expose all kinds of data to analysts.

The architecture is fully pluggable so anyone can write a connector to a new datastore and just include it on the classpath when starting the cluster. In the current 1.2 release Drill can connect to any database that exposes JDBC, like MySQL, Postgres, Oracle, as well as a number of non-relational datastores like HDFS, MongoDB and HBase.

https://drill.apache.org/

lot of bi tools provide connectivity via odbc and jdbc so works with multiple databases at the same time. check http://www.infocaptor.com for e.g, serves both odbc and jdbc, on prem and on cloud

take a look at Apache Calcite: https://calcite.incubator.apache.org/

I agree, the "one database at a time" approach is the way vendors ignore the harder problem of connecting data across platforms.

I used to work for a company that built the "automagic ETL" kind of solution. You could write a single straight SQL statement across virtual "tables" (if you had non-tabular data, say NoSQL, you could query it through a table-like projection). It could literally join data across heterogeneous systems by shuffling data between them or to a dedicated "analytical processing platform" for join processing. Or you could do things like, create a table on one system, as the result of a select from a join of tables on three other systems. At the time, this was way ahead of what anyone else was doing.

However, it is a hard problem to solve, the company is/was small and funding was a problem because it took a long time to find ways to invent the tech. Also, it was an enterprise solution and closed source - when it really probably needed to be open source to be able to support the diversity of data sources.

These days, between Apache Drill, Spark, Ignite, etc., and any number of other commercial solutions, we're starting to see the solutions to solve the problem you're talking about.

I bet this Metabase UI on top of Apache Spark, and your databases, would be a killer. That's a common pattern (BI tool on top of Spark), see Apache Zeppelin for how it uses Spark, for example.

That said, as long as your data isn't truly ridiculously huge - if it can be centralized, centralization still works just fine.

We use SAS Enterprise Guide for this at my work. Handles source data spread across multiple databases.

The non-cool BI tools work alright with multiple sources and integrating them into a common OLAP model that takes care of automating the relevant backend queries. This is a valid approach if you do not want to move all your data into a DW before reporting on consolidated views, but the there is no magic bullet in terms of which model is optimal. I am talking about Oracle BI, SAP BO, Cognos BI.

Thanks for the kind words!

Would love to hear what you think of the codebase.

Perhaps, but many companies have a 'Abosolutely No AGPL' policy which effectively reduces adoption.

Yup, TR has a bioinformatics DB they sell which I think is based on metabase.org. We're in a sufficiently different space that we're hoping it's not a conflict.

We don't explicitly support snowflake schemas but folks are definitely using MB with them them. We have a limited set of joins accessible through the query builder and snowflake schemas work really well.

The BI market is crowded af.

Everyone in the low end of the business world just uses Tableau or QlikView - neither company is very militant about enforcing their trial licensing; and full versions aren't really all that expensive for a business anyway (~$500 per license per year, but a small company can get away with a single license). Both tools are also much more mature and powerful than this product.

Not saying there's no room for an open source BI platform, but I don't know there's a lot of money in it given the competitive pressure Tableau and Qlik are putting on the SAPs/Oracles of the world. Anything over $100 is going to put you squarely in the realm of real BI tools with MUCH more capability, a much simpler interface, and standardized training programs.

Really strange. Out of curiosity what version of OS X are you on? If you don't mind filing an issue at https://github.com/metabase/metabase/issues/new or http://discourse.metabase.com/ we would love to get to the bottom of what's going on.

re:dash is very cool, and we actually used an early version way back.

The primary difference is audience. Most of the impetus to build it came from the difficulties we had getting non-analysts to do their own data pulls and ad hoc queries. While some number of people were able to edit SQL others had written for them, it rapidly became unsustainable.

As a consequence of the audience, we have lots of ancillary features (like the ability to see detail records and walk across foreign key links, the data reference, etc) that fell out as we learned how people used the tool.

Yes, writing the app in Clojure has been very interesting. Some things were downright magical, while other aspects felt very rough coming from other languages and frameworks. Ironically the prototype was in django =)

There will be a blog post about it coming up!

Clojure + React/Redux, the people behind it are incredible !

It's super easy to get started. If you have Docker installed, just run the following:

    docker run -d -p 3000:3000 --name metabase metabase/metabase

Thanks! We ship a sample database to play with in all the distributions.

Splunk is mainly about log analysis, though they have a new BI product. It's primarily about making sense of your logs. We primarily work with a database you already have (and eventually the data warehouse you'll spin up).

Palantir is a wide set of tools and really about much more powerful analysis than we're targeting.

Our whole bag is letting non-technical people in your company get answers to their common questions by themselves.

Would be interested in what you can offer when youre ready. Im helping businesses implement BI more from a strategic perspective. I dont get involved technically , its just part of my consulting.

Hey -

Really appreciate you taking the time to try it out!

If you're still around, are you using the default schema ('public') for your tables? We've found a few bugs around non-default schema we're working on.

Yes, we're mainly focused on making it easy for non-technical people to pull data out. We have had embedded applications that hit a REST api to expose actions on the data that was embedded though.

Redshift, Druid and Presto are in planning, but in general, we're going to build drivers in the order of community demand. If you have a specific db, open an issue or chime in on one of the open driver issues letting us know it's important.

Oh, that's an old version tag. Keep an eye out for a new version in a few hours, the app should offer you a chance to update.

Regarding FK's we let you filter or group by any attribute on either the table you're working with or any attribute on an FK in that table. Are you using the sample DB or your own?

We run it on linux in production and it's the primary platform we recommend for company wide deployments.

Power BI is pretty awesome, but pretty complicated when we tried it out. With enough attention it can do very cool things. Our primary audience is folks who don't need the level of customization, and have a lot of non-technical coworkers that want to ask their own questions.

These are very different product offerings. Power BI offers a mature in-memory columnstore database with a custom functional query language, DAX, with full dimensional modelling facilities. Additionally, if taking advantage of the (freemium) Power BI web portal, you get natural language query and cross-platform mobile apps.

This is essentially a SQL query builder on top of a RDBMS with some nice visualizations.

I am not trying to disparage the project - in fact I'm often of the opinion that a good query builder is all most organizations need for BI, but the two products in question are vastly different. Power BI has tooling to perform ETL, data modelling, and reporting. Metabase only covers the last piece.

In terms of performance, Metabase, being on top of a SQL RDBMS will have the potential to be incredibly speedy, but with large data volumes, you will need a DBA to make sure reporting scales appropriately. With Power BI, that conversation comes much later in the game. 10Ms of rows are trivial with a star schema and sufficient RAM in terms of performance tuning. With a SQL solution, there is a lot more back-end work to tune for an ad-hoc BI workload even at the 1Ms of rows level.

Curious. I run the docker image all the time and haven't had any issues with the port binding. What kind of system are you using? and what was your launch command?

You can double check that we are exposing port 3000 from the container which is where the app is designed to run by default. The Dockerfile used to build the image is in our repo here: https://github.com/metabase/metabase/blob/master/bin/release...

Heroku sometimes times out if the application takes too long to start. Could you try again and see if it works? We're working on making Heroku deploys more robust.

Are you on virtualbox? Sounds like a port binding issue. If you open an issue at github.com/metabase/metabase or http://discourse.metabase.com/ we'll try to debug exactly what's happening.

Same here, tried deploying with the Heroku button but can't access the app after deployment.

We occasionally see failures to deploy when things don't spin up fast enough. Does it blow up every time you click on the deploy button?

I'm able to get get past that and get the app deployed, but then I just get application error trying to launch it. Then I went and looked at the build log. I'm not sure its even doing anything:

-----> Fetching custom git buildpack... done -----> Null app detected -----> Nothing to do. -----> Discovering process types Procfile declares types -> web -----> Compressing... done, 33.1MB -----> Launching... done, v5

Thanks for the kind words!

Yeah all that database skin stuff... It's essentially derogatory or veiled complaints about simplicity right?

Could you take a screenshot of what it looks like and leave a post on with it on http://discourse.metabase.com/

I'll try to reproduce it. What database are you using?

easter eggs to come =)

Let us know what you think!