Open Source Business Intelligence Tools: Metabase and Redash

Bruno Santos, Francisco Sério, Steven Abrantes, Filipe Sá, Jorge Loureiro, Cristina Wanzeler and

Pedro Martins

Escola Superior de Tecnologia e Gestão de Viseu, Instituto Politécnico de Viseu, Viseu, Portugal

Keywords: Business Intelligence, Open Source BI Platforms, BI Software, Metabase, Redash.

Abstract: This electronic document is an article that explores the capabilities of Business Intelligence tools, primarily

their ability to analyze generated business data gathered from a company. These corporations can improve (or

even create) their products according to the insights provided by these platforms, with the possibility of

outclassing their direct competitors, something to be proved crucial for an ever-evolving market. In this article,

we have tested and compared two of the most promising open-source BI platforms currently available: these

are Metabase and Redash. Our focus is to analyze what they offer as a package, where we defined some key

points, such as: overall performance, search engine compatibility, key features, etc. May we remind that the

implementation of a platform of choice, concerning BI software, may vary according to company demands.

Some tools may be more suitable for a corporation, while others may be the best choice for a different entity.

1 INTRODUCTION

In recent years, technology has grown in the

innovation component on a tremendous scale. G. R.

Gangadharan and S. N. Sundaravall (Gangadharan

and Sundaravall, 2004) wrote that: “Managing an

organization requires access to information in order

to monitor activities and assess performance”.

However, one of the current realities is that there

are still business owners who use Excel sheets and

paper records to track business development and

record data. Neil Raden (Raden, 2005) acknowledged

this by stating: “There are over 150 million business

users of Excel worldwide, and a large proportion of

them are devoted, at least part of the time, to entering

data by hand, extracting data manually from other

systems and functioning as report servers”.

These owners maintain a high level of confidence

in these methods which, unfortunately, ends up

revealing how much of a "closed mindset" they have

when it comes to adopting new strategies.

The necessity of a platform for decision support is

rapidly increasing, especially if it’s a large business

we’re speaking of. We must consider that with the

amount of data stored by companies growing

exponentially, it comes to no surprise that finding a

more efficient data management solution should be at

the top of these industry owners’ priority list. Chandra

Nandyala and Haeng-Kon Kim (Nandyala and Kim,

2016) wrote: “Data needs to be secure, and its

distribution must be done efficiently so that important

and up-to-date business decisions are made. In

today’s world, the way to store and retrieve or access

personal as well as other information has captured a

massive revolution”.

To resolve this matter, the concept of Business

Intelligence started to gain more traction, although it

wasn’t quite study yet. Bernhard Wieder and Maria-

Luise Ossimitz (Wieder and Ossimitz, 2015) stated

that: “Business Intelligence (BI) systems have been a

top priority of CIOs for a decade, but little is known

about how to successfully manage those systems

beyond the implementation phase”.

Undeniably, it’s critical for companies that

gathered data is translated into information for

planning future business strategies. For a lot of these,

valuable data is stored on large-scale servers, the so-

called clusters. Ideally, this stored data should

provide information on sales trends, consumer

behaviors, and resource allocation.

And for this reason, the market started to appraise

Business Intelligence, investing more into these

systems. William Yeoh and Andy Koronios (Yeoh

and Koronios, 2010) wrote that: “(The) BI market

appears vibrant and the importance of BI systems is

more widely accepted, few studies have investigated

the critical success factors that affect the

implementation success”.

Santos, B., Sério, F., Abrantes, S., Sá, F., Loureiro, J., Wanzeler, C. and Martins, P.

Open Source Business Intelligence Tools: Metabase and Redash.

DOI: 10.5220/0008351704670474

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 467-474

ISBN: 978-989-758-382-7

467

Company data can indicate the viability of a given

product and determine key indicators for potential

future expansion and/or growth. In this way, data can

help maximize revenues and reduce costs.

Currently, huge organizations are adopting BI

systems in the field of Information Technology that

are able of operations such as extract data, convert

what was collected into understandable values, and

then cram those into the platform, being then able to

fully analyze the given data.

Mihaela Muntean and Traian Surcel (Muntean

and Surcel, 2013) state that: “Traditional BI systems

use ETL tools for extracting data from multiple

sources and temporarily storing those datasets at a

staging area. Organizations use data warehouses to

aggregate cleaned and structured data”.

Another relevant area that should be mentioned is

IoT (Internet of Things). According to In Lee and

Kyoochun Lee (Lee and Lee, 2015): “IoT devices and

machines with embedded sensors and actuators

generate enormous amounts of data and transmit it to

business intelligence and analytics tools for humans

to make decisions. These data are used to discover

and resolve business issues - such as changes in

customer behaviors and market conditions - to

increase customer satisfaction, and to provide value-

added services to customers”. They further expand

stating: “Business analytics tools may be embedded

into IoT devices, such as wearable health monitoring

sensors, so that real-time decision making can take

place at the source of data”.

The remainder of this article will be structured as

follows: section II dives more into what Business

Intelligence stands for while correlating with the topic

of open-source technologies, also expanding on the

technologies to be tested; section III describes the

experimental setup used to perform tests on the open-

source BI platforms in study, e. g., utilized hardware;

section IV displays the obtained results derived from

testing; and in section V we present our conclusions,

adding a perspective for our future work.

2 STATE OF ART

A. Business Intelligence

It’s primarily in the data analysis component that

Business Intelligence (also known by the

abbreviation BI) tools materialize. According to

Solomon Negash and Paul Gray (Negash and Gray,

2008): “Business Intelligence (BI) is a Data-Driven

Decision Support System (DSS) that encompasses

data collections, data storage, and curriculum

management to facilitate entry into the decision-

making process. (...) Business Intelligence is an

analysis of large volumes of data about a company

and its operations. Includes competitive intelligence

(customer concentration) as a subset. In computing

environments, a large database, such as a data

warehouse or data mart, is used as a source of

information and as a database for the sophisticated

ones. Reads range from receiving the "data slices",

will receive an ad hoc review, a real-time analysis and

a forecast. (...) Recent developments in this area

include business performance analysis (BPM),

business activity monitoring (BAM) and BI

expansion of your workforce for people across the

organization (BI for the masses). In the longer term,

BI techniques and discoveries are embedded in

business processes”.

This line of thought is expanded by Hugh J.

Watson and Barbara H. Wixom (Watson and Wixom,

2007), as they imply: “(…) BI reduces IT

infrastructure costs by eliminating redundant data

extraction processes and duplicate data housed in

independent data marts across the enterprise. (…) BI

also saves time for data suppliers and users because

of more efficient data delivery”.

Likewise, Marcus Gibson, David Arnott and Ilona

Jagielska (Gibson, Arnott and Jagielska, 2004) state

that: “The role of BI is to extract the information

deemed central to the business, and to present or

manipulate that data into information that is useful for

managerial decision support. In their simplest form,

these tools permit a decision maker to access an up to

date, often consolidated, view of business

performance.”

The concept of Business Intelligence can be

traced back to the early 90’s, according to Matteo

Golfarelli, Stefano Rizzi and Iuris Cella (Golfarelli,

Rizzi and Cella, 2004): “BI was born within the

industrial world in the early 90’s, to satisfy the

managers’ request for efficiently and effectively

analyzing the enterprise data in order to better

understand the situation of their business and

improving the decision process. In the mid-90’s BI

became an object of interest for the academic world,

and ten years of research managed to transform a

bundle of naive techniques into a well-founded

approach to information extraction and processing”.

BI has seen a rise in popularity in the Northern

Europe region, according to Mika Hannula and Virpi

Pirttimaki (Hannula and Pirttimaki, 2003): “Business

Intelligence activities have recently become much

common in Finland. It is common knowledge that

large-scale companies operating in a global

marketplace – especially in the ICT sector – do put

effort into sophisticated BI activities”.

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Similar to an OLAP (On-Line Analytical

Processing) tool, BI is integrated in the Data Access

Tools stage of an Data Warehouse platform. As

pointed out by Dr. Jawahar Babu (Babu, 2012): “The

data warehouse is the significant component of

business intelligence. It is subject oriented,

integrated. The data warehouse supports the physical

propagation of data by handling the numerous

enterprise records for integration, cleansing,

aggregation and query tasks. It can also contain the

operational data which can be defined as an

updateable set of integrated data used for enterprise

wide tactical decision-making of a particular subject

area. It contains live data, not snapshots, and retains

minimal history”.

Supporting this claim are Barbara H. Wixom and

Hugh J. Watson (Wixom and Watson, 2010), when

mentioning people who work on Data Warhouse

systems: “A variety of stakeholders play essential BI

roles. Extraction, Transformation and Loading (ETL)

experts, data modelers and database administrators

focus on preparing the data warehouse for use”.

So, subsequently, we can safely assume that

software provided by technologies in the BI

department is fulfilling a lot of the criteria present in

the current market. As stated by Joaquim Lapa, Jorge

Bernardino and Ana Figueiredo (Lapa, Bernardino

and Figueiredo, 2014): “(…) we consider the

presence of Collaborative Technologies in BI

platforms will be a requirement for organizations

(…)”.

The possibilities that BI reports offer, whether for

their clearly understandable information, or for their

facilitated interaction, coupled with intuitively

designed dashboards for a more assessed evaluation

from the user, makes Business Intelligence a “must”

in order to organizations have a thriving future.

B. Open source BI

We’ve seen so far how promising Business

Intelligence software is, but most of the tools out

there are “locked” behind a monthly subscription fee,

tools that are only within the realm of larger

corporations. But what about smaller firms? These

organizations may not have the economic capabilities

to afford a tool that may cost more than 5000$ per

month.

Thankfully, there has been a notable ascension in

BI platforms that are open source. And contrary to

what you may think, these are no slouch either. Karim

Lakhani and Eric von Hippel (Lakhani and von

Hippel, 2004) state that: “Open source software

products represent the leading edge of innovation

development and diffusion systems conducted for and

by users themselves – no manufacturer required”. The

question here is, why use open source BI platforms?

Well, to answer that, we must briefly discuss the

benefits of open source.

One of the most prominent reasons to “go open”

is how you can access an application developed by a

“team” of talented people, with the release of several

stable versions, and be instructed how to use it with

the help of pages of detailed online documentation

they have created, available to the general public.

Chris Coppola and Ed Neelley (Coppola and Neelley,

2004) claim that: “New versions are released very

often and rely on the community of users and

developers to test it, resulting in superior quality

software tested on more platforms, and in more

environments than most commercial software”.

Other benefit resides in the fact that you may be

able to customize (depending on a license) the

application to suit your needs, or the company’s.

Brian Fitzgerald (Fitzgerald, 2006) stated that: “High-

profile organizations like Amazon, Google and

Salesforce.com take advantage of the reliability and

low cost of open source to create a platform on which

they can offer value-added services in their own

business domains. (…) These companies also

customize open source products to suit their internal

needs”.

Lastly, we must not forget what makes these

applications open source, the price. Or rather, the lack

of it. A big reason that attracts customers and

companies alike is the fact that you don’t have to pay

for the software whilst having a well-supported app

by the community that is also able to combine

proprietary technology with open source technology.

InduShobha Chengalur-Smith, Saggi Nevo and

Pindaro Demertzoglou (Chengalur-Smith, Nevo and

Demertzoglou, 2010) concluded that: “(…)

compatibility of the open source technology with the

existing technology infrastructure creates an

environment that promotes use of the technology and

increases the opportunity for realizing business

value”.

The open source model has proven itself to be as

crucial as it is viable and combining this with

Business Intelligence may possibly give us a

powerful tool that is within the reach of both smaller

and larger companies.

C. Metabase

Metabase is an open source tool that allow people in

a company to ask questions and learn from data

descendant from data sources. This software allows

filter and/or group data according to user needs,

without resorting to Structured Query Language

(SQL). If needed, Metabase also provides with a SQL

interface for users.

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469

This tool has a functionality that monitors

questions created by users to gain insights on the

available data. These questions can produce graphs and

charts, and these visualizable results can be saved and

organized in Dashboards.

The Metabase platform is available under three

types of licences: AGPL, which is free of costs,

Premium Embedding License, with acquisition costs

(includes a White labeled Embedding option), and

Metabase Commercial License, with acquisition costs

(offers more functionalities not present in the previous

mentioned license). For this project, we used the free

v0.32.7 version of Metabase.

D. Redash

Redash is an open source platform that lets a user

connect and query his data sources by browsing the

existing schemas through the usage of an incorporated

SQL editor. Available also is an option to schedule data

sources refresh times.

A user can visualize data by building dashboards

with graphs and charts, by simply dragging and

dropping them. These dashboards can be shared within

the company with other users or can be shared publicly.

The Redash platform is available in two models:

free and paid. Within the paid model there are three

types of packages: Starter, Pro, and Business, where

the key difference between these three is the number of

data sources, dashboards, saved queries and maximum

query execution times allowed. For this project, we

used the free v7.0.0 version of Redash.

3 EXPERIMENTAL SETUP

With this project, we wish to demonstrate the potential

of the Business Intelligence tools under study, by

querying the data present in databases, which will be

connected to the BI tools, and transform that data into

easily perceived dashboards and/or graphs to

demonstrate how they allow us to identify patterns and

facts of potential relevance. These created graphics are

entirely dependent on the capabilities of the tool. The

queries will be made in the same way for either.

In order to carry out the desired tests, databases

were set up with large amounts of data coming from a

search engine of our choice. These databases are stored

on personal computers, meaning that they are not

present in the cloud.

The search engine in question is MySQL, since it

is compatible with both of the Business Intelligence

tools. This proved to be beneficial for us, since we have

experience working with this search engine, whilst

having an idea of the capabilities of it. Although, we

must mention that it was not our first choice.

We initially thought of using PostgreSQL, but we

found issues when trying to insert more than 20

million rows with the inclusion of indexes (this

subject will be expanded further). So, we resorted to

MySQL instead, a search engine we had work with

during our scholar years, which proved to be capable

of handling these large datasets, both with indexes

and without them.

To generate the data that would populate our

MySQL databases, it was necessary to use the TPC-

H tool. This tool allowed us to generate data on the

threshold of Gigabytes, which are fragmented into 8

TBL files. In order to insert the data in the tables

directly into the search engine, it was necessary to

convert the files in TBL format to CSV format.

The two databases used have the same tables:

Orders, Lineitem, Customer, Supplier, Part, Partsupp,

Nation and Region, as they have the same number of

records in them:

 Region: 5 rows.

 Nation: 25 rows.

 Supplier: 80 thousand rows.

 Customer: 1,2 million rows.

 Part: 1,6 million rows.

 Partsupp: 6,4 million rows.

 Orders: 12 million rows.

 Lineitem: 48 million rows (aprox.).

Figure 1: Schematic of the used tables (with index

example).

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In the above available Fig.1 we present the

schema, created using a tool present in MySQL, that

includes the tables and relations shared by the two

databases.

The key difference between the two is that

indexes have been created in one of the databases.

This implies that the index database will have a larger

size than the non-index database. The Database with

indexes has a total of 10.1 Gigabytes of data, while

the indexless DB totals at 9.3 Gigabytes, as can be

seen in Fig. 2 and Fig. 3.

It is intended to compare databases without

indexes directly with the DB with indexes to verify if

there are any differences between the Business

Intelligence software regarding the processing of

queries made, more specifically, what are the

response times.

All test queries made in the Business Intelligence

tools and the MySQL search engine were performed

using two laptops with the same CPU: Intel i7-8750H

Figure 2: Database Tables with Indexes (Action tab

removed).

Figure 3: Database Tables without Indexes (Action tab

removed).

Hexa-Core, with a base clock of 2.2 GHz, max

clock of 4.1 GHz and 9 MB of Cache. A third

computer was used to house the tools described in this

study. The computer has a different CPU than the

previously described laptops: Intel i7-4710HQ Quad-

Core, with a base clock of 2.5 GHz, max clock of 3.5

GHz and 6MB of Cache.

Finally, Fig. 4 shows the queries that will serve as

a test for what we will search for in both BI softwares.

Figure 4: Test Queries.

4 RESULTS

By using the SQL Editor in each of the Business

Intelligence tools, the SQL queries mentioned in the

previous chapter were performed. Fig. 5 and Fig. 6

show the software editors used in the context of our

project in its fullness.

Figure 5: Metabase SQL Editor.

After querying the data using the Business

Intelligence tools without index and with index, as

desired, the query execution times were recorded, as

well as graphs were created using the results obtained

by the queries in the respective tools, with the purpose

of exploring their potential in this field.

Open Source Business Intelligence Tools: Metabase and Redash

471

Figure 6: Redash SQL Editor.

Figure 7: Metabase’s Dashboard of data with indexes.

The dashboards created in Metabase can be found

in Fig. 7 and Fig. 8, where the first figure corresponds

to the index data dashboard, while the second is

related to the data without index.

Figure 8: Metabase’s Dashboard of data without indexes.

Similarly, the dashboards created in the BI

Redash tool are shown in Fig. 9 and Fig. 10, where,

according to the previously shared line of thought, the

first figure corresponds to the dashboard of the index

data, while the second corresponds to the data without

index.

Figure 9: Redash’s Dashboard of data with indexes.

Figure 10: Redash’s Dashboard of data without indexes.

In order to analyze the obtained results, in the

execution times of the SQL queries component,

charts were created where it’s possible to make a

direct comparison of Metabase with Redash,

regarding the time that each query took to return the

result.

This is true for both the indexed data, Fig. 11, as

well as for the non-indexed data, Fig. 12.

Figure 11: Chart of query results with indexes.

Figure 12: Chart of query results without indexes.

Based on the results obtained, we can draw the

following conclusions:

 The query B, in the data without index, was not

able to return any result, both in Metabase and in

Redash.

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 Contrary to the previous point, in the indexed

data, the query returned results in both tools, both

of which took less than 2 seconds.

 Interestingly, inversely to query B, query E, in

indexed data, was unable to return results either

in Metabase or Redash, returning only on non-

indexed data, where Redash took less time to

return information.

 In query D, for both indexed and non-indexed

data, Redash was unable to obtain any kind of

information.

 In query C, Redash took more time than Metabase

to return information, either for data with indexes

or data without them.

 In queries A, C and F, response times between

index and non-index data showed no significant

changes.

 It’s particularly interesting to note that in

Metabase, although query E has returned data,

since it took more than 60 seconds to process it,

the dashboard failed to display information about

it.

5 CONCLUSIONS

With this article, we have analyzed BI in its essence

by conducting several tests with big amounts of data.

We found that the core definition of BI is mostly

shared by several authors, defining Business

Intelligence as a process where data is gathered,

stored and transformed into information through

analysis, and where information is transformed into

knowledge that, ultimately, aids on the decision

making side of organizations.

Developing on the topic of indexing, the results

indicate that, for more general queries, where, in the

most specific cases, ranges of values are specified

(WHERE clause), there is a considerable difference

that justifies the use of indexes. However, the biggest

difference comes in the form of the JOIN clauses,

where it’s evident that the usage of indexes on table

columns is noticeable in terms of performance. We

recommend using indexes if a Business Intelligence

expert is required to use queries with a JOIN clause.

Mentioning the results obtained in the execution

of query B, as a reference point, it was noticed that,

without the use of indexes, the query was not able to

return results, however, with the use of these, the

query returned data in a matter of seconds. It’s in this

perspective that, although the indexes imply an

increase in size of the data present in tables, we

recommend the implementation of these.

Speaking of user experience, the BI Metabase and

Redash tools did not show a huge learning curve. The

intended functionalities are located and organized in

a very explicit way, and the documentation of the

tools, when they were consulted, provided a good

level of clarification.

Table 1: Comparison of integrations and search engines

supported between Metabase and Redash.

Redash

Metabase

MySQL



PostgreSQL



MongoDB



Microsoft SQL

Server



AWS Redshift



Google BigQuery



Druid



SQLite



Oracle



Crate



Google Analytics



Vertica



Spark



Presto



Snowflake



Amazon Athena



Amazon Aurora



Amazon Redshift



Amazon DynamoDB



Axibase TSDB



Cassandra



ClickHouse



Druid



ElasticSearch



Graphite



Greenplum



Hive



Impala



InfluxDB



MemSQL



Rockset



ScyllaDB



Snowflake



TreasureData



Total:

In terms of resource usage, Metabase has proved

to be a more dependent tool regarding the hosted

machine’s hardware than Redash. The percentage of

CPU and Hard Drive utilization in Metabase reached

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473

26% and 92%, respectively, while Redash reached

12% and 76%, respectively.

In the compatibility side of things, Redash offers

a greater number of integrations and compatible

search engines, as it can be seen in the following

table.

Of these documented search engines, integrations

were tested only for with PostgreSQL and

MySQL, with the later being used due to issues

with PostgreSQL mentioned in the Experimental

Setup chapter.

For future work, we intend to test these Business

Intelligence tools using NoSQL search engines, e. g.,

MongoDB, supported by both Metabase and Redash,

in the Internet of Things (IoT) area. Data generated

by IoT devices is generally stored in these type of

Database Management Systems (DBMS), and, as we

mentioned in the State of Art chapter, IoT is one of

Business Intelligence’s most important areas of

actuation currently.

Likewise, we intend to test these BI platforms on

machines with more powerful specifications than the

computers used in this project, in order to verify the

differences in response times between databases with

configured indexes and databases without indexes.

Finally, although we have used some charting

options provided by these BI tools, we believe there

is margin for further exploration of these options in a

future approach.

ACKNOWLEDGEMENTS

This work is financed by national funds through FCT

- Fundação para a Ciência e Tecnologia, I.P., under

the project UID/Multi/04016/2019. Furthermore we

would like to thank the Instituto Politécnico de Viseu

and CI&DETS for their support.

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