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thenaturalist
awesome-business-intelligence
Data & Databases2017–2022
thenaturalist / awesome-business-intelligence

awesome-business-intelligence

Actively curated list of awesome BI tools. PRs welcome!

2Distinct people
8Mentions
0Explicit recommenders
2.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Actively curated list of awesome BI tools. PRs welcome!

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year8 dated mentions

Observed mentions: 2017-10-02 20:11:05 UTC to 2022-02-13 23:24:00 UTC.

01232017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC303
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC202
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101

What Hacker News says

8 sources
I totally do not get the negative comments. Having seen a fair share of BI tools (I created and maintain an "awesome" list at [1] which I created for the sole purpose of getting an overview of what's out there) and assessing the stage you're at developing solo, you're getting quite some important UI basics very right imo. The "Next Steps" give a great impression of what's possible and how to navigate the app. From an E-Commerce viewpoint, most people want to flexibly play around with Products, Orders and Order Line Items and ratios between the three. Configuring this by hand in something like Google Datastudio etc. is cumbersome, even or more so in Looker (if broken out into separate Views, one needs to needs to create Merged Results Looks; if not the different cardinality objects are always connected). I feel like Insights is handling th
Read the rest
is really well. If you would add support for calculated fields, you'd offer a compelling E-Commerce controlling solution. One issue I noticed though is that your nesting seems to be unlimited. I could open an infinite "table" tree if I start from the order_lines or product view --> order_lines --> order --> order_lines --> product --> seller --> orders/ products... ad infinitum. You want to make sure that this drilldown into FK relationships does not lead to loops. Also I think some visual support where in the nest tree one is at (orders --> order_lines --> product --> sellers) would be great, otherwise it's quite easy to get lost. Other than that honestly congrats on doing a lot of things right in terms of data viz, responsiveness, sharing capabilities. Really like your work! [1]: https://github.com/thenaturalist/awesome-business-intelligen...
In Insights – Open-Source Self-Hosted Business Intelligence Platform
While this is a very open question both in terms of your analytical resources (dedicated data engineers, how tech savy are analysts, can they easily model their own data) as well as constraints from operating in an enterprise setup with regards to DevOps (easy to open ports, can data viz solutions be run online or only on prem), I'll try to mention a bunch of tools assuming they can be used. Notably, one thing you did not mention is which data visualization tool is used and how much that allows general business users to get insights. Database: In terms of enterprise ready Data Warehousing databases I would take a good look at Exasol (on prem & cloud) and Snowflake (cloud only). Exasol will likely be the pricier of the two and is much closer to the Java based Oracle world than Snowflake. Personally, I'm not a big fan of Redshift, as Snowfl
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ake takes all the pain that one might experience in Redshift away (manual tuning of sort & dist keys, tuning of coupled storage & compute clusters). Again, if you have good data engineering resources readily available it might make sense to look into a comparative cost analysis of the three in more detail. Notably, Snowflake has Snowpipe, a native API allowing you to stream data into the DB. Data modeling: You mentioned you use Kimball. In my experience while this is the definite choice for analytical querying and modeling, in an enterprise setup it might make sense to draw a Data Vault model layer between your raw sources and your dimensional model layer. Data Vault is more modular and easily extensible, will allow you to model history (compliance or GDPR readiness) and through the latter might very well serve you to expand/ extend your dimensional model. Not my own thought originally, very good read can be found here [0]. Data integration: A standard enterprise integration tool which comes to mind is Talend, although licenses are also pretty expensive and it feels very antiquated as well. Talend bought Stitch last year [1], which follows a much more modern approach. Worthy to take a look at, not sure if it can cover all of your requirements. Another modern solution provider for integration more geared towards enterprise would probably be alooma [2]. There is also Fivetran [3]. ETL: While some of the tools above can effectively be used for ETL, I'd take a good look at dbt by Fishtown Analytics [4]. It's SQL only but does a lot of things right in terms of being a module in a fragmented data stack which does its job really well. The thinking that goes into dbt is universally a good approach imho. A player geared towards enterprise is Matillion [5]. Visualization: The most empowering solution for general business users I know of is hands down Looker [6]. Of course there is Tableau and many other more mature solutions out there, but being able to write SQL is always a prerequisite to getting results there. I acknowledge this is a personal, strongly biased opinion, so don't take my word alone for it. Looker can be deployed on prem as well, has LDAP & SSO, and a bunch of other features which would make me consider it enterprise ready from a pure deployment perspective. In general I try to stay on top of the most common tools in the BI space, and maintain a repository of tools here: https://github.com/thenaturalist/awesome-business-intelligen... I am not associated in any form with any one company mentioned here or listed in the repo. 0: https://medium.com/rv-data/overhauling-our-data-warehouse-wi... 1: https://www.stitchdata.com 2: https://www.alooma.com 3: https://fivetran.com 4: https://www.getdbt.com 5: https://www.matillion.com 6: https://looker.com
In Ask HN: What do modern enterprise data warehouses look like?

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