10 ms·

So I have to deal with both tools at my company. The reality is that it all feels so incomplete from a BI standpoint. Managers are throwing money at front end t

by kfk 7y ago

So I have to deal with both tools at my company. The reality is that it all feels so incomplete from a BI standpoint. Managers are throwing money at front end tools like Salesforce and Tableau, but the entire back end stack is still pretty much the same as 20-30 years ago (big expensive Oracle-ish databases). I think the development of Python and Jupyter and other less known things like Vega are much more interesting. Python is today the only "glue code" that puts all of it together, from data to insights.

jjtheblunt 7y ago
What is Vega?
- tarp 7y ago
  > The goal of the Vega project is to promote an ecosystem of usable and interoperable tools, supporting use cases ranging from exploratory data analysis to effective communication via custom visualization design.[0] 0: https://vega.github.io/vega/about/ https://vega.github.io/vega/about/
remus 7y ago
> the entire back end stack is still pretty much the same as 20-30 years ago (big expensive Oracle-ish databases). Other than the expensive part, is it really such a bad thing? I feel like relational databases are a pretty good fit for a wide set of use cases and have a huge amount of tooling.
- kfk 7y ago
  The businesses are coming with requests that require complex SQL on millions of records of data that normally is sitting in various sources (warehouse, salesforce, etc.). Unless you hire expensive data engineers, you can't do this type of work reliably. You can stick things together with expensive GUI oriented prep tools like Alteryx, but you pay in reliability and, quite frankly, sleep. And forget IT, IT is so stuck in their ways that you'd be waiting years for each analysis + you'd spend 10x what you should.
  - deleted 7y ago
    [deleted]
  - prepend 7y ago
    Tableau has a data etl tool called “Prep” that helps with this problem. But it only goes so far. But I think that’s where the problem truly requires a data engineer.
    - initself 7y ago
      Exactly. Instead of ETL, start writing your own Perl and various logical, reusable components. Roll your own ETL, however you want it, in a terminal. So what, you have to learn vim, big deal! Mouse driven interfaces are a huge part of the dysfunction.
      - new4thaccount 7y ago
        Yea I've been a little confused here until I realized I would just write some bash, Python, Perl...etc script where some would advocate for complicated tools.
      - FranzFerdiNaN 7y ago
        And after a few years you leave your job, a new person comes in and gets stuck with your script soup and lack of documentation. Companies prefer well known products like Alteryx or Tableau because, despite the cost, it makes people easier to replace. But i cant blame you for writing your own things. Im currently replacing a large SSIS-based etl proces with Python, because i'm sick of SSIS randomly breaking.
    - theyard 7y ago
      There are plenty of modern day ETL tools like Funnel, Improvad or Dataddo to help with that part of the puzzle, though it does mean you have to pay another saas each month on top of Tableau.
  - jdgoesmarching 7y ago
    Expensive data engineer here, I see nothing wrong with this >:)
- dustingetz 7y ago
  RDBMS = data silo
  - stewbrew 7y ago
    You make it sound as if this were a bad thing. RDBMS work well for many use cases. There are plenty of tools around to work with them. Good open source implementations exist.
    - shmooth 7y ago
      I played with Denodo (data virtualization software) a couple years ago and thought it was pretty legit. In theory, it could be used to provide that industrial strength abstraction layer between your Tableau/Looker/etc. and your bajillion weird and not-so-weird (RDBMS) data sources. That would seem to make sense to me from the point of view of -- I would want my data visualization/analytics-type company to be able to concentrate on data visualization/analytics, not building some insane and never-ending data abstraction layer. The part that surprised me was that Denodo could allegedly do a lot of smart data caching, thus speeding things up (esp hadoop-oriented data sources) and keeping costs down. I'm guessing the other data virtualization providers can do similar.
      - kfk 7y ago
        I have had to work with the Denodo for the past 1+ year, a total nightmare. Data virtualization is a "good in theory" concept but "doesn't work in practice" reality. Going back to the original sources for each query doesn't work, it will always be slower than using a proper analytics data warehouse. Caching doesn't help because at that point you can just do ETL. Also Denodo itself is full of weird behaviors and bugs, my team collectively decided it's worth the most hate of all the "enterprise" tools we use. One thing Denodo is good for is as an "access layer", but then maybe PrestoDB would be worth a shot or maybe even just a sqlalchemy and python.
    - dustingetz 7y ago
      Consider the case when you are a big company and have hundreds of them
- initself 7y ago
  Ungodly expensive. PostgreSQL on the other hand - so good, so free!
  - timClicks 7y ago
    Just make sure that you tune it for analytical workloads. Its defaults are very conservative and lean towards transactional workloads.
    - erichocean 7y ago
      Do you have a link for more info on how to tune Postgres for analytical workloads? TIA
qwert-e 7y ago
> I think the development of Python and Jupyter and other less known things like Vega are much more interesting. In that case you may be interested in Dash (dash.plot.ly). It’s a free and open source library that you can use to create dashboards online with Python only.
- jbny_pi 7y ago
  +1 for Dash. It's very good.
- ruoyucad 7y ago
  plotly aint exactly free, you get 25ish free plot and then you gotta pay.
  - nicolaskruchten 7y ago
    That's not totally accurate... If you use our SaaS Chart Studio product then yes, but otherwise (i.e. in 'offline' mode) you can use our Python, R and Javascript libraries as much as you like: they're MIT licensed.
    - nicolaskruchten 7y ago
      PS: and Dash, which is what the GP was talking about, is totally free and unrelated to Chart Studio :)
    - stonecharioteer 7y ago
      I'd like to add that Dash helped me grok React far quicker than I'd expected. I code in a lot of React now and I'm only here because of Dash. Thank you for your hard work!
- ZeroCool2u 7y ago
  We love Dash on our team, anything more than a Tableau dashboard goes into Dash. You can basically just treat it as a Flask app. We write our back ends with FastAPI[1], which is usually just a wrapper around our ML models. Then serve both Dash and FastAPI with gunicorn. The backend is provided the uvicorn[2] worker class with the gunicorn -k arg[3] to greatly increase the speed as well. For personal projects you can use this stack in GCP's AppEngine standard environment to basically host your (relatively low traffic) apps for free. 1. https://fastapi.tiangolo.com/ https://fastapi.tiangolo.com/ 2. https://www.uvicorn.org/ https://www.uvicorn.org/ 3. https://docs.gunicorn.org/en/stable/run.html#commonly-used-arguments https://docs.gunicorn.org/en/stable/run.html#commonly-used-a...
frostyj 7y ago
Tableau might be friendly to analysts/end users, but its a piece of at the back end. The linux version they released is no more than a wrapper of original C code into JVM and thus causing huge performance issues and memory leaks. Being that said GL CRM
avip 7y ago
Confused why you call SalesForce “front end”?
- ineedasername 7y ago
  I interpreted that as front end to a database.
  - initself 7y ago
    It's really a whole working methodology that includes an Object model, which one would call backend. Did I mention its a nasty fugly POS?
    - tomrod 7y ago
      Mostly non-functional?
    - ineedasername 7y ago
      Yeah, it's certainly more than just a CRUD, and I agree it's awful. I never had to use it in the early years, so I don't know if it's that their original platform was poorly made, or that the accumulation of bloat & bolt-ons is what has made it this way.
    - da_chicken 7y ago
      Yeah but to someone who is in to working directly with RDBMSs, Salesforce is 100% front end. It's like how CEs think C is high level, but web developers think C is low level.
orky56 7y ago
Are you referring to APM/infrastructure monitoring, etc.? I was curious to find out if you're thinking BI capabilities need to be just as powerful at the backend as these frontend tools.
- kfk 7y ago
  In business analytics backend > frontend. If you are a business analyst you want all your data at your finger tips + the ability of running somewhat complex transformations and calculations. You also need to be able to validate data with your own rules, because you can't always trust data from warehouses and other sources. Then you want to take all your work and schedule it, so it runs automatically and refreshes all the reports depending on it. Finally, you want triggers on data changes/updates and good looking email notifications with screenshots, attachments, etc.. Of all of the above, end users might only see the dashboards, pdf's or Excel's on the frontend part. If you have a solid backend, you will be able to serve your customers at multiple times the speed and with a lot more insightful reports. At that point the difference between a Tableau dashboard (which does look good) and, say, Superset (which does not look as good) is somewhat negligible.
oarabbus_ 7y ago
What is Vega? I only found the genome browser tool, unless that is what you're referring to?
- bosie 7y ago
  My guess is this, since we are talking about tableau: https://vega.github.io/vega/ https://vega.github.io/vega/
  - oarabbus_ 7y ago
    Thanks. Seems extremely similar to Plotly upon first glance and their viz data structures.
    - solidasparagus 7y ago
      Vega is nice for its declarative, portable nature. Also, it makes certain things very easy such as embedding interactive Vega charts and generating images of charts without needing something like PhantomJS.
- acomjean 7y ago
  Its a javascript graphing library. We've started using it at work a bit for our online dynamic graphs. Its a graphic grammar on top of the d3 js library. We seem to prefer the lite version, which is simplier. If you look at the examples, you can click a button a go to a dynamic editor which we rather like.[1] https://vega.github.io/vega-lite/ https://vega.github.io/vega-lite/ If JS and web browsers aren't your thing, they have python version called "altair" https://altair-viz.github.io https://altair-viz.github.io [1]https://vega.github.io/editor/#/examples/vega-lite/stacked_bar_weather https://vega.github.io/editor/#/examples/vega-lite/stacked_b...
smrtinsert 7y ago
When I look at Jupyter notebooks, I ask myself why is anyone bother with ootb reporting tools or growing their own? Jupyter notebooks are the right mix of customizable and self-documenting.
- manigandham 7y ago
  How many business users are going to use Juypter notebooks? Meanwhile, someone with basic computing experience can create in-depth reports with Tableau in less than an hour.
  - dragonwriter 7y ago
    > How many business users are going to use Juypter notebooks? None, because it's too much programming for IT to let business people have access to it, and it's not disguised as an office productivity app the way Excel is. If they had access to it and had basic training on it that anyone already competent in any vaguely quantitative domain could handle, plenty of them could and would. At least judging by my experience with SQL shells and similar told that are both less powerful and less friendly than Jupyter + Python, and yet plenty of business people used them productively in enterprise environments (often right up until IT ripped it from their hands.)
    - shearskill 7y ago
      This is a repost but since you’re interested - Netflix data science is very pro-Jupyter/Python/data analysis packages. https://medium.com/netflix-techblog/notebook-innovation-591ee3221233 https://medium.com/netflix-techblog/notebook-innovation-591e...
    - mr_toad 7y ago
      Python is making some inroads in shops that have been using SAS and/or R, but it’s a hard sell. Jupyter is even harder to push because of the server element.
  - sct202 7y ago
    SAS and R are pretty common business user tools that are basically the same thing as Juypter. Tableau is nice, but you generally need to use something else to prep data for it whether that's an ETL process IT sets up or something manual that an analyst publishes.
matchagaucho 7y ago
My Salesforce clients have increasingly been considering Tableau. Tableau has great out-of-box discoverability of geo fields, dimensions and facts. The ability to auto-suggest a map of the U.S. based on data containing City/State is "magic" to power users. The only barriers to Salesforce + Tableau adoption I noticed were cross-object JOINs and live vs cached data extracts. Both issues were remedied by denormalizing the data prior to export. For example, a nightly flattened "view" of Opportunities with key related objects moved into columns. Mulesoft is well suited to perfect the ETL challenges. Bringing them to the table could be a win for everyone.
pea 7y ago
Hey - I'm hacking on a product which allows you to build interactive browser-based apps/dashboards from Python and dataset blocks. You can return altair (vega) plots from your Python and they'll get rendered in the app, and we're just adding the ability to import existing Jupyter notebooks this week. It's still really early, but feel free to have a play and create an app. Here is an example app examining using the Prophet forecasting library: https://nstack.com/apps/rdA647Q/ https://nstack.com/apps/rdA647Q/ I'd love any feedback, and if you'd like to chat to learn more, reach out to me on leo@nstack.com.
- lstroud 7y ago
  I can relate. The sales pitch is still “so easy anyone can do it”. As a result, corporate money is thrown after tools that enable business folks to build dashboards...which isn’t a good use of their time. :). In truth, they need good data folks working with them. I guess that doesn’t make for a sexy product sales pitch.
  - ericd 7y ago
    I think the counterargument is that it's not a good use of their time to convince someone to build it for them, when they can finally do it themselves in half the time :-)
initself 7y ago
The backend stack of both make development like swimming in glue. The same managers you are referring to cut us over to these tools and we subsequently experienced a massive decline in our business.
KorematsuFred 7y ago
Lindy effect. If it has survived that long it probably will survive in future too. Unless the quality of individuals working in non-tech goes significantly up in tech savvyness I find it hard that it will change.
scomp 7y ago
big expensive Oracle-ish databases Don't forget Teradata. I found the same thing with MicroStrategy. I spent a lot of time reverse engineering what I could from MicroStrategy jars to expose additional functions in their plugin interface (which is so incomplete it shouldn't be advertised). But the reality is its 20+ year old system with front end updates, can only put so many band aids on it. I think the only thing keeping MicroStategy alive is its cube functionality and the businesses who have invested to much into it.
manigandham 7y ago
The backend has also evolved massively from Hbase to the cloud-powered data warehouses. We have the ability to ingest and query petabytes with single-second delays now. There's also on-demand querying like Presto/Drill/Dremio, ETL systems like CBT, and the growing space of "data lineage" for seeing how data is connected and has evolved over time. The real issue has always been the organizational problems of larger teams and companies as data gets split into multiple silos and needs ETL and cleanup before it's useful. The new abilities we have gained have increased the complexity and scale which can lead to new challenges, but the tools are definitely getting better every day.
_AzMoo 7y ago
There are loads of options for BI backends. Snowflake, Panoply, Redshift... the list goes on and on and on.
bshoemaker 7y ago
If you have to write code for this stuff 90% of people won't be able to analyze things, and 90% of analyses won't happen because they won't be worth the time.

by kfk 7y ago

jjtheblunt 7y ago

What is Vega?

tarp 7y ago
> The goal of the Vega project is to promote an ecosystem of usable and interoperable tools, supporting use cases ranging from exploratory data analysis to effective communication via custom visualization design.[0] 0: https://vega.github.io/vega/about/ https://vega.github.io/vega/about/

remus 7y ago

> the entire back end stack is still pretty much the same as 20-30 years ago (big expensive Oracle-ish databases). Other than the expensive part, is it really such a bad thing? I feel like relational databases are a pretty good fit for a wide set of use cases and have a huge amount of tooling.

kfk 7y ago
The businesses are coming with requests that require complex SQL on millions of records of data that normally is sitting in various sources (warehouse, salesforce, etc.). Unless you hire expensive data engineers, you can't do this type of work reliably. You can stick things together with expensive GUI oriented prep tools like Alteryx, but you pay in reliability and, quite frankly, sleep. And forget IT, IT is so stuck in their ways that you'd be waiting years for each analysis + you'd spend 10x what you should.
- deleted 7y ago
  [deleted]
- prepend 7y ago
  Tableau has a data etl tool called “Prep” that helps with this problem. But it only goes so far. But I think that’s where the problem truly requires a data engineer.
  - initself 7y ago
    Exactly. Instead of ETL, start writing your own Perl and various logical, reusable components. Roll your own ETL, however you want it, in a terminal. So what, you have to learn vim, big deal! Mouse driven interfaces are a huge part of the dysfunction.
    - new4thaccount 7y ago
      Yea I've been a little confused here until I realized I would just write some bash, Python, Perl...etc script where some would advocate for complicated tools.
    - FranzFerdiNaN 7y ago
      And after a few years you leave your job, a new person comes in and gets stuck with your script soup and lack of documentation. Companies prefer well known products like Alteryx or Tableau because, despite the cost, it makes people easier to replace. But i cant blame you for writing your own things. Im currently replacing a large SSIS-based etl proces with Python, because i'm sick of SSIS randomly breaking.
  - theyard 7y ago
    There are plenty of modern day ETL tools like Funnel, Improvad or Dataddo to help with that part of the puzzle, though it does mean you have to pay another saas each month on top of Tableau.
- jdgoesmarching 7y ago
  Expensive data engineer here, I see nothing wrong with this >:)
dustingetz 7y ago
RDBMS = data silo
- stewbrew 7y ago
  You make it sound as if this were a bad thing. RDBMS work well for many use cases. There are plenty of tools around to work with them. Good open source implementations exist.
  - shmooth 7y ago
    I played with Denodo (data virtualization software) a couple years ago and thought it was pretty legit. In theory, it could be used to provide that industrial strength abstraction layer between your Tableau/Looker/etc. and your bajillion weird and not-so-weird (RDBMS) data sources. That would seem to make sense to me from the point of view of -- I would want my data visualization/analytics-type company to be able to concentrate on data visualization/analytics, not building some insane and never-ending data abstraction layer. The part that surprised me was that Denodo could allegedly do a lot of smart data caching, thus speeding things up (esp hadoop-oriented data sources) and keeping costs down. I'm guessing the other data virtualization providers can do similar.
    - kfk 7y ago
      I have had to work with the Denodo for the past 1+ year, a total nightmare. Data virtualization is a "good in theory" concept but "doesn't work in practice" reality. Going back to the original sources for each query doesn't work, it will always be slower than using a proper analytics data warehouse. Caching doesn't help because at that point you can just do ETL. Also Denodo itself is full of weird behaviors and bugs, my team collectively decided it's worth the most hate of all the "enterprise" tools we use. One thing Denodo is good for is as an "access layer", but then maybe PrestoDB would be worth a shot or maybe even just a sqlalchemy and python.
  - dustingetz 7y ago
    Consider the case when you are a big company and have hundreds of them
initself 7y ago
Ungodly expensive. PostgreSQL on the other hand - so good, so free!
- timClicks 7y ago
  Just make sure that you tune it for analytical workloads. Its defaults are very conservative and lean towards transactional workloads.
  - erichocean 7y ago
    Do you have a link for more info on how to tune Postgres for analytical workloads? TIA

qwert-e 7y ago

> I think the development of Python and Jupyter and other less known things like Vega are much more interesting. In that case you may be interested in Dash (dash.plot.ly). It’s a free and open source library that you can use to create dashboards online with Python only.

jbny_pi 7y ago
+1 for Dash. It's very good.
ruoyucad 7y ago
plotly aint exactly free, you get 25ish free plot and then you gotta pay.
- nicolaskruchten 7y ago
  That's not totally accurate... If you use our SaaS Chart Studio product then yes, but otherwise (i.e. in 'offline' mode) you can use our Python, R and Javascript libraries as much as you like: they're MIT licensed.
  - nicolaskruchten 7y ago
    PS: and Dash, which is what the GP was talking about, is totally free and unrelated to Chart Studio :)
  - stonecharioteer 7y ago
    I'd like to add that Dash helped me grok React far quicker than I'd expected. I code in a lot of React now and I'm only here because of Dash. Thank you for your hard work!
ZeroCool2u 7y ago
We love Dash on our team, anything more than a Tableau dashboard goes into Dash. You can basically just treat it as a Flask app. We write our back ends with FastAPI[1], which is usually just a wrapper around our ML models. Then serve both Dash and FastAPI with gunicorn. The backend is provided the uvicorn[2] worker class with the gunicorn -k arg[3] to greatly increase the speed as well. For personal projects you can use this stack in GCP's AppEngine standard environment to basically host your (relatively low traffic) apps for free. 1. https://fastapi.tiangolo.com/ https://fastapi.tiangolo.com/ 2. https://www.uvicorn.org/ https://www.uvicorn.org/ 3. https://docs.gunicorn.org/en/stable/run.html#commonly-used-arguments https://docs.gunicorn.org/en/stable/run.html#commonly-used-a...

frostyj 7y ago

Tableau might be friendly to analysts/end users, but its a piece of at the back end. The linux version they released is no more than a wrapper of original C code into JVM and thus causing huge performance issues and memory leaks. Being that said GL CRM

avip 7y ago

Confused why you call SalesForce “front end”?

ineedasername 7y ago
I interpreted that as front end to a database.
- initself 7y ago
  It's really a whole working methodology that includes an Object model, which one would call backend. Did I mention its a nasty fugly POS?
  - tomrod 7y ago
    Mostly non-functional?
  - ineedasername 7y ago
    Yeah, it's certainly more than just a CRUD, and I agree it's awful. I never had to use it in the early years, so I don't know if it's that their original platform was poorly made, or that the accumulation of bloat & bolt-ons is what has made it this way.
  - da_chicken 7y ago
    Yeah but to someone who is in to working directly with RDBMSs, Salesforce is 100% front end. It's like how CEs think C is high level, but web developers think C is low level.

orky56 7y ago

Are you referring to APM/infrastructure monitoring, etc.? I was curious to find out if you're thinking BI capabilities need to be just as powerful at the backend as these frontend tools.

kfk 7y ago
In business analytics backend > frontend. If you are a business analyst you want all your data at your finger tips + the ability of running somewhat complex transformations and calculations. You also need to be able to validate data with your own rules, because you can't always trust data from warehouses and other sources. Then you want to take all your work and schedule it, so it runs automatically and refreshes all the reports depending on it. Finally, you want triggers on data changes/updates and good looking email notifications with screenshots, attachments, etc.. Of all of the above, end users might only see the dashboards, pdf's or Excel's on the frontend part. If you have a solid backend, you will be able to serve your customers at multiple times the speed and with a lot more insightful reports. At that point the difference between a Tableau dashboard (which does look good) and, say, Superset (which does not look as good) is somewhat negligible.

oarabbus_ 7y ago

What is Vega? I only found the genome browser tool, unless that is what you're referring to?

bosie 7y ago
My guess is this, since we are talking about tableau: https://vega.github.io/vega/ https://vega.github.io/vega/
- oarabbus_ 7y ago
  Thanks. Seems extremely similar to Plotly upon first glance and their viz data structures.
  - solidasparagus 7y ago
    Vega is nice for its declarative, portable nature. Also, it makes certain things very easy such as embedding interactive Vega charts and generating images of charts without needing something like PhantomJS.
acomjean 7y ago
Its a javascript graphing library. We've started using it at work a bit for our online dynamic graphs. Its a graphic grammar on top of the d3 js library. We seem to prefer the lite version, which is simplier. If you look at the examples, you can click a button a go to a dynamic editor which we rather like.[1] https://vega.github.io/vega-lite/ https://vega.github.io/vega-lite/ If JS and web browsers aren't your thing, they have python version called "altair" https://altair-viz.github.io https://altair-viz.github.io [1]https://vega.github.io/editor/#/examples/vega-lite/stacked_bar_weather https://vega.github.io/editor/#/examples/vega-lite/stacked_b...

smrtinsert 7y ago

When I look at Jupyter notebooks, I ask myself why is anyone bother with ootb reporting tools or growing their own? Jupyter notebooks are the right mix of customizable and self-documenting.

manigandham 7y ago
How many business users are going to use Juypter notebooks? Meanwhile, someone with basic computing experience can create in-depth reports with Tableau in less than an hour.
- dragonwriter 7y ago
  > How many business users are going to use Juypter notebooks? None, because it's too much programming for IT to let business people have access to it, and it's not disguised as an office productivity app the way Excel is. If they had access to it and had basic training on it that anyone already competent in any vaguely quantitative domain could handle, plenty of them could and would. At least judging by my experience with SQL shells and similar told that are both less powerful and less friendly than Jupyter + Python, and yet plenty of business people used them productively in enterprise environments (often right up until IT ripped it from their hands.)
  - shearskill 7y ago
    This is a repost but since you’re interested - Netflix data science is very pro-Jupyter/Python/data analysis packages. https://medium.com/netflix-techblog/notebook-innovation-591ee3221233 https://medium.com/netflix-techblog/notebook-innovation-591e...
  - mr_toad 7y ago
    Python is making some inroads in shops that have been using SAS and/or R, but it’s a hard sell. Jupyter is even harder to push because of the server element.
- sct202 7y ago
  SAS and R are pretty common business user tools that are basically the same thing as Juypter. Tableau is nice, but you generally need to use something else to prep data for it whether that's an ETL process IT sets up or something manual that an analyst publishes.

matchagaucho 7y ago

My Salesforce clients have increasingly been considering Tableau. Tableau has great out-of-box discoverability of geo fields, dimensions and facts. The ability to auto-suggest a map of the U.S. based on data containing City/State is "magic" to power users. The only barriers to Salesforce + Tableau adoption I noticed were cross-object JOINs and live vs cached data extracts. Both issues were remedied by denormalizing the data prior to export. For example, a nightly flattened "view" of Opportunities with key related objects moved into columns. Mulesoft is well suited to perfect the ETL challenges. Bringing them to the table could be a win for everyone.

pea 7y ago

Hey - I'm hacking on a product which allows you to build interactive browser-based apps/dashboards from Python and dataset blocks. You can return altair (vega) plots from your Python and they'll get rendered in the app, and we're just adding the ability to import existing Jupyter notebooks this week. It's still really early, but feel free to have a play and create an app. Here is an example app examining using the Prophet forecasting library: https://nstack.com/apps/rdA647Q/ https://nstack.com/apps/rdA647Q/ I'd love any feedback, and if you'd like to chat to learn more, reach out to me on leo@nstack.com.

lstroud 7y ago
I can relate. The sales pitch is still “so easy anyone can do it”. As a result, corporate money is thrown after tools that enable business folks to build dashboards...which isn’t a good use of their time. :). In truth, they need good data folks working with them. I guess that doesn’t make for a sexy product sales pitch.
- ericd 7y ago
  I think the counterargument is that it's not a good use of their time to convince someone to build it for them, when they can finally do it themselves in half the time :-)

initself 7y ago

The backend stack of both make development like swimming in glue. The same managers you are referring to cut us over to these tools and we subsequently experienced a massive decline in our business.

KorematsuFred 7y ago

Lindy effect. If it has survived that long it probably will survive in future too. Unless the quality of individuals working in non-tech goes significantly up in tech savvyness I find it hard that it will change.

scomp 7y ago

big expensive Oracle-ish databases Don't forget Teradata. I found the same thing with MicroStrategy. I spent a lot of time reverse engineering what I could from MicroStrategy jars to expose additional functions in their plugin interface (which is so incomplete it shouldn't be advertised). But the reality is its 20+ year old system with front end updates, can only put so many band aids on it. I think the only thing keeping MicroStategy alive is its cube functionality and the businesses who have invested to much into it.

manigandham 7y ago

The backend has also evolved massively from Hbase to the cloud-powered data warehouses. We have the ability to ingest and query petabytes with single-second delays now. There's also on-demand querying like Presto/Drill/Dremio, ETL systems like CBT, and the growing space of "data lineage" for seeing how data is connected and has evolved over time. The real issue has always been the organizational problems of larger teams and companies as data gets split into multiple silos and needs ETL and cleanup before it's useful. The new abilities we have gained have increased the complexity and scale which can lead to new challenges, but the tools are definitely getting better every day.

_AzMoo 7y ago

There are loads of options for BI backends. Snowflake, Panoply, Redshift... the list goes on and on and on.

bshoemaker 7y ago

If you have to write code for this stuff 90% of people won't be able to analyze things, and 90% of analyses won't happen because they won't be worth the time.