Frontline Systems Analytic Solver is an Excel add-on for performing data mining, and predictive analytics from within Microsoft Excel.
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dbt
Score 9.0 out of 10
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dbt is an SQL development environment, developed by Fishtown Analytics, now known as dbt Labs. The vendor states that with dbt, analysts take ownership of the entire analytics engineering workflow, from writing data transformation code to deployment and documentation. dbt Core is distributed under the Apache 2.0 license, and paid Teams and Enterprise editions are available.
$0
per month per seat
Pricing
Analytic Solver
dbt
Editions & Modules
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No answers on this topic
Offerings
Pricing Offerings
Analytic Solver
dbt
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Community Pulse
Analytic Solver
dbt
Features
Analytic Solver
dbt
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Analytic Solver
-
Ratings
dbt
9.7
8 Ratings
17% above category average
Simple transformations
00 Ratings
10.08 Ratings
Complex transformations
00 Ratings
9.48 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Based on my limited experience and use, and therefore limited global knowledge of the software, I would recommend it especially if the data that will be used as inputs to the model has previously worked on a spreadsheet such as Excel. I would also recommend it to analyze problems of medium and small size. Given the experience I have had when I have used it with large problems, there have been noticeable decreases in the speed of response (which are not associated with the size of the system of equations involved in the calculation). Excellent for processing linear programming models.
The prerequisite is that you have a supported database/data warehouse and have already found a way to ingest your raw data. Then dbt is very well suited to manage your transformation logic if the people using it are familiar with SQL. If you want to benefit from bringing engineering practices to data, dbt is a great fit. It can bring CI/CD practices, version control, automated testing, documentation generation, etc. It is not so well suited if the people managing the transformation logic do not like to code (in SQL) but prefer graphical user interfaces.
On the few occasions when I have used it to deal with problems of optimization of relatively large parameters (with a large number of restrictions and decision variables), the program has been slower, not substantially but slower, than programs such as the WinQsb, even when the latter runs on 32-bit machines and not 64. That has caught my attention, even though it is not a real problem for the uses I give to the program.
Given my partial function as a university professor, it has been much more effective and practical to use other software, due to the limited options that the educational license associated with the software has.
dbt is very easy to use. Basically if you can write SQL, you will be able to use dbt to get what you need done. Of course more advanced users with more technical skills can do more things.
We believe in building the models in Excel. A limitation with Excel is that Excel Solver can not take more than 200 decision variables with multiple constraints. It is cheap in terms of license and maintenance fees against other softwares which are available in the market.
I actually don't know what the alternative to dbt is. I'm sure one must exist other than more 'roll your own' options like Apache Airflow, say, bu tin terms of super easy managed/cloud data transforms, dbt really does seem to be THE tool to use. It's $50/month per dev, BUT there's a FREE version for 1 dev seat with no read-only access for anyone else, so you can always start with that and then buy yourself a seat later.
- It has allowed finding ways to optimize (minimizing costs or times) the field processes involved in various projects.
It has even allowed, in specific cases where it was used for that purpose, to optimize the allocation of resources (people) to work in different jobs that present weekly variations of the activity that these people must perform.
It has allowed the sensitivity analysis of projects to changes in the decision variables related to them, which, and in very dynamic and changing environments, resulted in substantial decreases in money losses.