rel8 VS prosto

Compare rel8 vs prosto and see what are their differences.


Hey! Hey! Can u rel8? (by circuithub)


Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby (by asavinov)
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rel8 prosto
2 8
104 59
- -
7.9 5.5
7 days ago 6 months ago
Haskell Python
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of rel8. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-10.


Posts with mentions or reviews of prosto. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-31.
  • Excel 2.0 – Is there a better visual data model than a grid of cells?
    5 projects | | 31 Mar 2022
    One idea is to use columns instead of cells. Each column has a definition in terms of other columns which might also be defined in terms of other columns. If you change value(s) in some source column then these changes will propagate through the graph of these column definitions. Some fragments of this general idea were implemented in different systems, for example, Power BI or Airtable.

    This approach was formalized in the concept-oriented model of data which relies on two basic elements: mathematical functions and mathematical sets. In contrast, most traditional data models rely on only sets. Functions are implemented as columns. The main difficulty in any formalization is how to deal with columns in multiple tables.

    This approach was implemented in the Prosto data processing toolkit:

  • Show HN: Query any kind of data with SQL powered by Python
    6 projects | | 25 Jan 2022
    Having Python expressions within a declarative language is a really good idea because we can combine low level logic of computations of values with high level logic of set processing.

    A similar approach is implemented in the Prosto data processing toolkit:

    Although Prosto is viewed as an alternative to Map-Reduce by relying on functions, it also supports Python User-Defined Functions in its Column-SQL:

  • No-Code Self-Service BI/Data Analytics Tool
    1 project | | 13 Nov 2021
    Most of the self-service or no-code BI, ETL, data wrangling tools are am aware of (like airtable, fieldbook, rowshare, Power BI etc.) were thought of as a replacement for Excel: working with tables should be as easily as working with spreadsheets. This problem can be solved when defining columns within one table: ``ColumnA=ColumnB+ColumnC, ColumnD=ColumnAColumnE`` we get a graph of column computations* similar to the graph of cell dependencies in spreadsheets.

    Yet, the main problem is in working multiple tables: how can we define a column in one table in terms of columns in other tables? For example: ``Table1::ColumnA=FUNCTION(Table2::ColumnB, Table3::ColumnC)`` Different systems provided different answers to this question but all of them are highly specific and rather limited.

    Why it is difficult to define new columns in terms of other columns in other tables? Short answer is that working with columns is not the relational approach. The relational model is working with sets (rows of tables) and not with columns.

    One generic approach to working with columns in multiple tables is provided in the concept-oriented model of data which treats mathematical functions as first-class elements of the model. Previously it was implemented in a data wrangling tool called Data Commander. But them I decided to implement this model in the *Prosto* data processing toolkit which is an alternative to map-reduce and SQL:

    It defines data transformations as operations with columns in multiple tables. Since we use mathematical functions, no joins and no groupby operations are needed and this significantly simplifies and makes more natural the task of data transformations.

    Moreover, now it provides *Column-SQL* which makes it even easier to define new columns in terms of other columns:

  • Show HN: Hamilton, a Microframework for Creating Dataframes
    6 projects | | 8 Nov 2021
    Hamilton is more similar to the Prosto data processing toolkit which also relies on column operations defined via Python functions:

    However, Prosto allows for data processing via column operations in many tables (implemented as pandas data frames) by providing a column-oriented equivalents for joins and groupby (hence it has no joins and no groupbys which are known to be quite difficult and require high expertise).

    Prosto also provides Column-SQL which might be simpler and more natural in many use cases.

    The whole approach is based on the concept-oriented model of data which makes functions first-class elements of the model as opposed to having only sets in the relational model.

  • Against SQL
    8 projects | | 10 Jul 2021
    One alternative to SQL (type of thinking) is Column-SQL [1] which is based on a new data model. This model is relies on two equal constructs: sets (tables) and functions (columns). It is opposed to the relational algebra which is based on only sets and set operations. One benefit of Column-SQL is that it does not use joins and group-by for connectivity and aggregation, respectively, which are known to be quite difficult to understand and error prone in use. Instead, many typical data processing patterns are implemented by defining new columns: link columns instead of join, and aggregate columns instead of group-by.

    More details about "Why functions and column-orientation" (as opposed to sets) can be found in [2]. Shortly, problems with set-orientation and SQL are because producing sets is not what we frequently need - we need new columns and not new table. And hence applying set operations is a kind of workaround due the absence of column operations.

    This approach is implemented in the Prosto data processing toolkit [0] and Column-SQL[1] is a syntactic way to define its operations.

    [0] Prosto is a data processing toolkit - an alternative to map-reduce and join-groupby

    [1] Column-SQL (work in progress)

    [2] Why functions and column-orientation?

  • Functions matter – an alternative to SQL and map-reduce for data processing
    1 project | | 19 May 2021
  • NoSQL Data Modeling Techniques
    1 project | | 10 Apr 2021
    > This is closer to the way that humans perceive the world — mapping between whatever aspect of external reality you are interested in and the data model is an order of magnitude easier than with relational databases.

    One approach to modeling data based on mappings (mathematical functions) is the concept-oriented model [1] implemented in [2]. Its main feature is that it gets rid of joins, groupby and map-reduce by manipulating data using operations with functions (mappings).

    > Everything is pre-joined — you don’t have to disassemble objects into normalised tables and reassemble them with joins.

    One old related general idea is to assume the existence of universal relation. Such an approach is referred to as the universal relation model (URM) [3, 4].

    [1] A. Savinov, Concept-oriented model: Modeling and processing data using functions, Eprint: arXiv:1911.07225 [cs.DB], 2019

    [2] Prosto Data Processing Toolkit: No join-groupby, No map-reduce


    [4] R. Fagin, A.O. Mendelzon and J.D. Ullman, A Simplified Universal Relation Assumption and Its Properties. ACM Trans. Database Syst., 7(3), 343-360 (1982).

  • Feature Processing in Go
    3 projects | | 21 Dec 2020
    (Currently, it is not actively developed and the focus is moved to a similar project - - also focused on data preprocessing and feature engineering)

What are some alternatives?

When comparing rel8 and prosto you can also consider the following projects:


go-featureprocessing - 🔥 Fast, simple sklearn-like feature processing for Go

fquery - A graph query engine

Preql - An interpreted relational query language that compiles to SQL.

cape-python - Collaborate on privacy-preserving policy for data science projects in Pandas and Apache Spark

Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark

hamilton - A scalable general purpose micro-framework for defining dataflows. You can use it to create dataframes, numpy matrices, python objects, ML models, etc.

PostgreSQL - Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see