Zillion: Make sense of it all

Introduction

Zillion is a data modeling and analytics tool that allows combining and analyzing data from multiple datasources through a simple API. It acts as a semantic layer on top of your data, writes SQL so you don't have to, and easily bolts onto existing database infrastructure via SQLAlchemy Core. The Zillion NLP extension has experimental support for AI-powered natural language querying and warehouse configuration.

With Zillion you can:

• Define a warehouse that contains a variety of SQL and/or file-like datasources
• Define or reflect metrics, dimensions, and relationships in your data
• Run multi-datasource reports and combine the results in a DataFrame
• Flexibly aggregate your data with multi-level rollups and table pivots
• Customize or combine fields with formulas
• Apply technical transformations including rolling, cumulative, and rank statistics
• Apply automatic type conversions - i.e. get a "year" dimension for free from a "date" column
• Save and share report specifications
• Utilize ad hoc or public datasources, tables, and fields to enrich reports
• Query your warehouse with natural language (NLP extension)
• Leverage AI to bootstrap your warehouse configurations (NLP extension)

Table of Contents

• Installation
• Primer
  • Metrics and Dimensions
  • Warehouse Theory
  • Query Layers
  • Warehouse Creation
  • Executing Reports
  • Natural Language Querying
  • Zillion Configuration
• Example - Sales Analytics
  • Warehouse Configuration
  • Reports
• Advanced Topics
  • Subreports
  • FormulaMetrics
  • Divisor Metrics
  • Divisor Metrics
  • FormulaDimensions
  • DataSource Formulas
  • Type Conversions
  • AdHocMetrics
  • AdHocDimensions
  • AdHocDataTables
  • Technicals
  • Config Variables
  • DataSource Priority
• Supported DataSources
• Multiprocess Considerations
• Demo UI / Web API
• Docs
• How to Contribute

Installation

> Warning: This project is in an alpha state and is subject to change. Please test carefully for production usage and report any issues.

$ pip install zillion

or

$ pip install zillion[nlp]

---

Primer

The following is meant to give a quick overview of some theory and nomenclature used in data warehousing with Zillion which will be useful if you are newer to this area. You can also skip below for a usage example or warehouse/datasource creation quickstart options.

In short: Zillion writes SQL for you and makes data accessible through a very simple API:

result = warehouse.execute(
    metrics=["revenue", "leads"],
    dimensions=["date"],
    criteria=[
        ("date", ">", "2020-01-01"),
        ("partner", "=", "Partner A")
    ]
)

Metrics and Dimensions

In Zillion there are two main types of Fields that will be used in your report requests:

1. Dimensions: attributes of data used for labelling, grouping, and filtering
2. Metrics: facts and measures that may be broken down along dimensions

A Field encapsulates the concept of a column in your data. For example, you may have a Field called "revenue". That Field may occur across several datasources or possibly in multiple tables within a single datasource. Zillion understands that all of those columns represent the same concept, and it can try to use any of them to satisfy reports requesting "revenue".

Likewise there are two main types of tables used to structure your warehouse:

1. Dimension Tables: reference/attribute tables containing only related dimensions
2. Metric Tables: fact tables that may contain metrics and some related dimensions/attributes

Dimension tables are often static or slowly growing in terms of row count and contain attributes tied to a primary key. Some common examples would be lists of US Zip Codes or company/partner directories.

Metric tables are generally more transactional in nature. Some common examples would be records for web requests, ecommerce sales, or stock market price history.

Warehouse Theory

If you really want to go deep on dimensional modeling and the drill-across querying technique Zillion employs, I recommend reading Ralph Kimball's book on data warehousing.

To summarize, drill-across querying forms one or more queries to satisfy a report request for metrics that may exist across multiple datasources and/or tables at a particular dimension grain.

Zillion supports flexible warehouse setups such as snowflake or star schemas, though it isn't picky about it. You can specify table relationships through a parent-child lineage, and Zillion can also infer acceptable joins based on the presence of dimension table primary keys. Zillion does not support many-to-many relationships at this time, though most analytics-focused scenarios should be able to work around that by adding views to the model if needed.

Query Layers

Zillion reports can be thought of as running in two layers:

1. DataSource Layer: SQL queries against the warehouse's datasources
2. Combined Layer: A final SQL query against the combined data from the DataSource Layer

The Combined Layer is just another SQL database (in-memory SQLite by default) that is used to tie the datasource data together and apply a few additional features such as rollups, row filters, row limits, sorting, pivots, and technical computations.

Warehouse Creation

There are multiple ways to quickly initialize a warehouse from a local or remote file:

# Path/link to a CSV, XLSX, XLS, JSON, HTML, or Google Sheet
# This builds a single-table Warehouse for quick/ad-hoc analysis.
url = "https://raw.githubusercontent.com/totalhack/zillion/master/tests/dma_zip.xlsx"
wh = Warehouse.from_data_file(url, ["Zip_Code"]) # Second arg is primary key

# Path/link to a sqlite database
# This can build a single or multi-table Warehouse
url = "https://github.com/totalhack/zillion/blob/master/tests/testdb1?raw=true"
wh = Warehouse.from_db_file(url)

# Path/link to a WarehouseConfigSchema (or pass a dict)
# This is the recommended production approach!
config = "https://raw.githubusercontent.com/totalhack/zillion/master/examples/example_wh_config.json"
wh = Warehouse(config=config)

Zillion also provides a helper script to boostrap a DataSource configuration file for an existing database. See zillion.scripts.bootstrap_datasource_config.py. The bootstrap script requires a connection/database url and output file as arguments. See --help output for more options, including the optional --nlp flag that leverages OpenAI to infer configuration information such as column types, table types, and table relationships. The NLP feature requires the NLP extension to be installed as well as the following set in your Zillion config file:

• OPENAI_MODEL
• OPENAI_API_KEY

Executing Reports

The main purpose of Zillion is to execute reports against a Warehouse. At a high level you will be crafting reports as follows:

result = warehouse.execute(
    metrics=["revenue", "leads"],
    dimensions=["date"],
    criteria=[
        ("date", ">", "2020-01-01"),
        ("partner", "=", "Partner A")
    ]
)
print(result.df) # Pandas DataFrame

When comparing to writing SQL, it's helpful to think of the dimensions as the target columns of a group by SQL statement. Think of the metrics as the columns you are aggregating. Think of the criteria as the where clause. Your criteria are applied in the DataSource Layer SQL queries.

The ReportResult has a Pandas DataFrame with the dimensions as the index and the metrics as the columns.

A Report is said to have a grain, which defines the dimensions each metric must be able to join to in order to satisfy the Report requirements. The grain is a combination of all dimensions, including those referenced in criteria or in metric formulas. In the example above, the grain would be {date, partner}. Both "revenue" and "leads" must be able to join to those dimensions for this report to be possible.

These concepts can take time to sink in and obviously vary with the specifics of your data model, but you will become more familiar with them as you start putting together reports against your data warehouses.

Natural Language Querying

With the NLP extension Zillion has experimental support for natural language querying of your data warehouse. For example:

result = warehouse.execute_text("revenue and leads by date last month")
print(result.df) # Pandas DataFrame

This NLP feature requires a running instance of Qdrant (vector database) and the following values set in your Zillion config file:

• QDRANT_HOST
• OPENAI_API_KEY

Embeddings will be produced and stored in both Qdrant and a local cache. The vector database will be initialized the first time you try to use this by analyzing all fields in your warehouse. An example docker file to run Qdrant is provided in the root of this repo.

You have some control over how fields get embedded. Namely in the configuration for any field you can choose whether to exclude a field from embeddings or override which embeddings map to that field. All fields are included by default. The following example would exclude the net_revenue field from being embedded and map revenue metric requests to the gross_revenue field.

{
    "name": "gross_revenue",
    "type": "numeric(10,2)",
    "aggregation": "sum",
    "rounding": 2,
    "meta": {
        "nlp": {
            // enabled defaults to true
            "embedding_text": "revenue" // str or list of str
        }
    }
},
{
    "name": "net_revenue",
    "type": "numeric(10,2)",
    "aggregation": "sum",
    "rounding": 2,
    "meta": {
        "nlp": {
            "enabled": false
        }
    }
},

Additionally you may also exclude fields via the following warehouse-level configuration settings:

{
    "meta": {
        "nlp": {
            "field_disabled_patterns": [
                // list of regex patterns to exclude
                "rpl_ma_5"
            ],
            "field_disabled_groups": [
                // list of "groups" to exclude, assuming you have
                // set group value in the field's meta dict.
                "No NLP"
            ]
        }
    },
    ...
}

If a field is disabled at any of the aforementioned levels it will be ignored. This type of control becomes useful as your data model gets more complex and you want to guide the NLP logic in cases where it could confuse similarly named fields. Any time you adjust which fields are excluded you will want to force recreation of your embeddings collection using the force_recreate flag on Warehouse.init_embeddings.

> Note: This feature is in its infancy. It's usefulness will depend on the quality of both the input query and your data model (i.e. good field names)!

Zillion Configuration

In addition to configuring the structure of your Warehouse, which will be discussed further below, Zillion has a global configuration to control some basic settings. The ZILLION_CONFIG environment var can point to a yaml config file. See examples/sample_config.yaml for more details on what values can be set. Environment vars prefixed with ZILLION_ can override config settings (i.e. ZILLION_DB_URL will override DB_URL).

The database used to store Zillion report specs can be configured by setting the DB_URL value in your Zillion config to a valid database connection string. By default a SQLite DB in /tmp is used.

---

Example - Sales Analytics

Below we will walk through a simple hypothetical sales data model that demonstrates basic DataSource and Warehouse configuration and then shows some sample reports. The data is a simple SQLite database that is part of the Zillion test code. For reference, the schema is as follows:

CREATE TABLE partners (
  id INTEGER PRIMARY KEY,
  name VARCHAR NOT NULL UNIQUE,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE campaigns (
  id INTEGER PRIMARY KEY,
  name VARCHAR NOT NULL UNIQUE,
  category VARCHAR NOT NULL,
  partner_id INTEGER NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE leads (
  id INTEGER PRIMARY KEY,
  name VARCHAR NOT NULL,
  campaign_id INTEGER NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE sales (
  id INTEGER PRIMARY KEY,
  item VARCHAR NOT NULL,
  quantity INTEGER NOT NULL,
  revenue DECIMAL(10, 2),
  lead_id INTEGER NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Warehouse Configuration

A Warehouse may be created from a JSON or YAML configuration that defines its fields, datasources, and tables. The code below shows how it can be done in as little as one line of code if you have a pointer to a JSON/YAML Warehouse config.

from zillion import Warehouse

wh = Warehouse(config="https://raw.githubusercontent.com/totalhack/zillion/master/examples/example_wh_config.json")

This example config uses a data_url in its DataSource connect info that tells Zillion to dynamically download that data and connect to it as a SQLite database. This is useful for quick examples or analysis, though in most scenarios you would put a connection string to an existing database like you see here

The basics of Zillion's warehouse configuration structure are as follows:

A Warehouse config has the following main sections:

• metrics: optional list of metric configs for global metrics
• dimensions: optional list of dimension configs for global dimensions
• datasources: mapping of datasource names to datasource configs or config URLs

A DataSource config has the following main sections:

• connect: database connection url or dict of connect params
• metrics: optional list of metric configs specific to this datasource
• dimensions: optional list of dimension configs specific to this datasource
• tables: mapping of table names to table configs or config URLs

> Tip: datasource and table configs may also be replaced with a URL that points to a local or remote config file.

In this example all four tables in our database are included in the config, two as dimension tables and two as metric tables. The tables are linked through a parent->child relationship: partners to campaigns, and leads to sales. Some tables also utilize the create_fields flag to automatically create Fields on the datasource from column definitions. Other metrics and dimensions are defined explicitly.

To view the structure of this Warehouse after init you can use the print_info method which shows all metrics, dimensions, tables, and columns that are part of your data warehouse:

wh.print_info() # Formatted print of the Warehouse structure

For a deeper dive of the config schema please see the full docs.

Reports

Example: Get sales, leads, and revenue by partner:

result = wh.execute(
    metrics=["sales", "leads", "revenue"],
    dimensions=["partner_name"]
)

print(result.df)
"""
              sales  leads  revenue
partner_name
Partner A        11      4    165.0
Partner B         2      2     19.0
Partner C         5      1    118.5
"""

Example: Let's limit to Partner A and break down by its campaigns:

result = wh.execute(
    metrics=["sales", "leads", "revenue"],
    dimensions=["campaign_name"],
    criteria=[("partner_name", "=", "Partner A")]
)

print(result.df)
"""
               sales  leads  revenue
campaign_name
Campaign 1A        5      2       83
Campaign 2A        6      2       82
"""

Example: The output below shows rollups at the campaign level within each partner, and also a rollup of totals at the partner and campaign level.

> Note: the output contains a special character to mark DataFrame rollup rows that were added to the result. The ReportResult object contains some helper attributes to automatically access or filter rollups, as well as a df_display attribute that returns the result with friendlier display values substituted for special characters. The under-the-hood special character is left here for illustration, but may not render the same in all scenarios.

from zillion import RollupTypes

result = wh.execute(
    metrics=["sales", "leads", "revenue"],
    dimensions=["partner_name", "campaign_name"],
    rollup=RollupTypes.ALL
)

print(result.df)
"""
                            sales  leads  revenue
partner_name campaign_name
Partner A    Campaign 1A      5.0    2.0     83.0
             Campaign 2A      6.0    2.0     82.0
             􏿿               11.0    4.0    165.0
Partner B    Campaign 1B      1.0    1.0      6.0
             Campaign 2B      1.0    1.0     13.0
             􏿿                2.0    2.0     19.0
Partner C    Campaign 1C      5.0    1.0    118.5
             􏿿                5.0    1.0    118.5
􏿿            􏿿               18.0    7.0    302.5
"""

See the Report docs for more information on supported rollup behavior.

Example: Save a report spec (not the data):

First you must make sure you have saved your Warehouse, as saved reports are scoped to a particular Warehouse ID. To save a Warehouse you must provide a URL that points to the complete config.

name = "My Unique Warehouse Name"
config_url = 
wh.save(name, config_url) # wh.id is populated after this

spec_id = wh.save_report(
    metrics=["sales", "leads", "revenue"],
    dimensions=["partner_name"]
)

> Note: If you built your Warehouse in python from a list of DataSources, or passed in a dict for the config param on init, there currently is not a built-in way to output a complete config to a file for reference when saving.

Example: Load and run a report from a spec ID:

result = wh.execute_id(spec_id)

This assumes you have saved this report ID previously in the database specified by the DB_URL in your Zillion yaml configuration.

Example: Unsupported Grain

If you attempt an impossible report, you will get an UnsupportedGrainException. The report below is impossible because it attempts to break down the leads metric by a dimension that only exists in a child table. Generally speaking, child tables can join back up to parents (and "siblings" of parents) to find dimensions, but not the other way around.

# Fails with UnsupportedGrainException
result = wh.execute(
    metrics=["leads"],
    dimensions=["sale_id"]
)

---

Advanced Topics

Subreports

Sometimes you need subquery-like functionality in order to filter one report to the results of some other (that perhaps required a different grain). Zillion provides a simplistic way of doing that by using the in report or not in report criteria operations. There are two supported ways to specify the subreport: passing a report spec ID or passing a dict of report params.

# Assuming you have saved report 1234 and it has "partner" as a dimension:

result = warehouse.execute(
    metrics=["revenue", "leads"],
    dimensions=["date"],
    criteria=[
        ("date", ">", "2020-01-01"),
        ("partner", "in report", 1234)
    ]
)

# Or with a dict:

result = warehouse.execute(
    metrics=["revenue", "leads"],
    dimensions=["date"],
    criteria=[
        ("date", ">", "2020-01-01"),
        ("partner", "in report", dict(
            metrics=[...],
            dimension=["partner"],
            criteria=[...]
        ))
    ]
)

The criteria field used in in report or not in report must be a dimension in the subreport. Note that subreports are executed at Report object initialization time instead of during execute -- as such they can not be killed using Report.kill. This may change down the road.

Formula Metrics

In our example above our config included a formula-based metric called "rpl", which is simply revenue / leads. A FormulaMetric combines other metrics and/or dimensions to calculate a new metric at the Combined Layer of querying. The syntax must match your Combined Layer database, which is SQLite in our example.

{
    "name": "rpl",
    "aggregation": "mean",
    "rounding": 2,
    "formula": "{revenue}/{leads}"
}

Divisor Metrics

As a convenience, rather than having to repeatedly define formula metrics for rate variants of a core metric, you can specify a divisor metric configuration on a non-formula metric. As an example, say you have a revenue metric and want to create variants for revenue_per_lead and revenue_per_sale. You can define your revenue metric as follows:

{
    "name": "revenue",
    "type": "numeric(10,2)",
    "aggregation": "sum",
    "rounding": 2,
    "divisors": {
        "metrics": [
            "leads",
            "sales"
        ]
    }
}

See zillion.configs.DivisorsConfigSchema for more details on configuration options, such as overriding naming templates, formula templates, and rounding.

Aggregation Variants

Another minor convenience feature is the ability to automatically generate variants of metrics for different aggregation types in a single field configuration instead of across multiple fields in your config file. As an example, say you have a sales column in your data and want to create variants for sales_mean and sales_sum. You can define your metric as follows:

{
    "name": "sales",
    "aggregation": {
        "mean": {
            "type": "numeric(10,2)",
            "rounding": 2
        },
        "sum": {
            "type": "integer"
        }
    }
}

The final config would not have a sales metric, but would instead have sales_mean and sales_sum. Note that you can further customize the settings for the generated fields, such as getting a custom name, by specifying that in the nested settings for that aggregation type. In practice it's not a big savings over just defining the metrics separately, but some may prefer this approach.

Formula Dimensions

Experimental support exists for FormulaDimension fields as well. A FormulaDimension can only use other dimensions as part of its formula, and it also gets evaluated in the Combined Layer database. As an additional restriction, a FormulaDimension can not be used in report criteria as those filters are evaluated at the DataSource Layer. The following example assumes a SQLite Combined Layer database:

{
    "name": "partner_is_a",
    "formula": "{partner_name} = 'Partner A'"
}

DataSource Formulas

Our example also includes a metric "sales" whose value is calculated via formula at the DataSource Layer of querying. Note the following in the fields list for the "id" param in the "main.sales" table. These formulas are in the syntax of the particular DataSource database technology, which also happens to be SQLite in our example.

"fields": [
    "sale_id",
    {"name":"sales", "ds_formula": "COUNT(DISTINCT sales.id)"}
]

Type Conversions

Our example also automatically created a handful of dimensions from the "created_at" columns of the leads and sales tables. Support for automatic type conversions is limited, but for date/datetime columns in supported DataSource technologies you can get a variety of dimensions for free this way.

The output of wh.print_info will show the added dimensions, which are prefixed with "lead_" or "sale_" as specified by the optional type_conversion_prefix in the config for each table. Some examples of auto-generated dimensions in our example warehouse include sale_hour, sale_day_name, sale_month, sale_year, etc.

As an optimization in the where clause of underlying report queries, Zillion will try to apply conversions to criteria values instead of columns. For example, it is generally more efficient to query as my_datetime > '2020-01-01' and my_datetime < '2020-01-02' instead of DATE(my_datetime) == '2020-01-01', because the latter can prevent index usage in many database technologies. The ability to apply conversions to values instead of columns varies by field and DataSource technology as well.

To prevent type conversions, set skip_conversion_fields to true on your DataSource config.

See zillion.field.TYPE_ALLOWED_CONVERSIONS and zillion.field.DIALECT_CONVERSIONS for more details on currently supported conversions.

Ad Hoc Metrics

You may also define metrics "ad hoc" with each report request. Below is an example that creates a revenue-per-lead metric on the fly. These only exist within the scope of the report, and the name can not conflict with any existing fields:

result = wh.execute(
    metrics=[
        "leads",
        {"formula": "{revenue}/{leads}", "name": "my_rpl"}
    ],
    dimensions=["partner_name"]
)

Ad Hoc Dimensions

You may also define dimensions "ad hoc" with each report request. Below is an example that creates a dimension that partitions on a particular dimension value on the fly. Ad Hoc Dimensions are a subclass of FormulaDimensions and therefore have the same restrictions, such as not being able to use a metric as a formula field. These only exist within the scope of the report, and the name can not conflict with any existing fields:

result = wh.execute(
    metrics=["leads"],
    dimensions=[{"name": "partner_is_a", "formula": "{partner_name} = 'Partner A'"]
)

Ad Hoc Tables

Zillion also supports creation or syncing of ad hoc tables in your database during DataSource or Warehouse init. An example of a table config that does this is shown here. It uses the table config's data_url and if_exists params to control the syncing and/or creation of the "main.dma_zip" table from a remote CSV in a SQLite database. The same can be done in other database types too.

The potential performance drawbacks to such an approach should be obvious, particularly if you are initializing your warehouse often or if the remote data file is large. It is often better to sync and create your data ahead of time so you have complete schema control, but this method can be very useful in certain scenarios.

> Warning: be careful not to overwrite existing tables in your database!

Technicals

There are a variety of technical computations that can be applied to metrics to compute rolling, cumulative, or rank statistics. For example, to compute a 5-point moving average on revenue one might define a new metric as follows:

{
    "name": "revenue_ma_5",
    "type": "numeric(10,2)",
    "aggregation": "sum",
    "rounding": 2,
    "technical": "mean(5)"
}

Technical computations are computed at the Combined Layer, whereas the "aggregation" is done at the DataSource Layer (hence needing to define both above).

For more info on how shorthand technical strings are parsed, see the parse_technical_string code. For a full list of supported technical types see zillion.core.TechnicalTypes.

Technicals also support two modes: "group" and "all". The mode controls how to apply the technical computation across the data's dimensions. In "group" mode, it computes the technical across the last dimension, whereas in "all" mode in computes the technical across all data without any regard for dimensions.

The point of this becomes more clear if you try to do a "cumsum" technical across data broken down by something like ["partner_name", "date"]. If "group" mode is used (the default in most cases) it will do cumulative sums within each partner over the date ranges. If "all" mode is used, it will do a cumulative sum across every data row. You can be explicit about the mode by appending it to the technical string: i.e. "cumsum:all" or "mean(5):group"

---

Config Variables

If you'd like to avoid putting sensitive connection information directly in your DataSource configs you can leverage config variables. In your Zillion yaml config you can specify a DATASOURCE_CONTEXTS section as follows:

DATASOURCE_CONTEXTS:
  my_ds_name:
    user: user123
    pass: goodpassword
    host: 127.0.0.1
    schema: reporting

Then when your DataSource config for the datasource named "my_ds_name" is read, it can use this context to populate variables in your connection url:

"datasources": {
    "my_ds_name": {
        "connect": "mysql+pymysql://{user}:{pass}@{host}/{schema}"
        ...
    }
}

DataSource Priority

On Warehouse init you can specify a default priority order for datasources by name. This will come into play when a report could be satisfied by multiple datasources. DataSources earlier in the list will be higher priority. This would be useful if you wanted to favor a set of faster, aggregate tables that are grouped in a DataSource.

wh = Warehouse(config=config, ds_priority=["aggr_ds", "raw_ds", ...])

Supported DataSources

Zillion's goal is to support any database technology that SQLAlchemy supports (pictured below). That said the support and testing levels in Zillion vary at the moment. In particular, the ability to do type conversions, database reflection, and kill running queries all require some database-specific code for support. The following list summarizes known support levels. Your mileage may vary with untested database technologies that SQLAlchemy supports (it might work just fine, just hasn't been tested yet). Please report bugs and help add more support!

• SQLite: supported
• MySQL: supported
• PostgreSQL: supported
• DuckDB: supported
• BigQuery, Redshift, Snowflake, SingleStore, PlanetScale, etc: not tested but would like to support these

SQLAlchemy has connectors to many popular databases. The barrier to support many of these is likely pretty low given the simple nature of the sql operations Zillion uses.

Note that the above is different than the database support for the Combined Layer database. Currently only SQLite is supported there; that should be sufficient for most use cases but more options will be added down the road.

Multiprocess Considerations

If you plan to run Zillion in a multiprocess scenario, whether on a single node or across multiple nodes, there are a couple of things to consider:

• SQLite DataSources do not scale well and may run into locking issues with multiple processes trying to access them on the same node.
• Any file-based database technology that isn't centrally accessible would be challenging when using multiple nodes.
• Ad Hoc DataSource and Ad Hoc Table downloads should be avoided as they may conflict/repeat across each process. Offload this to an external ETL process that is better suited to manage those data flows in a scalable production scenario.

Note that you can still use the default SQLite in-memory Combined Layer DB without issues, as that is made on the fly with each report request and requires no coordination/communication with other processes or nodes.

Demo UI / Web API

Zillion Web UI is a demo UI and web API for Zillion that also includes an experimental ChatGPT plugin. See the README there for more info on installation and project structure. Please note that the code is light on testing and polish, but is expected to work in modern browsers. Also ChatGPT plugins are quite slow at the moment, so currently that is mostly for fun and not that useful.

---

Documentation

More thorough documentation can be found here. You can supplement your knowledge by perusing the tests directory or the API reference.

---

How to Contribute

Please See the contributing guide for more information. If you are looking for inspiration, adding support and tests for additional database technologies would be a great help.