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EtlKit is a lightweight ETL (extract · transform · load) library and data integration toolbox for .NET. Build your own data pipelines programmatically in C# — readable, testable, and tracked in source control like the rest of your code.

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# EtlKit

EtlKit is a fully open-source (MIT) ETL library and data integration toolbox for .NET.

This project originated as a fork of the original ETLBox library by Andreas Lennartz. Starting
with version 2.0, the original author decided to close the source and commercialize its newer
branch. EtlKit continues the open-source 1.x lineage, keeping it up to date with modern .NET and
database drivers while adding new features.

## The 2.0 rename: EtlBox.Classic → EtlKit

> ⚠️ **Breaking change in 2.0** ⚠️
>
> As of version 2.0, the project formerly known as **EtlBox.Classic** has been renamed to
> **EtlKit**. This avoids versioning and package naming conflicts with the original closed-source
> ETLBox library.
>
> The rename is comprehensive — NuGet package IDs, .NET root namespaces, the solution file, and DI
> registration extension methods have all changed. The class names and public API surface of every
> component are otherwise identical to 1.x; this is purely a rename, not a redesign.

**What changed:**

- **NuGet packages**: `EtlBox.Classic.*` → `EtlKit.*` (e.g. `EtlBox.Classic` → `EtlKit`,
  `EtlBox.Classic.Kafka` → `EtlKit.Kafka`)
- **Root namespaces**: `ALE.ETLBox.*` → `EtlKit.*` (the `ALE.` prefix is dropped entirely)
- **DI extensions**: `AddEtlBoxCore()` → `AddEtlKitCore()`, `AddEtlBoxJson()` → `AddEtlKitJson()`,
  etc.
- **Exception type**: `EtlBoxException` → `EtlKitException`
- **Solution file**: `ETLBox.sln` → `EtlKit.sln`

**Migrating from 1.x to 2.0:**

1. Update all `<PackageReference>` entries from `EtlBox.Classic.*` to `EtlKit.*`.
2. Find-and-replace `using ALE.ETLBox` → `using EtlKit` across all `.cs` files.
3. Find-and-replace `EtlBoxException` → `EtlKitException`.
4. Find-and-replace `AddEtlBox` → `AddEtlKit` in DI registration calls.
5. Update solution references from `ETLBox.sln` to `EtlKit.sln`.

See the [CHANGELOG](CHANGELOG.md) for the full mapping table of old → new package IDs and
namespaces.

## Documentation

- [Guides and tutorials](docs/README.md) — `docs/` contains hand-written guides and tutorials, browsable directly in the repo
- [API Reference](https://etlkit.github.io/etlkit/) — `docfx/` contains the DocFx config that generates the hosted API reference from XML comments

## Installation

EtlKit targets .NET Standard 2.0, so it works in any .NET project that supports it (basically all
modern .NET versions).

[EtlKit is available on NuGet](https://www.nuget.org/packages?q=EtlKit). Add the package to your
project via your NuGet package manager.

See individual package descriptions to understand what each one does. For a basic setup you only
need [EtlKit](https://www.nuget.org/packages/EtlKit).

## What is EtlKit

A lightweight ETL (extract, transform, load) library and data integration toolbox for .NET. Source
and destination components let you read and write data from the most common databases and file
types. Transformations allow you to harmonize, filter, aggregate, validate and clean your data.

Create your own tailor-made data flow with your .NET language of choice. EtlKit is written in C#
and offers full support for .NET Core.

## Why EtlKit

EtlKit is a comprehensive C# class library that is able to manage your
whole [ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load)
or [ELT](https://en.wikipedia.org/wiki/Extract,_load,_transform). You can use it to create your
own dataflow pipelines programmatically in .NET, e.g. with C#. Besides a big set of dataflow
components it comes with some control flow tasks that let you easily manage your database or simply
execute SQL code without any boilerplate code. It also offers extended logging capabilities based
on ILogger to monitor and analyze your ETL job runs.

EtlKit is a fully functional alternative to other ETL tools like Sql Server Integration Services
(SSIS). Creating your ETL processes programmatically has some advantages:

**Build ETL in .NET**: Code your ETL with your favorite .NET language, fitting your team's skills
and leveraging a mature toolset.

**Runs everywhere**: EtlKit runs on Linux, macOS, and Windows. It is written in .NET Standard and
successfully tested with the latest versions of .NET Core & .NET.

**Run locally**: Develop and test your ETL code locally on your desktop using your existing
development & debugging tools.

**Process In-Memory**: EtlKit comes with dataflow components that allow in-memory processing which
is much faster than storing data on disk and processing later.

**Manage Change**: Track your changes with git (or other source controls), code review your ETL
logic, and use your existing CI/CD processes.

**Data integration**: While supporting different databases, flat files and web services, you can
use EtlKit as a foundation for your custom-made Data Integration platform.

**Made for big data**: EtlKit relies
on [Microsoft's TPL.Dataflow library](https://docs.microsoft.com/en-us/dotnet/standard/parallel-programming/dataflow-task-parallel-library)
and was designed to work with big amounts of data.

## Data Flow and Control Flow

EtlKit is split into two main components: Data Flow and Control Flow Tasks. The Data Flow part
offers the core ETL components. The tasks in the Control Flow allow you to manage your databases
with a simple syntax. Both components come with customizable logging functionalities.

### Data Flow overview

EtlKit comes with a set of Data Flow components to construct your own ETL pipeline. You can connect with different
sources (e.g. a Csv file), add some transformations to manipulate that data on-the-fly (e.g. calculating a sum or
combining two columns) and then store the changed data in a connected destination (e.g. a database table).

To create your own data flow , you basically follow three steps:

- First you define your dataflow components (sources, optionally transformations and destinations)
- link these components together
- tell your source to start reading the data and wait for the destination to finish

Now the source will start reading and post its data into the components connected to its output. As soon as a connected
component retrieves any data in its input, the component will start with processing the data and then send it further
down the line to its connected components. The dataflow will finish when all data from the source(s) are read, processed
by the transformations and received in the destination(s).

Transformations are not always needed - you can directly connect a source to a destination. Normally, each source has
one output, each destination one input and each transformation at least one input and one or more outputs.

Of course, all data is processed asynchronously by the components. Each compoment has its own set of buffers, so while
the source is still reading data, the transformations can already process it and the destinations can start writing the
processed information into their target. So in an optimal flow only the current row needed for processing is stored in
memory. Depending on the processing speed of your components, the buffer of each component can store additional rows to
optimize throughput.

#### Data Flow example

It's easy to create your own data flow pipeline. This example data flow will read data from a MySql database, modify a
value and then store the modified data in a Sql Server table and a csv file, depending on a filter expression.

Step 1 is to create a source, the transformations and destinations:

```C#
var sourceCon = new MySqlConnectionManager("Server=10.37.128.2;Database=EtlKit_ControlFlow;Uid=etlkit;Pwd=etlkitpassword;");
var destCon = new SqlConnectionManager("Data Source=.;Integrated Security=SSPI;Initial Catalog=EtlKit;");

DbSource<MySimpleRow> source = new DbSource<MySimpleRow>(sourceCon, "SourceTable");
RowTransformation<MySimpleRow, MySimpleRow> rowTrans = new RowTransformation<MySimpleRow, MySimpleRow>(
    row => {  
        row.Value += 1;
        return row;
    });
Multicast<MySimpleRow> multicast = new Multicast<MySimpleRow>();
DbDestination<MySimpleRow> sqlDest = new DbDestination<MySimpleRow>(destCon, "DestinationTable");
CsvDestination<MySimpleRow> csvDest = new CsvDestination<MySimpleRow>("example.csv");
```

Now we link these elements together.

```C#
source.LinkTo(trans);
rowTrans.LinkTo(multicast);
multicast.LinkTo(sqlDest, row => row.FilterValue > 0);
multicast.LinkTo(csvDest, row => row.FilterValue < 0);
```

Finally, start the dataflow at the source and wait for the destinations to rececive all data (and the completion message
from the source).

```C#
source.Execute();
sqlDest.Wait();
csvDest.Wait();
```

### Data integration

The heart of an ETL framework is it's ability to integrate with other systems.
The following table shows which types of sources and destination are supported out-of-the box with the current version
of EtlKit.
**You can *always* integrate any other system not listed here by using a `CustomSource` or `CustomDestination` - though
you have to write the integration code yourself.**

| Source or Destination | Support for                                     | Limitations                                       |
| --------------------- | ----------------------------------------------- | ------------------------------------------------- |
| Databases             | Sql Server, Postgres, SQLite, MySql, Clickhouse | Full support                                      |
| Queues and streaming  | Kafka, RabbitMQ                                 | Kafka — full support, RabbitMQ — destination only |
| Flat files            | Csv, Json, Xml                                  | Full support                                      |
| Office                | Microsoft Access, Excel                         | Full support for Access, Excel only as source     |
| Cube                  | Sql Server Analysis Service                     | Only XMLA statements                              |
| Memory                | .NET IEnumerable & Collections                  | Full support                                      |
| API                   | REST, OpenAI                                    | Full support                                      |
| Cloud Services        | Tested with Azure                               | Full support                                      |
| Any other             | integration with custom written code            | No limitations                                    |

You can choose between different sources and destination components. `DbSource` and `DbDestination` will connect to the
most used databases (e.g. Sql Server, Postgres, MySql, SQLite). `CsvSource`, `CsvDestination` give you support for flat
files - [based on CSVHelper](https://joshclose.github.io/CsvHelper/). `ExcelSource` allows you to read data from an
excel sheet. `JsonSource`, `JsonDestination`, `XmlSource` and `XmlDestination` let you read and write json from files or
web service request. `MemorySource`, `MemoryDestinatiation` as well as `CustomSource` and `CustomDestination` will give
you a lot flexibility to read or write data directly from memory or to create your own custom made source or destination
component.

### Transformations

EtlKit has 3 types of transformations: Non-blocking, partially blocking and blocking transformations. Non-blocking
transformations will
only store the row that is currently processed in memory (plus some more in the buffer to optimize throughput and
performance). Partially blocking transformations will load some data in the memory before they process data row-by-row.
Blocking transformations will wait until all data has arrived at the component before it starts processing all records
subsequently.

The following table is an overview of the most common transformations in EtlKit:

| Non-blocking              | Partially blocking   | Blocking            |
| ------------------------- | -------------------- | ------------------- |
| RowTransformation         | LookupTransformation | BlockTransformation |
| RowBatchTransformation    |                      |                     |
| Aggregation               | CrossJoin            | Sort                |
| MergeJoin                 |                      |                     |
| Multicast                 |                      |                     |
| RowDuplication            |                      |                     |
| RowMultiplication         |                      |                     |
| RowFiltration             |                      |                     |
| ExpressionRowFiltration   |                      |                     |
| JsonTransformation        |                      |                     |
| ScriptedRowTransformation |                      |                     |
| AIBatchTransformation     |                      |                     |

#### Designed for big data

EtlKit was designed for performance and is able to deal with big amounts of data. All destinations do support Bulk or
Batch operations. By default, every component comes with an input and/or output buffer. You can design your data flow
that only batches or your data is stored in memory, which are kept in different buffers for every component to increase
throughput. All operations can be execute asynchrounously, so that your processing will run only within separate
threads.

#### Data transformations

Data transformations take input data from source, perform an external operation (via DB, API, or queue) and return
produced result into a destination. Namely data transformations are:

| Transformation            | Input                                                          | Processing                                         | Output                                                                                  |
| ------------------------- | -------------------------------------------------------------- | -------------------------------------------------- | --------------------------------------------------------------------------------------- |
| SqlQueryTransformation    | Parameters to a liquid-based SQL query template                | Execute SQL query                                  | SQL query results (0..N for each input row)                                             |
| SqlCommmandTransformation | Parameters to a liquid-based SQL query template                | Execute SQL non-query statement                    | Input object (or this can be customised with Transform delegate) — 1 for each input row |
| KafkaTransformation       | Parameters to liquid-based message and key templates           | Produce messages to Kafka topic (optional key)     | Input object or null                                                                    |
| RabbitMqTransformation    | Parameters to a liquid-based string message template           | Publish messages to RabbitMQ channel               | Input object or null                                                                    |
| JsonTransformation        | Json object                                                    | Execute JSON path transformation for each field    | Output object where each field is the result of Json path evaluation                    |
| RestTransformation        | Parameters to a liquid-based request URL and body templates    | Execute HTTP request                               | ExpandoObject with response code, raw body and Json parsed body in fields               |
| ScriptRowTransformation   | Object as Globals to C# script templates for each result field | Execute C# code                                    | Result object with one field per script                                                 |
| AIBatchTransformation     | Parameters to a liquid-based AI prompt template                | Execute request to configured OpenAI HTTP endpoint | ExpandoObject with response code, raw body and Json parsed body in fields               |

### Control Flow - overview

Control Flow Tasks gives you control over your database: They allow you to create or delete databases, tables,
procedures, schemas or other objects in your database. With these tasks you also can truncate your tables, count rows or
execute *any* sql you like. Anything you can script in sql can be done here - but with only one line of easy-to-read C#
code. This improves the readability of your code a lot, and gives you more time to focus on your business logic.

Code tells - here is some example code, without writing the whole "boilerplate" code by ADO.NET.

```C#
var conn = new SqlConnectionManager("Server=10.37.128.2;Database=EtlKit_ControlFlow;Uid=etlkit;Pwd=etlkitpassword;");
//Execute some Sql
SqlTask.ExecuteNonQuery(conn, "Do some sql",$@"EXEC myProc");
//Count rows
int count = RowCountTask.Count(conn, "demo.table1").Value;
//Create a table (works on all databases)
CreateTableTask.Create(conn, "Table1", new List<TableColumn>() {
    new TableColumn(name:"key",dataType:"INT",allowNulls:false,isPrimaryKey:true, isIdentity:true),
    new TableColumn(name:"value", dataType:"NVARCHAR(100)",allowNulls:true)
});
```

### Logging

EtlKit uses [Microsoft.Extensions.Logging](https://www.nuget.org/packages/Microsoft.Extensions.Logging/)
which is a de-facto standard for modern .NET. EtlKit also has its own logging-to-database capabilities,
implemented in the `EtlKit.Logging.Database` package, which has its own configuration and uses NLog internally
at the time being.

## Contribution

Clone the repository:

```bash
git clone https://github.com/etlkit/etlkit.git
```

Then, open the downloaded solution file `EtlKit.sln` with Visual Studio 2022 or JetBrains Rider.
Now you can build the solution, and use it as a reference in other projects.

### Running tests

See [TEST_SETUP.md](TEST-SETUP.md) for instructions.

## Going further

EtlKit is open source. Feel free to make changes or to fix bugs. Every participation in this open
source project is appreciated.

To dig deeper into it, have a look at the test projects. There is a test for (almost) everything
that you can do with EtlKit.

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