
Product
Introducing Scala and Kotlin Support in Socket
Socket now supports Scala and Kotlin, bringing AI-powered threat detection to JVM projects with easy manifest generation and fast, accurate scans.
This is the SQL Server connector for ETLBox. It enables integration with Microsoft SQL Server for handling structured data in ETL workflows. ETLBox is a complete ETL (Extract, Transform, Load) library and data integration toolbox for .NET. # Build scalable, code-first ETL pipelines for SQL, NoSQL, APIs, and flat files. # Automate data movement, transformation, and synchronization with minimal memory usage. # Ideal for data warehousing, migrations, and big data processing. Simplify your data integration workflow: ETLBox enables efficient, asynchronous data processing by reading from databases, APIs, and file formats like CSV, Excel, and JSON. Transform data dynamically with row-based, batch, or lookup transformations, and read or write from/to multiple destinations in parallel. Key Features: * Stream large datasets efficiently without loading everything into memory * Maximize performance with parallel, task-based data flow execution * Connect to various data sources with built-in connectors or extend with custom components For tutorials, examples, and documentation, visit: https://www.etlbox.net/
Get Started | Overview | Code Examples | Licensing | Free Trial
ETLBox is a complete ETL (Extract, Transform, Load) library and data integration toolbox for .NET. It helps you create and manage data integration pipelines to extract data from different sources, transform it into the format you need, and load it into databases, APIs, or files. Whether you're migrating data, synchronizing systems, or automating workflows, ETLBox provides the components to handle these tasks efficiently.
ETLBox is a flexible alternative to traditional ETL tools like SQL Server Integration Services (SSIS) or Azure Data Factory. Unlike those tools, ETLBox does not rely on a graphical user interface, making it ideal for developers who prefer programmatic control. It supports parallel execution and streaming, allowing efficient processing of large data with minimal memory usage.
ETLBox lets you build data pipelines that process structured and unstructured data from various sources, including databases, APIs, and flat files. It automates tasks like data transformation, validation, and cleaning, and supports bulk inserts, updates, and deletes. With its ability to handle streaming data and parallel workflows, ETLBox delivers performance and flexibility for modern data integration tasks. Developers can rely on built-in connectors or extend the library to fit custom requirements.
Example 1: Bulk Insert into SQL Database
string connString =
@"Source=.;Trusted_Connection=true;Initial Catalog=ETLBox;"
var destCon = new SqlConnectionManager(connString);
//Create the dataflow components
var source = new CsvSource(sourceCon, "SourceData.csv");
var dest = new DbDestination(destCon, "DestinationTable");
//Default mapping: Csv header and column name match (case-sensitive)
//Override mapping like this:
dest.ColumnMapping = new[] {
new DbColumnMap() {
DbColumnName = "Id", PropertyName = "OrderNumber"
}
};
//Link the source and destination, run the dataflow
source.LinkTo(dest);
await Network.ExecuteAsnyc(source);
Example 2: Migrate Between Databases
string mySqlConnString =
@"Server=localhost;Database=ETLBox;Uid=user;Pwd=password;";
string postgresConnString =
@"Server=.;Initial Catalog=ETLBox;Trusted_Connection=true;";
var sourceCon = new MySqlConnectionManager(mySqlConnString);
var destCon = new PostgresConnectionManager(postgresConnString);
//Create the dataflow components
var source = new DbSource(sourceCon, "SourceTableName");
var dest = new DbDestination(destCon, "DestTableName");
//Link the source and destination, run the dataflow
source.LinkTo(dest);
await Network.ExecuteAsync(source);
Example 3: Merge Data Between Tables
public class MyMergeRow : MergeableRow
{
[IdColumn]
public long Key { get; set; }
[CompareColumn]
[UpdateColumn]
public string Value { get; set; }
[DeleteColumn(true)]
public bool DeleteThisRow { get; set; }
}
//Create the dataflow components
DbSource<MyMergeRow> source = new (connection, "SourceTable");
DbMerge<MyMergeRow> mergeDest = new (connection, "DestinationTable");
merge.MergeMode = MergeMode.Full;
merge.CacheMode = CacheMode.Partial;
//Link the source and destination, run the dataflow
source.LinkTo(merge);
Network.Execute(source);
For our full list of code examples, tutorials, licensing information, and documentation visit: www.etlbox.net
For more support and inquiries, please email us at: support@etlbox.net
Ready to build your ETL pipelines? Start now at www.etlbox.net.
FAQs
This is the SQL Server connector for ETLBox. It enables integration with Microsoft SQL Server for handling structured data in ETL workflows. ETLBox is a complete ETL (Extract, Transform, Load) library and data integration toolbox for .NET. # Build scalable, code-first ETL pipelines for SQL, NoSQL, APIs, and flat files. # Automate data movement, transformation, and synchronization with minimal memory usage. # Ideal for data warehousing, migrations, and big data processing. Simplify your data integration workflow: ETLBox enables efficient, asynchronous data processing by reading from databases, APIs, and file formats like CSV, Excel, and JSON. Transform data dynamically with row-based, batch, or lookup transformations, and read or write from/to multiple destinations in parallel. Key Features: * Stream large datasets efficiently without loading everything into memory * Maximize performance with parallel, task-based data flow execution * Connect to various data sources with built-in connectors or extend with custom components For tutorials, examples, and documentation, visit: https://www.etlbox.net/
We found that etlbox.sqlserver demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Product
Socket now supports Scala and Kotlin, bringing AI-powered threat detection to JVM projects with easy manifest generation and fast, accurate scans.
Application Security
/Security News
Socket CEO Feross Aboukhadijeh and a16z partner Joel de la Garza discuss vibe coding, AI-driven software development, and how the rise of LLMs, despite their risks, still points toward a more secure and innovative future.
Research
/Security News
Threat actors hijacked Toptal’s GitHub org, publishing npm packages with malicious payloads that steal tokens and attempt to wipe victim systems.