The Post-ETL - Smarter Data Engineering

datasqill /ˈdeɪtə skɪl/ is a lightweight data transformation and data engineering solution for massive data processing in context of data warehousing, data science, data integration and data migration.

datasqill executes transformations directly in the environment where data is stored which leads to higher performance and transparency. datasqill helps customers get the best out of their data storage solutions and brings it to the next level with rich integration capabilities, smart orchestration, reliable scheduling and customizability.

datasqill is an All-in-One solution that allows development, execution, scheduling and monitoring of data transformations, all within one tool, making it a perfect ETL / ELT choice for all data engineering needs.

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Native Execution

Transformations get executed directly in the environment where data is stored, which allows using datasqill for performant and transparent data processing for ELT and ETL Scenarios. Resources of respective databases are optimally used and new features like In-Memory or MPP are seamlessly supported.

SQL Support

SQL is a universal interface language for databases and Hadoop (SQL-on-Hadoop). SQL can be used in all project stages from prototyping to implementation thus making development process more streamlined, agile and debugging process more transparent as the code is directly executable.

Smart Orchestration

datasqill identifies dependencies between transformations based on the metadata and executes them in a correct order without needing to create special job nets thus saving developers effort and minimizing mistakes. Historical execution statistics are used for optimization of the future runs.


datasqill provides a number of modules to streamline transformation routines, amongst them In-Database Transformation, Flat Files Handling, Web Services Communication, Integration with Reporting Tools and External Schedulers. Custom modules can be built using generic or database-native technologies (Java, PL/SQL, Shell-Scripting, Lua, PL/pgSQL) and seamlessly integrated.

Data Historization

Out-of-the-Box Solution for technical and business data historization (Bitemporal Data Historization) for databases allows efficient Data Management for scenarios like Slowly Changing Dimensions (SCD2), Data Staging and maintaining mapping tables in Data Warehousing without writing a single line of code.

Scheduling and Monitoring

datasqill allows flexible scheduled execution of transformation batches while considering inter-batch dependencies. Monitoring views provide visual insights into running batches and give operators control over executed processes.


Transformations (blue) are placed on the Worksheet, source and target objects (grey) are defined for each transformation.

SQL Editor

Transformation logic is programmed in form of SQL Select-Statement. Functional module generates a target SQL at runtime and executes it against the database.


Monitoring view allows supervising and controlling the running transformations.

Batch Editor

Transformations from multiple worksheets can be grouped into batches and scheduled for execution.

  • datasqill attracted our attention by its concepts of In-Database Execution and Development with SQL.

    Using SQL for analysis, implementation and testing allowed a smooth transition between project phases. Execution of the transformations directly in the database ensured performant operations. Further features like smart scheduling, intuitive UI, standardized modularity and customizability have convinced us even more.

    datasqill supports short development cycles and stable operations as opposed to many other ETL Solutions. This all made datasqill one of the pillars of our Data Warehouse. The achievements during implementation of our Data Warehouse were noted by CIO Magazine and Business Application Research Center (BARC) in Germany.

    Christof Grill, Head of BICC
    M-net Telekommunikations GmbH

  • Development of datasqill was sponsored in part by the grant of Innovational Program “Bayern Innovativ“ of the state of Bavaria in Germany. We are glad that concepts and ideas of the solution are convincing and have received that degree of recognition. Implementation was carried out in cooperation with Hochschule Rosenheim and Goethe-University Frankfurt.

    Bayern Innovativ

  • Graphical modeling of data transformation flows combined with Implementation of business logic in SQL make datasqill an easy-to-learn and powerful tool for ETL-Developers. datasqill allows developers to focus on the efficient implementation of business requirements instead of learning proprietary ETL Tools with their specifics and limitations.

    Cezary Drozd, CEO
    blue veery GmbH

Data Warehousing and BI

datasqill is the ideal tool for implementation, execution and monitoring of ELTs and ETLs for Data Warehousing and Business Intelligence, staging source systems data, loading 3NF cores, Star-Schemas, Data Vaults and communication with reporting systems.

Mass Data / Big Data

Data transformations get propagated directly into the environment where data is stored which is especially important technique to process large amounts of data. By doing so, datasqill allows achieving high performance and minimizing network traffic when working with mass data or big data.

Data Integration

Data integration from heterogeneous data sources (Databases, Flat Files, Web Services), parsing different formats (e.g. CSV, XML, JSON), transforming und exporting processed data can be easily automated with datasqill.


datasqill supports communication via web services, allows performing data transformations cross-environment and provides an overview of processes running In-the-Cloud and On-Premises, so that the customers have their transformation landscape under control disregarding of its distribution.

In-Memory / MPP Databases

datasqill runs native transformations in database with SQL or database-native scripting (PL/SQL, Lua), thus eliminating unnecessary layer in form of ETL application server and utilizing natural power of In-Memory or MPP Databases for transformations.

Data Migration

Data Transformation and Data Preparation for migration from legacy into new systems is easily implementable with datasqill. Data can be extracted from the legacy system, cleansed, transformed, mapped and populated directly into the new system or exported as a flat file.

datasqill was with by SoftQuadrat GmbH in Germany
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We would be glad to show you how datasqill can improve your data transformation processes and address your data engineering challenges.

We are also interested in getting in contact with consulting companies that find datasqill attractive for their customers.
Tel.: +49 8095 875927

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