datasqill /ˈdeɪtə skɪl/ is a server-based data engineering and scheduling solution for massive data processing in context of data warehousing, big data, data science and data integration.
datasqill helps customers to uncover full potential of their data solutions by providing transparent yet very powerful platform to develop, execute and monitor data transformations. datasqill brings it to the next level by modularizing routine operations, providing rich integration capabilities, full SQL support, reliable scheduling and high customizability. datasqill ensures high quality standards without sacrificing agility and development speed.Download Flyer Request Demo
datasqill@Exasol Xperience in Berlin 4-6 June 2019
Join us at this year’s Exasol Xperience, where we will take you beyond the data frontier and cover key topics such as BI, big data, cloud, data science and predictive analytics. Experience the future of data analytics first hand at #XP19 We would be happy to meet you during Deep Dive session on datasqill: No-nonsense ELT for Exasol at 5pm on June 5
Exasol Xperience 2019
datasqill@TDWI Conference 2019 24-26 June in Munich
More than 130 national and international experts present the latest findings and developments in the data community at TDWI München 2019. Listen to exciting presentations on analytics, AI, machine learning, cloud, data science, digitization, IoT and much more. Would be happy to meet you at our booth during TDWI.
TDWI Konference 2019
datasqill@Big Data World in Frankfurt 6-7 November 2018
Meet datasqill at Big Data World 2018 in Frankfurt! Big Data World is an international conference that deals with practical aspects of using data to support business. It is a “How to” – Event focusing on big data, massive data and cloud.
Big Data World 2018
"noETL, yesSQL – why ELT and SQL are the optimal choice for a modern Data Warehouse" in the German Oracle User Group Magazine
The article of datasqill Co-Developer has recently been published in the German Oracle User Group Magazine. The article deals with using ELT (extract, load, transform) approach as opposed to ETL approach in context of data warehousing. Graphical data transformation is also compared to development with SQL. The author provides real world examples and blueprints for ELT-based data transformation architectures.
Download article in German
German Oracle User Group Magazine
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.
datasqill enables use of SQL as data transformation language in all project stages making development process more agile and debugging process more transparent as the code is directly executable. datasqill provides Apache Freemarker Template-based modules for most common database-native transformations (e.g. Insert, Update, Upsert, Merge, Delete) to automate and standardize routine operations.
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. Metadata, dependencies and transformation code are stored in historized Repository of datasqill thus enabling auditability and data lineage.
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 (Apache Freemarker, Java, PL/SQL, Shell-Scripting, Lua, PL/pgSQL) and seamlessly integrated.
Out-of-the-Box Solution for 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.
datasqill allows flexible scheduled execution of transformation batches while considering inter-batch dependencies. Historical execution statistics are used for optimization of the future runs. Monitoring views provide visual insights into running batches and give operators control over executed processes.
datasqill is the ideal tool for implementation, execution and monitoring of ELTs and ETLs for Data Warehousing: staging source systems data, loading 3NF cores, Star-Schemas, Data Vaults and communication with reporting systems. Rich module library, well thought through concepts for security, code deployment and easy operations will ensure enterprise-grade standards without sacrificing agility.
datasqill can orchestrate not only its own transformations but also trigger and control operations performed by other tools (e.g. other ETL Tools, Java Programs or Scripts). In this case customers would benefit from centralized environment that allows building and scheduling batches, managing dependencies (intra- and inter-batch) and monitoring execution, thus making operations much easier.
datasqill is able to control native Hadoop Clients like HDFS or Apache Sqoop as well as to execute transformations via Apache Hive or Spark, thus bringing transformation logic directly into the environment where data is stored which is especially important technique to process large amounts of data.
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. datasqill server runs on Linux and can be run in any cloud.
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 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.
Christof Grill, Head of BICC, M-net Telekommunikations GmbH
datasqill attracted our attention by its concepts of native execution and development with SQL. Using SQL for analysis, implementation and testing allowed a smooth transition between project phase and more agile development. Further features like smart scheduling, standardized modularity and customizability have convinced us even more. datasqill supports short development cycles 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.
datasqill was with by SoftQuadrat GmbH in Germany.
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 interesting for their work.
Tel.: +49 8095 875927