The ETL Jumpstart Kits are based largely on Kimball best practice and have been designed to provide a framework for consistent ETL development practices. Available for Microsoft SQL Server 2014 and 2016.
- Highly configurable templates – simplify the process of moving packages between environments or when deploying packages to multiple servers.
- Configurations stored within the database – allowing data to be included in standard back-up and restore procedures.
- Generic set of Variables – dynamic package variables are provided which are applicable to a data warehouse implementation.
- Flexible ETL architecture – allowing flexibility when extracting from source systems that are updated at different times or allow a limited access window to extract data.
- Parent/ child template packages – provides a Parent (or Master) package to arrange child packages into a logical sequence that represents the order in which they need to be processed. Furthermore, when a child package is executed from a parent package the parameters are passed from the parent to the child.
- Error Handling – contains error flows such as single row error redirection.
- Logging and Auditing – captures all events raised.
- Checkpoints – provides guidelines on checkpoints and transaction handling to enable restart and rollback.
- Established naming standards – naming standards have been applied at the package, task and component levels.
- Use of Kimball SCD Component – is proven to offer significant performance improvements compared to the pre-installed Microsoft-equivalent component.
- Templates self-documented – annotations are provided throughout the templates for ease of understanding.
With the release of MS SQL Server 2016, Altis have enhanced our 2016 Jumpstart Kits, to include:
- Azure and AWS cloud solutions – including a Cloud Security Framework.
- Archiving – off-load cold and only occasionally queried data to Azure SQL Stretch DB.
- SQL Server 2016 enabled features (Stretch DB, Polybase).
- Big Data Enabled.
- Bot builders.
- Machine Learning Analytics.
Reduce the cost of the design phase of projects (on average around 60% reduction in project design costs).
- Lower BAU support costs and effort (average of 35% savings on BAU costs across our customers).
- Reduce development errors through a consistent approach.
- Easier to train internal development team.
Contact us to find out more.