Data accelerators helps to speed up implementation time across in various phases of data integration,data management & migration engagements,ensuring faster go-to-market for data management solutions.
We believe that data accelerators are differentiators that helps to provide better turn around time when building data integration, management and & migration solutions. That’s why, we keep on creating new accelerators while improving the existing ones.
We have created multiple data accelerators to address the typical challenges associated with data onboarding, data transformation - which helps in accelerating the various tasks of data integration, - data management, code migration, and data quality.
Head of Digital Innovation
Reduce costs due to low maintenance,& reduced development effort. They are also scalable to incorporate new data ingestion requirements or when adding & modifying any data transformation rules.
Facilitates with faster data ingestion development frameworks are able to ingest data from various sources with minimal coding. For migration, assist from legacy code to the latest technologies.
Framework to complete most data management activities,such as building & implementing coding standards to ensure that standardized & consistent coding patterns are followed across multiple projects.
We have built our framework to ingest and transform various types of multi-format data data lakes. This open source- based framework can be deployed across any Hadoop distribution network. This is the metadata driven framework.
We have created a reusable data accelerator framework to reduce the migration effort from existing technology to lates technology. This greatly reduces the effort required when analyzing and rewriting existing code. This framework has the capability of converting most of the code written in SQL Server and, Oracle to Snowflake.
This reusable data model can be used across multiple projects to capture all the audit information required for ETL- and ELT- related implementation. Through the audit log, we can also ensure reusable components and best practice for audit logging and process exception handling.
A reconciliation framework is used to conduct data reconciliation (DR) after the data has been loaded to staging and report differences to ensure that the migration architecture has transferred the data.