Assessment of your Qlik Sense Environment
In my last blog “Why Move to Qlik SaaS“, I talked about three main things.
Realtime Scalable Data Pipelines
Leverage the scalability of cloud DW for Transformations with ELT
Cloud based ‘pay as you use’ Data Warehouse/lake
Next Gen BI that goes much beyond reports
No Code AI/ML for Business Analysts to leverage data science and gain insights
Scale your Enterprise with error free processes
Create implementable Strategy for your environment to generate value by leveraging data assets
In my last blog “Why Move to Qlik SaaS“, I talked about three main things.
Proactively Manage Qlik Performance and User Experience
Validate Access and manage Compliance for Qlik dashboards
Drive BI Adoption, increase Productivity and enable Proactive action
In my last blog “Why Move to Qlik SaaS“, I talked about three main things. Considerations, Benefits, and Cautions. In this blog, I will help
Challenges: Extracting base reports from ERP was a manual process that involved collating, updating and
Realtime Scalable Data Pipelines
Leverage the scalability of cloud DW for Transformations with ELT
Cloud based ‘pay as you use’ Data Warehouse/lake
Next Gen BI that goes much beyond reports
No Code AI/ML for Business Analysts to leverage data science and gain insights
Scale your Enterprise with error free processes
Create implementable Strategy for your environment to generate value by leveraging data assets
In my last blog “Why Move to Qlik SaaS“, I talked about three main things.
Proactively Manage Qlik Performance and User Experience
Validate Access and manage Compliance for Qlik dashboards
Drive BI Adoption, increase Productivity and enable Proactive action
In my last blog “Why Move to Qlik SaaS“, I talked about three main things. Considerations, Benefits, and Cautions. In this blog, I will help
Challenges: Extracting base reports from ERP was a manual process that involved collating, updating and
Legacy ETL process of taking data from OLTP systems like SAP to a data warehouse has been a mammoth task, both due to the complexity of transformation and the short time window available to make the updated data available before business start.
The ELT (Extract Load Transform) paradigm is changing this. While it involves the same work as legacy ETL, it operates by moving raw data from source system to destination data warehouse first and then leveraging the power and scalability of the Datawarehouse (typically on cloud using MPP architecture and columnar storage) to do the compute intensive transformations in record time. This eliminates the need to have an oversized ETL tool with a staging server used just for few hours. It also makes real time data transfer seamless
As we have increasing real time Semi structured and unstructured data with schema on read use cases, ELT becomes more of a choice rather than ETL which has been primarily for structured data.