Case Study: F500 Pharma company

Replicant is a high-performance, scale out and easy-to-use legacy RDBMS and Big Data replication software, which enables organizations to accelerate and reduce the costs of distributing, sharing and ensuring the availability of data for meeting business operations and business intelligence needs.

Using Replicant, organizations can load data efficiently and quickly to operational data stores/warehouses.

• create copies of production databases to enable operational reporting/analytics.

• offload queries from operational systems to reduce load and impact.

• facilitate zero-downtime migrations and upgrades.

• consolidate or distribute data across data sources/centers.

Instead of relying on a complex, resource-intensive ETL (Extract/Transform/Load) process which moves data in batches, Replicant enables continuous real-time ELT (Extract/ Load/Transform) with a simple yet powerful GUI (Graphic User Interface) expediting the traditionally-slow process of data provisioning. Our case study is from a large pharmaceutical company. Their marketing analytics department had a problem in consolidating data from multiple worldwide locations.

Pain Points in Global Analytics Pipeline

• Data arrives from ‘source-of-truth’ databases to a staging SAS store (in the form of sas7bdat files) daily, weekly, monthly on an IBM AIX host.*

• Manual intervention required to move these files to a cloud environment (AWS/GCP).

• Manual intervention required to convert sas7bdat files to csv files for direct consumption by analytic engines (RedShift/BigQuery).

• Legacy AIX host with limited storage capacity and network bandwidth (conversion of formats not feasible at source).

Replicant as a future proof data pipeline

Imagine ‘Replicant’ as a virtual agent able to continuously ‘detect’ any change in SAS Store and replicate it to any target without manual intervention.

Eventually, Replicant enabled a more complex topology and 10x the data movement throughput while enabling incremental updates.

Benefits

• Enable lower TTA (time to analytics)

• Improve efficiency & productivity for global analytics projects

• Faster turnaround for Advanced analytics projects

• Manage replication in heterogeneous environments (operational databases and big data stores)