Enterprise Data Modernization Drivers

Two of the most dramatic trends in contemporary information technology (IT) architectures are the rise of the cloud for processing and storing data, and the modernization of data management tools to accommodate unstructured data and advanced analytics needs to exploit the possibilities of the new data driven paradigm. These two trends are happening simultaneously. Key drivers for modernization of data are,

1. Advanced Analytics (ML & AI) needs in order to derive more value from data
2. Processing requirements for large data sets
3. Use-cases requiring real-time processing of data
4. Unstructured data storing & processing
5. High cost of existing data-warehouse appliances
6. Products going out of support (Netezza etc.)

The Enterprise Challenge

1. Most of these data-warehouse systems are very old and have been subject to many lift & shift migrations leading to issues like missing SME knowledge & no data lineage.
2. Majority of the queries are not used, redundant, duplicate & not required.
3. There is no merit in simply dumping the data to big-data / cloud with so many inefficiencies and redundancies (remember cloud can be really expensive if not used wisely)
4. Lift & Shift migration to big-data or cloud will be highly ineffective and out of synch very fast as soon as incremental data starts flowing. This will also not fulfil the ultimate data modernization goals of the modern day enterprise.

NucleusTeq: Approach

1. NucleusTeq has a methodical approach towards enterprise data modernization that truly enables the enterprise to leverage the data as a re-usable enterprise asset.
2. Analysis of existing queries on usage, dependency, performance & capacity in order to optimize the return on investment & mitigate dependency on expensive appliances
3. The data ingestion is done from the source instead of data-warehouse in order to minimize dependency on Datawarehouse & create single version of truth
4. Proven methodology for data governance, metadata, lineage, data quality etc for enterprise re-use.
5. Mapping of ‘Point of Departure’ & ‘Point of Arrival’ metadata and migration of historical data
6. Query conversion to spark, python, java, or any native cloud language keeping the new environment in mind
7. Query optimization and refinement
8. Productionvalidation of reports against the POD systems

Key Benefits

1. 5X Faster & 100% accurate conversion. Future ready platform.
2. Single version of truth. Certified Data for enterprise consumption
3. Data-driven-enterprise enabled through modern data-lake
4. No-Redundancies or In-efficiencies
5. 6-10X Faster time to market for new reports.
6. Enabling federation of data through self-service