Legacy applications that have exceeded their useful life can be expensive to maintain. They often require specialized skills and old versions of software and hardware to support. But, they can also contain very valuable data that needs to be retained for business or compliance purposes.
What if you could preserve all that good data, make it more accessible to the business, and lower the retention cost dramatically, all at the same time? It may be time to retire your old apps to the (smart data) lake.
I have previously written about the Smart Data Lake here:http://linkd.in/1NeWvMY and it offers very compelling benefits for application retirement:
- Preserves institutional knowledge of the meaning of data in legacy applications by mapping the data to a business friendly conceptual model; The model is a high-level domain representation easily understood by business users so it does not require specialized application skills to use going forward.
- Provides easy to use mapping and ETL tools to migrate data from legacy apps to a low-cost, Hadoop (HDFS) storage environment; The domain models drive the data migration so meaning and provenance are preserved.
- Provides business self-service through a browsable and searchable catalog of available data sets
- Delivers on-demand access to high performing, in-memory query, search and analytics capabilities across any legacy data set; In many cases the analytics capabilities far exceed those of the legacy application.
- Enhances the data value by making it easy to combine and analyze with other data sets - you can combine and ask questions of the data that were not possible previously.
The Smart Data Lake is a perfect complement to low-cost, commodity cloud infrastructure for providing a retirement home for your legacy application data.
It simplifies maintenance and support, significantly lowers costs and provides increased access to the data. By making the data more accessible and easy to combine with other data sets, it can also provide an ROI that goes beyond just saving cost.