The large data volumes resulting from proliferating web site, email server, call detail record, application logs, RFID, NFC, GPS, etc. are be valuable in understanding business processes and customer behavior, but is usually not presented in a format suitable for business analysis. For example, web logs contain IP addresses, but lack the information to analyze traffic based on demographics such as city or country.
Extract – Load – Transform
SAND’s unique column database architecture and compression technology efficiently restructures (denormalizes) and stores big data making it easily available for analytics, and augmenting it with data for advanced analytics.
SAND ELT delivers:
- Very small data footprint
- Self-defining compressed files
- Zero external catalog structures
- Historical data is enriched with demographic data
- Adapts to deal with slowly changing dimensions
Data is fully denormalized with no inflation of disk space requirements, enabling a huge reduction in TCO and delivering data agility.
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For a large US advertiser, the ability to analyze data about internet users’ response to advertisements is crucial to business success. SAND’s ELT process has drastically reduced the company’s storage requirements for their analytical data warehouse, as well as the time and hardware required for necessary data preparation tasks. It is now possible for the company to analyze online advertising campaigns in their entirety and get the accurate, comprehensive intelligence they need to guide future campaigns.