Database Storage Optimization best practices, tips and tricks and guidance from Database Compression Product Management

Automatic Data Optimization Reduces ILM Development and Administrative Time at Yapı Kredi Bank

Gregg Christman
Product Manager

In earlier blogs I’ve discussed the use of Heat Map and
Automatic Data Optimization (ADO) for both compression tiering (
) and storage tiering (See

this blog we’re fortunate to be able to discuss why Yapı Kredi Bank decided to
look at Automatic Data Optimization to implement an ILM (Information Lifecycle
Management) solution.

Established in 1944 as Turkey’s first
retail focused private bank with a nationwide presence, Yapı Kredi has played a significant role in Turkey’s
development, setting standards in the sector through its innovative approach,
commitment to social responsibility and investment in culture and arts. Yapı
Kredi, the fourth largest private bank in Turkey with TL 248.1 billion of
assets, is one of the 10 most valuable brands in Turkey.

Like many organizations, Yapı Kredi saw
that 90% of the data (approximately 90TB) managed by Oracle Database was data
warehouse data, leaving the remainder of the data, approximately 10TB, being
OLTP data which was being actively modified. Yapı Kredi’s DBA’s recognized that
as their data become less active, moving from highly active OLTP data to less
active, query-mostly data warehouse data it was possible to use different types
of Oracle compression to suit different access patterns.

ILM was identified by their organization
and a key goal, but prior to ADO, when organizations wanted to implement an ILM
strategy they would have typically leveraged the Advanced Compression and Data
Partitioning options to create a manual database compression and storage
tiering solution – a solution which required organizations to have a fairly
deep understanding of their data access and usage patterns, often across
thousands of tables/partitions – or they created a custom solution.

Their DBAs recognized that the data
maintenance tasks associated with a manually implemented ILM solution would
require serious time of DBAs, developers, and even the business users
themselves. Knowing that if ADO worked
as they expected they believed the organization would save development and
administration time, and costs, for the life of the deployment.

But how could their DBA’s identify which
tables/partitions, across the database, are best suited for compression, and
which type of compression? To do so requires that DBA’s have the ability to
easily determine which of their tables/partitions are “hot” (the most active
data) and which have “cooled” (less active historic/archive/reporting data).

Interested in learning more about how ILM allows
Yapı Kredi to better utilize the organizations existing storage, better manage
their storage growth and help with database performance, then please see the
new Yapı Kredi Case Study

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