Reduce to the time and costs of data warehouse migrations with granular insight into business activity and data usage of your legacy systems.
Data Warehouse migrations consume months of planning and implementation, often requiring a large number of IT staff with different functional roles such as data architects, application managers and database managers. Most migration projects experience time and cost overruns since the process typically relies on anecdotal evidence of the usage of legacy systems to drive the design and migration plans resulting in inefficiencies.
The challenges for IT during data consolidation and data warehouse migrations are:
- Lack of visibility needed to assess usage and impact from a business perspective to streamline migrations, consolidations and improve IT efficiencies.
- Inefficient design in data architecture and optimization and unnecessarily retaining unused or irrelevant data, reports and applications.
- Unable to uncover and resolve bottlenecks during testing and deployment in a timely manner.
The Appfluent Solution
Appfluent Visibility for BI and Data Warehousing® provides visibility into business
activity and data usage of both your legacy and new systems’ Business Intelligence
and Data Warehouse infrastructure.
With Appfluent you can get granular information on how your applications and data are being used and impacting your legacy systems as well as your new system, enabling you reduce the complexity and costs of migrating to a new data warehouse.
Appfluent supports migrations to and from Oracle/Exadata, Teradata and IBM DB2.
Key Features
- Assess the usage of all user and application activity and identify periodic activity patterns and trends to discover impact on databases.
- Identify infrequently used or unnecessary applications, reports and data models. Assess converting frequent and expensive ad-hoc activity to standardized reports.
- Identify standardized reports or ad-hoc activity frequently run with poor database performance or unnecessarily retrieving excessive number of records. Find the associated details including the users, SQL statements, database performance metrics and data used to guide application remediation.
- Find frequently used data objects most associated with the poorest query performance and identify the sources to guide the tuning and optimization of the most relevant data.
- Discover frequently used complex SQL such as Joins including outer joins and its associated objects to asses SQL re-writes or data architecture improvements.
- Uncover frequent and expensive data conversions within application that are better suited for database operations.
- Identify columns that are candidates for indexing based on their usage in predicates such as Where, Order By, Group By etc. Identify unused columns that are indexed to assess the eliminating unnecessary indexes.
- Benchmark performance of critical ETL data loads based on business priority and SLA’s. Identify poor performing critical data loads and assess for remediation.
- Assess overall data utilization and identify unused or dormant schemas, tables and columns that are unnecessarily loaded, stored and maintained.
- Streamline data loads by eliminated entire Tables or fields that are not used but unnecessarily loaded. Identify candidates for operations better suited for ETL such as aggregations repeated done in the database.
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