Challenges for the Classic Data Warehouse
• Integrating big data with existing data and making it available for reporting and analytics
• Supporting self-service BI and self-service data preparation
• Faster time-to-market for reports
• Polyglot persistency – processing data stored in Hadoop and NoSQL systems
• Operational Business Intelligence, or analyzing of 100% up-to-date data
The Logical Data Warehouse
• The essence : decoupling of reporting and data sources
• From batch-integration to on-demand integration of data
• The impact on flexibility and productivity – an improved time-to-market for reports
• Examples of organizations operating a logical data warehouse
• Can a logical data warehouse really work without a physical data warehouse?
Implementing a Logical Data Warehouse with Data Virtualization Servers
• Why data virtualization?
• Market overview: AtScale, Cirro Data Hub, Cisco Information Server, Data Virtuality UltraWrap, Denodo Platform, RedHat JBoss Data Virtualization, Rocket DV, and Stone Bond Enterprise Enabler
• Importing non-relational data, such as XML and JSON documents, web services, NoSQL, and Hadoop data
• The importance of an integrated business glossary and centralization of metadata specifications
Improving the Query Performance of Data Virtualization Servers
• How does caching really work
• Which virtual tables should be cached?
• Query optimization techniques and the explain feature
• Smart drivers/connectors can help improve query performance
• How can SQL-on-Hadoop engines speed up query performance?
• Working with multiple data virtualization servers in a distributed environment to minimize network traffic
Migrating to a Logical Data Warehouse
• An A to Z roadmap
• Guidelines for the development of a logical data warehouse
• Three different methods for modelling: outside-in, inside-out, and middle-out
• The value of a canonical data model
• Considerations for security aspects
• Step by step dismantling of the existing architecture
• The focus on sharing of metadata specifications for integration, transformation, and cleansing
Self-Service BI and the Logical Data Warehouse
• Why self-service BI can lead to “report chaos”
• Centralizing and reusing metadata specifications with a logical data warehouse
• Upgrading self-service BI into managed self-service BI
• Implementing Gartner’s BI-modal environment
Big Data and the Logical Data Warehouse
• New data storage technologies for big data, including Hadoop, MongoDB, Cassandra
• The appearance of the polyglot persistent environment; or each application its own optimal database technology
• Design rules to integrate big data and the data warehouse seamlessly
• Big data is too “big” to copy
• Offloading cold data with a logical data warehouse
Physical Data Lakes or Virtual Data Lakes?
• What is a Data Lake?
• Is developing a physical Data Lake realistic when working with Big Data?
• Developing a virtual Data Lake with data virtualization servers
• Can the logical Data Warehouse and the virtual Data Lake be combined?
Implementing Operational BI with a Logical Data Warehouse
• Examples of operational reporting and operational analytics
• Extending a logical data warehouse with operational data for real-time analytics
• “Streaming” data in a logical data warehouse
• The coupling of data replication and data virtualization
Making Data Vault more Flexibile with a Logical Data Warehouse
• What exactly is Data Vault?
• Using a Logical Data Warehouse to make data in a Data Vault available for reporting and analytics
• The structured SuperNova design technique to develop virtual data marts
• SuperNova turns a Data Vault in a flexible database
The Logical Data Warehouse and the Environment
• Design principles to define data quality rules in a logical data warehouse
• How data preparation can be integrated with a logical data warehouse
• Shifting of tasks in the BICC
• Which new development and design skills are important?
• The impact on the entire design and development process