With this textbook, Vaisman and Zim nyi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes "Fundamental Concepts" including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details "Implementation and Deployment," which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers "Advanced Topics" such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases.
I S 445 Database Management (4)Examines the business need for database processing. Discusses database design, development, and administration. Students practice real-world database design and implementation using SQL. Discusses issues related to transaction management, data warehouse, etc. Prerequisite: I S 320, which may be taken concurrently; may not be repeated. Offered: AWSp.View course details in MyPlan: I S 445
I S 461 Systems Implementation (4)Develops business information systems integrating knowledge gained in previous 400-level I S courses. Topics include software project management, system/database design, GUI, software testing, systems implementation/support/maintenance, user training, integrating web, and business environments. Prerequisite: I S 445; I S 460; may not be repeated.View course details in MyPlan: I S 461
I S 545 Database Systems and Applications (4)Logical data models, relational database systems, structured query language (SQL), conceptual modeling, database design, Web-connected databases, transaction management, distributed and heterogeneous systems, data warehousing, data mining, database administration issues. Focuses on the use/management of business data in areas such as finance. Prerequisite: B A 502 or I S 504.View course details in MyPlan: I S 545
I S 581 Advanced Research Topics in Information Systems II (4, max. 12)Advanced topics of current interest of faculty in heterogeneous database, temporal database, data warehousing, data uncertainty, active and deductive database systems, database design, and formal database languages. Prerequisite: doctoral student or permission of instructor.View course details in MyPlan: I S 581
4. Physical modeling: For the data warehouses to perform efficiently, physical modeling is needed. This contains designing the physical data warehouse organization, data placement, data partitioning, deciding on access techniques, and indexing.
5. Sources: The information for the data warehouse is likely to come from several data sources. This step contains identifying and connecting the sources using the gateway, ODBC drives, or another wrapper.
6. ETL: The data from the source system will require to go through an ETL phase. The process of designing and implementing the ETL phase may contain defining a suitable ETL tool vendors and purchasing and implementing the tools. This may contains customize the tool to suit the need of the enterprises.
7. Populate the data warehouses: Once the ETL tools have been agreed upon, testing the tools will be needed, perhaps using a staging area. Once everything is working adequately, the ETL tools may be used in populating the warehouses given the schema and view definition.
8. User applications: For the data warehouses to be helpful, there must be end-user applications. This step contains designing and implementing applications required by the end-users.
9. Roll-out the warehouses and applications: Once the data warehouse has been populated and the end-client applications tested, the warehouse system and the operations may be rolled out for the user's community to use.
1. Build incrementally: Data warehouses must be built incrementally. Generally, it is recommended that a data marts may be created with one particular project in mind, and once it is implemented, several other sections of the enterprise may also want to implement similar systems. An enterprise data warehouses can then be implemented in an iterative manner allowing all data marts to extract information from the data warehouse.
2. Need a champion: A data warehouses project must have a champion who is active to carry out considerable researches into expected price and benefit of the project. Data warehousing projects requires inputs from many units in an enterprise and therefore needs to be driven by someone who is needed for interacting with people in the enterprises and can actively persuade colleagues.
3. Senior management support: A data warehouses project must be fully supported by senior management. Given the resource-intensive feature of such project and the time they can take to implement, a warehouse project signal for a sustained commitment from senior management.
5. Corporate strategy: A data warehouse project must be suitable for corporate strategies and business goals. The purpose of the project must be defined before the beginning of the projects.
6. Business plan: The financial costs (hardware, software, and peopleware), expected advantage, and a project plan for a data warehouses project must be clearly outlined and understood by all stakeholders. Without such understanding, rumors about expenditure and benefits can become the only sources of data, subversion the projects.
7. Training: Data warehouses projects must not overlook data warehouses training requirements. For a data warehouses project to be successful, the customers must be trained to use the warehouses and to understand its capabilities.
8. Adaptability: The project should build in flexibility so that changes may be made to the data warehouses if and when required. Like any system, a data warehouse will require to change, as the needs of an enterprise change.
Data warehouse data is stored in a separate storage tier Redshift Managed Storage (RMS). RMS provides the ability to scale your storage to petabytes using Amazon S3 storage. RMS allows to you scale and pay for compute and storage independently, so that you can size your cluster based only on your compute needs. It automatically uses high-performance SSD-based local storage as tier-1 cache. It also takes advantage of optimizations, such as data block temperature, data block age, and workload patterns to deliver high performance while scaling storage automatically to Amazon S3 when needed without requiring any action.
Amazon Redshift is based on PostgreSQL. Amazon Redshift and PostgreSQL have a number of very important differences that you need to take into account as you design and develop your data warehouse applications. For information about how Amazon Redshift SQL differs from PostgreSQL, see Amazon Redshift and PostgreSQL.
A data warehouse provides for the integration, structuring and storing of business data for analytical querying and reporting. We, at ScienceSoft, consider data warehouse design the first step in implementing a data warehouse solution, as at this stage we focus on creating the architecture of a data warehouse system.
Note: The timeframes below are highly approximate, as, for example, the architecture design project for an enterprise-level data warehouse may last up to 3-6 months and even more because of the project scale and specificity.
During the discovery step, our consultants analyze relevant documentation, interview and hold brainstorming sessions with all stakeholders to collect their needs, goals, and vision of the successful data warehousing project implementation. It helps understand their priorities, plan the development process accordingly and as a result - provide a satisfactory end product.
In our projects, we set up close cooperation of business users with a BA and a solution architect while defining the core and advanced functionality of the future solution to avoid overcomplicating the data warehouse architecture.
Our practice has shown that effective data warehouse design project planning can help reduce project time and budget by up to 30%. To achieve that, we carefully elaborate on the findings of the preceding stages.
Note: The next steps would be data warehouse development and launch, which are not addressed within the framework of this guide. In case you are interested in the end-to-end data warehouse implementation process, explore our structured overview of the data warehouse implementation process.
The company owns the data warehouse design project management while relying on outsourced resources to perform data warehouse platform selection, data warehouse solution architecture design and data modeling, etc.
System Center Operations Manager requires access to an instance of a server running Microsoft SQL Server to support the operational, data warehouse, and ACS audit database. The operational and data warehouse databases are required and created when you deploy the first management server in your management group, while the ACS database is created when you deploy an ACS collector in your management group.
If you're designing a distributed deployment that will require SQL Always On Availability Groups to provide failover functionality for the Operations Manager databases, there are additional firewall configuration settings that need to be included in your firewall security strategy.
System Center - Operations Manager inserts data into the Reporting data warehouse in near-real time, it's important to have sufficient capacity on this server that supports writing all of the data that is being collected to the Reporting data warehouse. As with the Operations Manager database, the most critical resource on the Reporting data warehouse is the storage I/O subsystem. On most systems, loads on the Reporting data warehouse are similar to the Operations Manager database, but they can vary. In addition, the workload put on the Reporting data warehouse by reporting is different than the load put on the Operations Manager database by Operations console usage. 781b155fdc