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Deploying Data Warehouses
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Given new classes of business problems that can be solved using this platform, data warehousing workloads will continue to grow more complex Queries and analyses will be performed against ever-growing data volumes Data loads will move toward near real-time in response to the need for faster tactical business decisions based on current information As these loads become trickle feeds, the platform must not only perform traditional data warehousing workloads efficiently, but also exhibit excellence in performance and loading characteristics that can appear more akin to transaction processing workloads The Oracle Exadata Database Machine is designed to handle workloads that cross this entire spectrum of workload demands Since the platform is balanced out-of-the box and incrementally grows in a balanced fashion, the CPUs, memory, flash, storage, and interconnect scale as workload demands grow, enabling consistent query response times to be delivered Automatic Storage Management (ASM) provides a critical role in distributing data in a striped fashion across the disk The Oracle Exadata Storage Server Software further speeds obtaining results to queries by processing more of the data in storage, where the data lives As you plan your data warehouse deployment, traditional Oracle and data warehousing best practices do apply to the Oracle Exadata Database Machine But you will also find this platform to be a lot more forgiving and easier to manage than traditional Oracle database platforms Of course, the business justification exercise will likely be similar to other data warehousing initiatives, but with new potential areas to explore due to new levels of performance delivered The end result for you should be faster deployment of a platform that delivers the query response and analytics needed by your business analysts and delivers measurable business value
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Exadata and OLTP
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Achieving Extreme Performance with Oracle Exadata
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hen the first Oracle Exadata Database Machine, the HP Oracle Database Machine, was introduced, it represented the first version of the power of the Database Machine This version was aimed at delivering extreme performance for data warehousing, performance many times faster than was previously achievable with standard database systems Version 2 of the Database Machine, the Sun Oracle Database Machine, was released about a year later, with additional capabilities This version of the Database Machine not only further improved performance for data warehouse workloads, but also provided a solution that delivered extraordinary performance for OLTP workloads The X2 versions of the Oracle Exadata Database Machine provided even more flexibility in deployment choices for OLTP workloads This chapter will explore the use of the Database Machine for OLTP workloads As with data warehouse workloads, the power of the Oracle Exadata Database Machine stems from a combination of state-of-the-art hardware and software, which includes features that are standard in the Oracle Database 11g environment and features that are exclusive to the Exadata platform
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OLTP Workloads and Exadata Features
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Before jumping into an examination of the features and functionality of the Exadata Database Machine as it relates to OLTP, we should start by reviewing some of the key aspects of both OLTP and Exadata software The first thing to discuss is the nature of OLTP workloads themselves OLTP stands for Online Transaction Processing, a description that refers to the core nature of OLTP systems OLTP systems are used to handle the tactical operations of an organization, that is transactions where data is written to and updated in the database on an ongoing basis Although data is also written to data warehouses, that data input is traditionally in the form of some type of batch activity, while the write activity in an OLTP system comes in a large number of smaller transactions Because of this, OLTP workloads typically have a higher number of database operations with a smaller data payload, and the performance emphasis is on the responsiveness of the database in handling these smaller transactions
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