22 August 2017

Oracle Database 12c Release 2

Oracle Database 12c Release 2 (12.2), is now available everywhere 

Ask Tom Answer Team (Connor McDonald and Chris Saxon) on Oracle Database 12c Release 2 New Features

Oracle Database 12.2c Architecture Diagram

The latest generation of the world's most popular database, Oracle Database 12c Release 2 (12.2), is now available everywhere - in the Cloud, with Oracle Cloud at Customer, and on-premises.  This latest release provides organizations of all sizes with access to the world’s fastest, most scalable and reliable database technology in a cost-effective, hybrid Cloud environment. 12.2 also includes a series of innovations that helps customers easily transform to the Cloud while preserving their investments in Oracle Database technologies, skills and resources.

Oracle RAC 12c Release 2 New Features

Database Security - Comprehensive Defense in Depth

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Oracle Database 12c provides multi-layered security including controls to evaluate risks, prevent unauthorized data disclosure, detect and report on database activities and enforce data access controls in the database with data-driven security. Oracle Database 12c Release 2 (12.2), now available in the Cloud and on-premises, introduces new capabilities such as on-line and off-line tablespace encryption and database privilege analysis. Combined with Oracle Key Vault and Oracle Audit Vault and Database Firewall, Oracle Database 12c provides unprecedented defense-in-depth capabilities to help organizations address existing and emerging security and compliance requirements.

Partner Webcast – Enabling Oracle Database High Availability and Disaster Recovery with Oracle Cloud

Database Cloud Services

Oracle Cloud provides several Oracle Cloud Service deployment choices. These choices allow you to start at the cost and capability level suitable to your use case and then gives you the flexibility to adapt as your requirements change over time. Choices include: single schemas, dedicated pluggable databases, virtualized databases, bare metal databases and databases running on world class engineered infrastructure.

The Oracle Exadata Cloud Service offers the largest most business-critical database workloads a place to run in Oracle Cloud. With all the infrastructure components including hardware, networking, storage, database and virtualization in place, access to secured, highly available and powerful performance is easily provisioned in a few clicks. Exadata Cloud Service is engineered to support OLTP, Data Warehouse / Real-Time Analytic and Mixed database workloads at any scale. With this service, you maintain control of your database while Oracle manages the hardware, storage and networking infrastructure letting you focus on growing your business.


Oracle Database Exadata Cloud Machine delivers the world’s most advanced database cloud to customers who require their databases to be located on-premises. Exadata Cloud Machine uniquely combines the world’s #1 database technology and Exadata, the most powerful database platform, with the simplicity, agility and elasticity of a cloud-based deployment. It is identical to Oracle’s Exadata public cloud service, but located in customers’ own data centers and managed by Oracle Cloud Experts. Every Oracle Database and Exadata feature and option is included with the Exadata Cloud Machine subscription, ensuring highest performance, best availability, most effective security and simplest management. Databases deployed on Exadata Cloud Machine are 100% compatible with existing on-premises databases, or databases that are deployed in Oracle’s public cloud. Exadata Cloud Machine is ideal for customers desiring cloud benefits but who cannot move their databases to the public cloud due to sovereignty laws, industry regulations, corporate policies, or organizations that find it impractical to move databases away from other tightly coupled on-premises IT infrastructure.

Oracle Database 12c Release 2 Sharded Database Overview and Install (Part 1)

Oracle Sharding Part 2

Oracle Sharding Part 3

Oracle Sharding with Suresh Gandhi

Overview of Oracle‘s Big Data Management System

As today's enterprises embrace big data, their information architectures are evolving. The new information architecture in the big data era embraces emerging technologies such as Hadoop, but at the same time leverages the core strengths of previous data warehouse architectures.

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The data warehouse, built upon Oracle Database 12c Release 2 and Exadata, will continue to be the primary analytic database for storing core transactional data: financial records, customer data, point- of-sale data and so forth (see Key Data Warehousing and Big Data Capabilities for more information).

However, the data warehouse will be augmented by a big-data system (built upon Oracle Big Data Appliance), which functions as a ‘data reservoir’. This will be the repository for the new sources of large volumes of data: machine-generated log files, social-media data, and videos and images -- as well as a repository for more granular transactional data or older transactional data which is not stored in the data warehouse.

Data flows between the big data system and the data warehouse to create a unified foundation: the Oracle Big Data Management System.

The transition from the Enterprise Data Warehouse centric architecture to the Big Data Management System - both on-premise, on the Cloud, or in hybrid Cloud systems - is going to revolutionize any companies information management architecture. Oracle's Statement of Direction outlines Oracle's vision for delivering innovative new technologies for building the information architecture of tomorrow.

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Big data is in many ways an evolution of data warehousing. To be sure, there are new technologies used for big data, such as Hadoop and NoSQL databases. And the business benefits of big data are potentially revolutionary. However, at its essence, big data requires an architecture that acquires data from multiple data sources, organizes and stores that data in a suitable format for analysis, enables users to efficiently analyze the data and ultimately helps to drive business decisions. These are the exact same principles that IT organizations have been following for data warehouses for years.

The new information architecture that enterprises will pursue in the big data era is an extension of their previous data warehouse architectures. The data warehouse, built upon a relational database, will continue to be the primary analytic database for storing much of a company’s core transactional data, such as financial records, customer data, and sales transactions. The data warehouse will be augmented by a big-data system, which functions as a ‘data lake’. This will be the repository for the new sources of large volumes of data: machine-generated log files, social- media data, and videos and images -- as well as the repository for more granular transactional data or older transactional data which is not stored in the data warehouse. Even though the new information architecture consists of multiple physical data stores (relational, Hadoop, and NoSQL), the logical architecture is a single integrated data platform, spanning the relational data warehouse and the Hadoop-based data lake.

Technologies such as Oracle Big Data SQL make this distributed architecture a reality; Big Data SQL1 provides data virtualization capabilities, so that SQL can be used to access any data, whether in relational databases or Hadoop or NoSQL. This virtualized SQL layer also enables many other languages and environments, built on top of SQL, to seamlessly access data across the entire big data platform.

Oracle Database 12c Release 2 and Oracle Exadata: A Data Warehouse as a Foundation for Big Data

Even as new big data architectures emerge and mature, business users will continue to analyze data by directly leveraging and accessing data warehouses. The rest of this paper describes how Oracle Database 12c Release 2 provides a comprehensive platform for data warehousing that combines industry-leading scalability and performance, deeply-integrated analytics, and advanced workload management – all in a single platform running on an optimized hardware configuration.

Hot cloning and refreshing PDBs in Oracle 12cR2


The bedrock of a solid data warehouse solution is a scalable, high-performance hardware infrastructure. One of the long-standing challenges for data warehouses has been to deliver the IO bandwidth necessary for large-scale queries, especially as data volumes and user workloads have continued to increase. While the Oracle Exadata Database Machine is designed to provide the optimal database environment for every enterprise database, the Exadata architecture also provides a uniquely optimized storage solution for data warehousing that delivers order-of- magnitude performance gains for large-scale data warehouse queries and very efficient data storage via compression for large data volumes. A few of the key features of Exadata that are particularly valuable to data warehousing are:

  • » Exadata Smarts Scans. With traditional storage, all database intelligence resides on the database servers. However, Exadata has database intelligence built into the storage servers. This allows database operations, and specifically SQL processing, to leverage the CPU’s in both the storage servers and database servers to vastly improve performance. The key feature is “Smart Scans”, the technology of offloading some of the data-intensive SQL processing into the Exadata Storage Server: specifically, row-filtering (the evaluation of where-clause predicates) and column-filtering (the evaluation of the select-list) are executed on Exadata storage server, and a much smaller set of filtered data is returned to the database servers. “Smart scans” can improve the query performance of large queries by an order of magnitude, and in conjunction with the vastly superior IO bandwidth of Exadata’s architecture delivers industry-leading performance for large-scale queries.
  • » Exadata Storage Indexes. Completely automatic and transparent, Exadata Storage Indexes maintain each column’s minimum and maximum values of tables residing in the storage server. With this information, Exadata can easily filter out unnecessary data to accelerate query performance.
  • » Hybrid Columnar Compression. Data can be compressed within the Exadata Storage Server into a highly efficient columnar format that provides up to a 10 times compression ratio, without any loss of query performance. And, for pure historical data, a new archival level of hybrid columnar compression can be used that provides up to 40 times compression ratios.

Partner Webcast - Oracle Cloud Machine Technical Overview (Part1)

Partner Webcast - Oracle Cloud Machine Technical Overview (Part 2)

Oracle Database In-Memory

While Exadata tackles one major requirement for high-performance data warehousing (high-bandwidth IO), Oracle Database In-Memory tackles another requirement: interactive, real-time queries. Reading data from memory can be orders of magnitude faster than reading from disk, but that is only part of the performance benefits of In-Memory: Oracle additionally increases in-memory query performance through innovative memory-optimized performance techniques such as vector processing and an optimized in-memory aggregation algorithm. Key features include:

  • » In-memory (IM) Column Store. Data is stored in a compressed columnar format when using Oracle Database In-Memory. A columnar format is ideal for analytics, as it allows for faster data retrieval when only a few columns are selected from a table(s). Columnar data is very amenable to efficient compression; in-memory data is typically compressed 2-20x, which enables larger volumes of raw data to be stored in the in-memory column store.
  • » SIMD Vector Processing. When scanning data stored in the IM column store, Database In-Memory uses SIMD vector processing (Single Instruction processing Multiple Data values). Instead of evaluating each entry in the column one at a time, SIMD vector processing allows a set of column values to be evaluated together in a single CPU instruction. In this way, SIMD vector processing enables the Oracle Database In-Memory to scan and filter billion of rows per second.
  • » In-Memory Aggregation. Analytic queries require more than just simple filters and joins. They require complex aggregations and summaries. Oracle Database In-Memory provides an aggregation algorithm specifically optimized for the join-and-aggregate operations found in typical star queries. This algorithm allows dimension tables to be joined to the fact table, and the resulting data set aggregated, all in a single in-memory pass of the fact table.

Oracle Database In-Memory is useful for every data-warehousing environment. Oracle Database In-Memory is entirely transparent to applications and tools, so that it is simple to implement. Unlike a pure in-memory database, not all of the objects in an Oracle database need to be populated in the IM column store. The IM column store should be populated with the most performance-critical data, while less performance-critical data can reside on lower cost flash or disk. Thus, even the largest data warehouse can see considerable performance benefits from In- Memory.

Query Performance

Oracle provides performance optimizations for every type of data warehouse environment. Data warehouse workloads are often complex, with different users running vastly different operations, with similarly different expectations and requirements for query performance. Exadata and In-Memory address many performance challenges, but many other fundamental performance capabilities are necessary for enterprise-wide data warehouse performance.
Oracle meets the demands of data warehouse performance by providing a broad set of optimization techniques for every type of query and workload:

  • » Advanced indexing and aggregation techniques for sub-second response times for reporting and dashboard queries. Oracle’s bitmap and b-tree indexes and materialized views provide the developer and DBA’s with tools to make pre-defined reports and dashboards execute with fast performance and minimal resource requirements.
  • » Star query optimizations for dimensional queries. Most business intelligence tools have been optimized for star- schema data models. The Oracle Database is highly optimized for these environments; Oracle Database In- Memory provides fast star-query performance leverage its in-memory aggregation capabilities. For other database environments, Oracle’s “star transformation” leverages bitmap indexes on the fact table to efficiently join multiple dimension tables in a single processing step. Meanwhile, Oracle OLAP is a complete multidimensional analytic engine embedded in the Oracle Database, storing data within multidimensional cubes inside the database accessible via SQL. The OLAP environment provides very fast access to aggregate data in a dimensional environment, in addition to sophisticated calculation capabilities (the latter is discussed in a subsequent section of this paper).
  • » Scalable parallelized processing. Parallel execution is one of the fundamental database technologies that enable users to query any amount of data volumes. It is the ability to apply multiple CPU and IO resources to the execution of a single database operation. Oracle’s parallel architecture allows any query to be parallelized, and Oracle dynamically chooses the optimal degree of parallelism for every query based on the characteristics of the query, the current workload on the system and the priority of requesting user.
  • » Partition pruning and partition-wise joins. Partition pruning is perhaps one of the simplest query-optimization techniques, but also one of the most beneficial. Partition pruning enables a query to only access the necessary partitions, rather than accessing an entire table – frequently, partition-pruning alone can speed up a query by two orders of magnitude. Partition-wise joins provide similar performance benefits when joining tables that are partitioned by the same key. Together these partitioning optimizations are fundamental for accelerating performance for queries on very large database objects.

Oracle Database 12c Release 2 Rapid Home Provisioning and Maintenance

The query performance techniques described here operate in a concerted fashion, and provide multiplicative performance gains. For example, a single query may be improved by 10x performance via partition-pruning, by 5x via parallelism, by 20x via star query optimization, and by 10x via Exadata smart scans – a net improvement of 10,000x compared to a naïve SQL engine.
Orchestrating the query capabilities of the Oracle database are several foundational technologies. Every query running in a data warehouse benefit from:

  • » A query optimizer that determines the best strategy for executing each query, from among all of the available execution techniques available to Oracle. Oracle’s query optimizer provides advanced query-transformation capabilities, and, in Oracle Database 12c, the query optimizer adds Adaptive Query Optimization, which enables the optimizer to make run-time adjustments to execution plans.
  • » A sophisticated resource manager for ensuring performance even in databases with complex, heterogeneous workloads. The Database Resource Manager allows end-users to be grouped into ‘resource consumer groups’, and for each group, the database administrator can set policies to govern the amount of CPU and IO resources that can be utilized, as well as specify policies for proactive query governing, and for query queuing. With the Database Resource Manager, Oracle provides the capabilities to ensure that data warehouse can address the requirements of multiple concurrent workloads, so that a single data warehouse platform can, for example, simultaneously service hundreds on online business analysts doing ad hoc analysis in a business intelligence tool, thousands of business users viewing a dashboard, and dozens of data scientists doing deep data exploration.
  • » Management Packs to automate the ongoing performance tuning of a data warehouse. Based upon the ongoing performance and query workload, management packs provide recommendations for all aspects of performance, including indexes and partitioning.

More Information:













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