Watch this playlist on Oracle Big Data SQL. Learn about monitoring and tuning, Enterprise Manager, Cloudera Manager, and analyzing statistics.

Oracle R Enterprise (ORE), a component of the Oracle Advanced Analytics Option, makes the open source R statistical programming language and environment ready for the enterprise and big data.

This series teaches you how to use Oracle R Enterprise, version 1.4.

Although you may view these tutorials in any order, this series is designed in a linear fashion, with each tutorial building upon the previous ones. Therefore, if you are new to the R language, or to Big Data, it is recommended that you view these tutorials in order.

Learn to manage Big Data and understand the fundamentals of data science in this series.  

The videos in this series provide concise explanations of how to use Oracle products, open source tools, and data science to quickly find value in Big Data.  

(No previous background in statistics is necessary.)

This set of videos covers the Oracle MoviePlex - a big data end-to-end demonstration. It  highlights many of the key components of the Oracle big data platform.  There are 6 recorded  videos - each under 10 minutes.

Watch these demonstrations to get started with the Oracle NoSQL Database.

In these self-paced tutorials, you learn about few of the new features in the latest release of Oracle NoSQL Database. In these short tutorials, you are provided with an overview of the features and also a few demos on how these features are implement in an application.

Watch this set of videos to learn about Oracle SQL Analytics!

The tutorials in this series teach you how to use Oracle Big Data Spatial and Graph components, including Property Graph, Spatial Vector analysis, and Spatial Raster analysis.

The tutorials in this series teach you how to perform data mining with Oracle Database 12c, by using Oracle Data Miner 17.2. Oracle Data Miner 17.2 is included as an extension of Oracle SQL Developer, version 17.2.

Oracle Database 12c: Oracle Data Mining New Features

In this self-study, you learn how to use the Oracle Data Integrator Application Adapter for Hadoop. Topics include an overview of big data and the ODI application adapter. You learn how to load from a file into hive, transform data on hive, and load hive data into Oracle.

In this demo, you are shown the  role of the ODI Application Adapter in Big Data.

This session covers how to load data quickly into an Oracle database using clients implemented in languages such as Java, C++, Ruby, Python and PHP. It focuses on scenarios where data arrives continuously and must be made visible and queryable as soon as possible. 

The new groundbreaking SQL pattern matching capabilities brings the simplicity and efficiency of the most common data analysis language to the process of identifying patterns within a data set. It provides a completely new native SQL syntax that has adopted the regular expression capabilities of Perl by implementing a core set of rules to define patterns in sequences (streams of rows). In this first session will explore some of the main concepts and key words of the new syntax. Subsequent sessions will explore typical use cases for SQL pattern matching and how to debug your pattern matching process.

Watch how the Failover feature works in the Oracle NoSQL Database.  The Failover feature is used to fix a loss of quorum situation which can occur due to zone failure or in case of planned maintenances.

This series features an OBE and a recorded webcast. Learn how to use  SQL for Pattern Matching. Row pattern matching in native SQL improves application and development productivity and query efficiency for row-sequence analysis.

Recognizing patterns in a sequence of rows has been a capability that was widely desired, but not possible with SQL until now. There were many workarounds, but these were difficult to write, hard to understand, and inefficient to execute. With Oracle Database 12c Release 1 (12.1), you can use the MATCH_RECOGNIZE clause to perform pattern matching in SQL to do the following:

1. Logically partition and order the data that is used in the MATCH_RECOGNIZE clause with its PARTITION BY and ORDER BY clauses.
2. Define patterns of rows to seek using the PATTERN clause of the MATCH_RECOGNIZE clause. These patterns use regular expressions syntax, a powerful and expressive feature, applied to the pattern variables you define.
3. Specify the logical conditions required to map a row to a row pattern variable in the DEFINE clause.
4. Define output measures, which are expressions usable in the MEASURES clause of the SQL query.
5. Control the output (summary vs. detailed) from the pattern matching process

Recognizing patterns in a sequence of rows has been a capability that was widely desired, but not possible with SQL until now. There were many workarounds, but these were difficult to write, hard to understand, and inefficient to execute. With Oracle Database 12c Release 1 (12.1), you can use the MATCH_RECOGNIZE clause to perform pattern matching in SQL to do the following:

1. Logically partition and order the data that is used in the MATCH_RECOGNIZE clause with its PARTITION BY and ORDER BY clauses.
2. Define patterns of rows to seek using the PATTERN clause of the MATCH_RECOGNIZE clause. These patterns use regular expressions syntax, a powerful and expressive feature, applied to the pattern variables you define.
3. Specify the logical conditions required to map a row to a row pattern variable in the DEFINE clause.
4. Define output measures, which are expressions usable in the MEASURES clause of the SQL query.
5. Control the output (summary vs. detailed) from the pattern matching process

Perform the steps required to configure and use Oracle NoSQL Database with Oracle SQL Developer.