Breadcrumb

This tutorial shows you how to create a Notebook and run it in Oracle Machine Learning. This is the second tutorial in the series Getting Started with Oracle Machine Learning. Read the tutorials in sequence.

1. Creating Projects and Workspaces in Oracle Machine Learning
2. Creating and Running Notebooks in Oracle Machine Learning
3. Collaborating in Oracle Machine Learning
4. Creating SQL Scripts in Oracle Machine Learning
5. Running SQL Statements in Oracle Machine Learning

SQL or Structured Query Language is the language standard for relational database management systems. You can use SQL statements to perform tasks such as retrieving data from a database, update data on a database and so on. Some examples of SQL statements are SELECT, INSERT, UPDATE, DELETE, CREATE, and DROP. In this OBE, we will look at how to create a SQL script.

This is the fourth tutorial in the series Getting Started with Oracle Machine Learning. Read the tutorials in sequence.

1. Creating Projects and Workspaces in Oracle Machine Learning
2. Creating and Running Notebooks in Oracle Machine Learning
3. Collaborating in Oracle Machine Learning
4. Creating SQL Scripts in Oracle Machine Learning
5. Running SQL Statements in Oracle Machine Learning

This tutorial shows you how to run SQL statements in Oracle Machine Learning. This is the fifth and the last tutorial in the series Working with Oracle Machine Learning. Read the tutorials in sequence.

1. Creating Projects and Workspaces in Oracle Machine Learning
2. Creating and Running Notebooks in Oracle Machine Learning
3. Collaborating in Oracle Machine Learning
4. Creating SQL Scripts in Oracle Machine Learning
5. Running SQL Statements in Oracle Machine Learning

This tutorial shows you the steps to create a project and a workspace in Oracle Machine Learning. This is the first tutorial in the series Getting Started with Oracle Machine Learning.

This OBE explains the steps to create your own project, and optionally your workspace. Read the tutorials in sequence.

1. Creating Projects and Workspaces in Oracle Machine Learning
2. Creating and Running Notebooks in Oracle Machine Learning
3. Collaborating in Oracle Machine Learning
4. Creating SQL Scripts in Oracle Machine Learning
5. Running SQL Statements in Oracle Machine Learning

This tutorial shows you how to collaborate and share notebooks with other users in Oracle Machine Learning. This is the third tutorial in the series Getting Started with Oracle Machine Learning. Read the tutorials in sequence.

1. Creating Projects and Workspaces in Oracle Machine Learning
2. Creating and Running Notebooks in Oracle Machine Learning
3. Collaborating in Oracle Machine Learning
4. Creating SQL Scripts in Oracle Machine Learning
5. Running SQL Statements in Oracle Machine Learning

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

This tutorial is the seventh of eight self-study sessions on Oracle R Enterprise. In this tutorial, you learn how to:

This podcast is the first in a series of podcasts that will look at the core concepts behind Oracle’s in-database SQL analytics and examine, in detail, some of the key features and functions.

In this podcast we review the extensions to the SQL GROUP BY clause, which allow developers and business users to add subtotals and grand-totals to their result sets. These can range from simple row/column subtotals to the creation of sophisticated hierarchical, multi-dimensional cubes.

In this podcast we explore some examples of how to use SQL analytics to answer some typical business questions.

In an earlier podcast we introduced the concepts of windows in this podcast we are going to look a little deeper at how analytical windows work.

In this podcast we review some of the optimizations included in the database to support the SQL analytics

In this simple demo we explore how to calculate the average 90 day stock balance using SQL analytics.

In this podcast we continue our review some of the key concepts around the way data sets are processed by SQL analytics