Wednesday Aug 31, 2016

Big Data SQL Quick Start. Schema on Read and Schema on Write - Part11.

[Read More]

Sunday Aug 07, 2016

Big Data SQL Quick Start. Storage Indexes - Part10.

[Read More]

Wednesday Jun 01, 2016

Big Data SQL Quick Start. NoSQL databases - Part9.

[Read More]

Thursday May 19, 2016

Big Data SQL Quick Start. Data types - Part8.

[Read More]

Friday May 13, 2016

New pattern matching tutorial on LiveSQL

LiveSQL home page on

If you always wanted to try our new SQL pattern matching feature, MATCH_RECOGNIZE, but never had access to a Database 12c instance then you really need to checkout the our great new LiveSQL playground environment. LiveSQL is a great place to learn about all the new features of Database 12c along with all the existing features from earlier releases.

The new tutorial is called “Log file sessionization analysis with MATCH_RECOGNIZE” and you can view by clicking here.  The objective is to introduce you to some of the important keywords and concepts that are part of the MATCH_RECOGNIZE clause.

[Read More]

Monday May 02, 2016

Big Data SQL Quick Start. Partition Pruning - Part7.

[Read More]

Friday Apr 29, 2016

Big Data SQL Quick Start. Predicate Push Down - Part6.

[Read More]

Monday Apr 25, 2016

SQL Pattern Matching Deep Dive - Part 3, greedy vs. reluctant quantifiers

Welcome to the third post in this deep-dive series on SQL pattern matching using the MATCH_RECOGNIZE feature that is part of Database 12c. 

In the first part of this series we looked at a wide range of topics including ensuring query consistency, how to correctly use predicates and how to manage sorting. In the second part we looked at using the built-in measures to understand how a data set is matched to a pattern. 

In this post I am going to review the concepts of greedy and reluctant quantifiers. I will breakdown this down into a number of areas: 1) Overview of regular expressions, 2) understanding quantifiers, and 3) greedy vs. reluctant quantifiers. The examples in this post use the built-in measures to help show the difference between greedy and reluctant matching. If you are not familiar with the MATCH_NUMBER() function or the CLASSIFIER() function then please take some time to read the second post in this series

Overview of regular expressions

[Read More]

Monday Apr 18, 2016

Big Data SQL Quick Start. Joins. Bloom Filter and other features - Part5.

Using bloom filter for join operations: 

[Read More]

Friday Apr 15, 2016

Is an approximate answer just plain wrong?

We are starting to see a significant change in the way we analyze data as a result of the growth of interest in big data and the newer concept of Internet of Things. Ever since databases were first created everyone has been obsessed, quite rightly so, with ensuring queries returned the correct answer - i.e. precise, accurate answers. This key requirement is derived from the need to run operational, transactional applications. If we check our bank balance online we want the figure we see to be accurate right down to the last cent and for a good reason. Yet increasingly both as part of our online as well as offline experiences we deal with numbers that are not 100% accurate and somehow we manage to make good use of these approximate answers. Here are a couple of examples of where we already using approximations: route planning on our smartphones and crowd counting information in newspapers...

[Read More]

Thursday Apr 14, 2016

Big Data SQL Quick Start. Security - Part4.

Big Data SQL is the way to acsess data that stored in HDFS through Oracle RDBMS, using Oracle external table mechanism. In context of security "table" is key word, which means that you may apply standard security approches to those tables. Today I want to give you couple examples with:

- Oracle Virtual Private databases


- Oracle Data Redaction features. 

Oracle Data Redaction

I don't want to replace documentation of the Oracle Data Redaction within this blog, all available information you could find there, but just let me remind you the main idea of this feature. Very often databases contain sensitive data, like credit card number, SSN or other personal information. It could be useful to have this data in unchanged format for resolve different issue with billing department, but at the same time another departments (like call center) may need only part of this information (like 4 last digits of the credit cards) and for security complience you are not able to show them original data.

[Read More]

Wednesday Apr 13, 2016

Oracle OpenWorld 2016 call for papers is OPEN!

Yes it's that time of year again! If you have a story to tell about data warehousing, big data and SQL analytics then we want to hear from you because the OpenWorld 2016 call for presentations is now open. Mark your calendars: this year Oracle OpenWorld conference will be held in September on 18 - 22, 2016 at the Moscone Center in San Francisco.

We are looking for proposals that describe insights and improvements that attendees can put to use in their own jobs: exciting innovations, strategies to modernize their business, different or easier ways to implement key features, unique use cases, lessons learned, the best of best practices...

[Read More]

Tuesday Apr 12, 2016

SQL Pattern Matching Deep Dive - Part 2, using MATCH_NUMBER() and CLASSIFIER()

Welcome to the second post in this deep dive series on SQL pattern matching using the new MATCH_RECOGNIZE feature that is part of Database 12c. In the first part of this series we looked at the areas of ensuring query consistency, how to correctly use predicates and how to manage sorting.

In this post I am going to review the two built-in measures that we have provided to help you understand how your data set is mapped to the pattern that you have defined. This post will breakdown into three areas: 1) a review of the built-in measures, 2) understanding how to control the output (number of rows returned) and lastly I will bring these two topics together with some examples...

[Read More]

Tuesday Mar 22, 2016

Data loading into HDFS - Part2. Data movement from the Oracle Database to the HDFS

[Read More]

Monday Mar 21, 2016

SQL Pattern Matching deep dive - Part 1

There has been quite a lot of interest in the new 12c MATCH_RECOGNIZE feature for SQL pattern matching. Therefore, I thought now would be the perfect time to start a series of quick deep dive posts that explain how SQL pattern matching works. Over the coming weeks I will cover the following topics in a series of posts.

This is the start of a series of posts based on a presentation that I put together for the recent annual BIWA conference at Oracle HQ. The Oracle BI, DW and Analytics user community always puts on a great conference and this year was the best yet. You can download any or all of the presentations from this year’s conference by following this link. My pattern matching deep dive presentation started life about a year ago as a post covering some of the new keywords in the explain plan that are linked to pattern matching, see here. It has now expanded to cover a much wider range of topics.
The aim of this group of posts is to help you understand the underlying mechanics of the MATCH_RECOGNIZE clause. During these posts we will explore key concepts such as: how to get consistent results, using built-in debugging functions, deterministic vs. non-deterministic state machines, back-tracking (what is it and how to identify when it is occurring), and finally greedy vs. reluctant quantifiers. If you need a quick refresher on how MATCH_RECOGNIZE works then I would recommend that you take a look at the following links[Read More]

The data warehouse insider is written by the Oracle product management team and sheds lights on all thing data warehousing and big data.


« August 2016