SQL for Data Warehousing and Analytics

In my last post I looked at Oracle’s Cloud Services for data warehousing and described how they are based around engineered systems running the industry’s #1 database for data warehousing, fully optimised for data warehousing workloads and providing 100% compatibility with existing workloads. Most importantly, Oracle customers can run their data warehouse services on-premise, in the Cloud or using hybrid Cloud using the same management and business tools. I also looked at how Oracle’s Cloud Services for data warehousing are designed to simplify the process of integrating a data warehouse with cutting edge business processes around big data. Oracle Cloud offers a complete range of big data services are available to speed up the monetisation of data sets: Oracle Big Data Cloud Service, Big Data Preparation Cloud, Big Data Discovery, IoT Cloud. In this post, the last in this series, I am going to discuss Oracle’s cloud architecture for supporting data warehousing projects. Complete ...

In the last blog post ( Data Warehousing in the Cloud - Part 1 ) I examined why you need to start thinking about and planning your move to the cloud: looking forward data warehousing in the cloud is seen as having the greatest potential for driving significant business impact through increased agility, better cost control and faster data integration via co-location. In the last section I outlined the top 3 key benefits of moving your data warehouse to the Oracle cloud: it provides an opportunity to consolidate and rationalise your data warehouse environment, it opens up new opportunities to monetise the content within your warehouse, new data security requirements means require IT teams to start implementing robust data security systems alongside comprehensive audit reporting. In this post I am going to review Oracle’s cloud solutions for data warehousing, how Oracle’s key technologies enable Data Warehousing in the cloud and why Oracle’s Cloud runs Oracle better than any other clo...

Why is cloud so important? Data warehouses are currently going through two very significant transformations that have the potential to drive significant levels of business innovation: The first area of transformation is the drive to increase overall agility. The vast majority of IT teams are experiencing a rapid increase demand for data. Business teams want access to more and more historical data whilst at the same time, data scientists and business analysts are exploring ways to introduce new data streams into the warehouse to enrich existing analysis as well as drive new areas of analysis. This rapid expansion in data volumes and sources means that IT teams need to invest more time and effort ensuring that query performance remains consistent and they need to provision more and more environments (data sandboxes) for individual teams so that they can validate the business value of new data sets. The second area of transformation is around the need to improve the control of costs...

image courtesy of flicker: https://c1.staticflickr.com/1/55/185807556_21c547c02e_b.jpg I have been asked a number of times during and after presenting on this topic ( SQL Pattern Matching Deep Dive ) what is the difference between the various matching options such as EMPTY MATCHES and UNMATCHED ROWS . This is the area that I am going to cover in this particular blog post, which is No 4 in this deep dive series . When determining the type of output you want MATCH_RECOGNIZE to return most developers will opt for one of the following: ONE ROW PER MATCH - each match produces one summary row. This is the default. ALL ROWS PER MATCH - a match spanning multiple rows will produce one output row for each row in the match. The default behaviour for MATCH_RECOGNIZE is to return one summary row for each match. In the majority of use cases this is probably the ideal solution. However, there are also many use cases that require more detailed information to be returned. ...

    Your COMPLETE REVIEW of data warehousing and big fata from #OOW16 The COMPLETE REVIEW of OpenWorld covers all the most important sessions and related content from this year's conference, including Oracle's key data warehouse and big technologies: Oracle Database 12c Release 2, Oracle Cloud, engineered systems, partitioning, parallel execution, Oracle Optimizer, analytic SQL, analytic views, in-memory, spatial, graph, data mining, multitenant, Big Data SQL, NoSQL Database and industry data models. The COMPLETE review covers the following areas: On-demand videos of the most important keynotes Overviews of key data warehouse and big data sessions and links to download each presentation List of data warehouse and big data presenters who were at #oow16 Overview of Oracle Cloud services for data warehousing and big data Details of OpenWorld 2017 and details of how to justify your trip to San Francisco Links to the data warehouse and big data product ...