Video

He is a Great instructor. I learned a lot. Thank you.

Doesn't Type 5 dimension directing towards Snowflake design of dimension?

Great presentation. For Ragged hierarchy example of salesperson and managers would be highly relatable. Like sales is attributed to a salesperson and many salesperson fall into a specific manger and then senior manager..

Great work.

Excellent video, gives you a basic understanding of the dimensional modelling concepts.

DWS Consulting looks exciting place to associate with!

Lead and lag functions can do the same thing for the ragged hierarchy

Where can we download the slides? It would be very helpful. Thanks a lot

Excellent talk. Crystal clear explanation. Thanks a lot. Greetings from Chile-SoutAmerica.

22:09 How come an 8kb block with int as 8 byte only holds 400 entries. It should be 8kb/8 bytes, 1000 entries.

This is a good video on Dimensional Modeling based on a case study - highly recommended czcams.com/video/7HyGM3Iw0Kc/video.html

Awesome intro to DW design.

Start from 1:08

This is an absolute goldmine if one wants to get the basics of dimensional modelling. Time really well spent. Thank you DWS and Ross for publishing this!

Who is here because of the block?

Really nice set of presentations. Learned alot, thanks.

Dws now part of HCL technologies

2 dollar spirits? Come to Bulgaria ;-)

Wow, he is the best

Thank you for sharing this video!

thank you so much for your sharing.

I have one doubt- during B-Tree slide, how come 400 entries comes in one block of 8KB if you are creating an index on a 8 byte field? As per my calculation it will be 8000/8 = 1000 entries, does it make sense? Will help if you anyone can clarify.

Actually, a DW is the end product of a data journey What Organizations, Institutions and Companies are now implementing are 1) Source to Data Lakes (Exact Copies of the Source) 2) Data Lakes to Staging (Prepare the data) 3) Staging to DW

In example 5. select c.customer_name , c.customer_address, i.invoice_num, i.invoice_date,i.invoice_amount from Customer c, ---100 k rows Invoice i ---- 10 M rows where c.cust_id=i.cust_id AND c.customer_name=' Tarun Kumar' -- exactly one row AND i.invoice_date >= (sysdate-12) -- 20 % of all rows If we are scanning more than 20% of rows should we add the index on an invoice date? Does not it violate the thumb rule?

super helpful and really nice presentation.

boring

The real talk starts at 10:20.

Excellent Presentation.Cleared a lot of concepts.

I was once thrown out of oracle interview for telling B tree is Binary Tree... :) Wish the interviewer would have seen this and knew I am not alone...

waste of time.

These presentations are extremely helpful. I was able to understand all of the content. Thank you.

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Confirming dimensions are dimension table that combine or the ones that provide access two fact tables through them, but this was explained in very round about way and it was not clear

nice course on data modeling: www.udemy.com/datamodeling/?couponCode=PROMO17

DW Architecture, Data Modeling, ERwin, ETL fundamentals, Business Reporting and Data Visualisation twitter.com/IbexMarketing/status/818964583104643072

DW Architecture, Data Modeling, ERwin, ETL fundamentals, Business Reporting and Data Visualisation twitter.com/WikiOffer/status/818964539131588608

Excellent Video. I could able to solve my performance problem with this information. Thanks Ross

Come on, give it a rest! Bill (W. H.) Inmon is the grandfather of DW. Anyone who is anyone knows that.

Hi Ross in the recursive relationship, we can filter also on the basis where parent key = child key?

Hi Ross just thinking, in your treatment-diagnosis example, during ETL load can't I group the rows to have coma separated diagnosis, which then simplifies the architecture and there is no need of any bridge table. So you have patient joined to treatment fact on patient key and with diagnosis appearing in treatment fact as comma separated values? We also eliminate the exercise of "constructing" possible "groups of diagnosis", to be more close to real world. Also with this architecture then we have no need for weighting factor.... your thoughts please.

In Q&A, talking about network latency as a reason for performance degradation reading from disks, because the data has to go back and forth - Even if the data has to be read from the buffer cache where the buffer cache is also in the server memory and hence there is a round trip (network latency). So, doesn't it just come down to only the quick access from memory versus relatively slow access from the disk? Because, there is network latency in both? Thanks

the manager was hot.....

I want to learn your way - in the end you said there will be more presentations in future. Hope you have given them. But sadly, I was not able to find the links in CZcams. Can you help me finding those. Thanks in advance.

Ross you are awesome. I need more links to your video s

where can I find the link to more presentation s of urs. loads of things to learn from you

Thank you Ross! Excellent video, where can I find the video for next session?

Ross ... Its a pleasure watching your presentation on Dimensional Modeling.(I watched all 3 continuously) I believe going through the 3rd Edition of toolkit, though Ralph strongly opposes urges for normalization and believes in dimensional approach for modeling. In my opinion SCD4/SCD5 are definitely an alternate definition of Normalization to tackle Monster Dimensions. Similarly Junk Dimensions/Out-trigger are concept that is indeed supportive to Normalization. I feel both Bill Inmon & Ralph Kimball's approach are widely used. I am fascinated by Dimensional Modeling, I had been involved in projects with Normalization approach and would love to make foundation more solid in Dimensional Modeling. Thanks for all the insightful video tutorials, enjoyed and relished every part of the session. Thanks for providing it to the larger audience via CZcams.

That quiz team - what a fine bunch of athletes. Great video

A very elegant presentation. The exercise was perfect and I feel the guy representing Buffer Cache will definitely remember oracle index working for a long long time.

I am learning Dataware house concepts and your video is really nice to start with, thanks for it! it was a long video I took 2 days to watch it :), but loved the kitchen. please keep posting its nice to listen to you!