I have a CSV file of the form (unimportant columns hidden)
player,game1,game2,game3,game4,game5,game6,game7,game8
Example data:
Alice,0,-10,-30,-60,-30,-50,-10,30
Bob,10,20,30,40,50,60,70,80
Charlie,20,0,20,0,20,0,20,0
Derek,1,2,3,4,5,6,7,8
Emily,-40,-30,-20,-10,10,20,30,40
Francine,1,4,9,16,25,36,49,64
Gina,0,0,0,0,0,0,0,0
Hank,-50,50,-50,50,-50,50,-50,50
Irene,-20,-20,-20,50,50,-20,-20,-20
I am looking for a way to make a Data Studio view where I can see a chart of all the results of a certain player. How would I make a custom field that combines the data from game1 to game8 so I can make a chart of it?
| Name | Scores |
|----------|---------------------------------|
| Alice | [0,-10,-30,-60,-30,-50,-10,30] |
| Bob | [10,20,30,40,50,60,70,80] |
| Charlie | [20,0,20,0,20,0,20,0] |
| Derek | [1,2,3,4,5,6,7,8] |
| Emily | [-40,-30,-20,-10,10,20,30,40] |
| Francine | [1,4,9,16,25,36,49,64] |
| Gina | [0,0,0,0,0,0,0,0] |
| Hank | [-50,50,-50,50,-50,50,-50,50] |
| Irene | [-20,-20,-20,50,50,-20,-20,-20] |
The goal of the resulting chart would be something like this, where game1 is the first point and so on.
If this is not possible, how would I best represent the data so what I am looking for can work in Data Studio? I currently have it implemented in a Google Sheet, but the issue is there's no way to make views, so when someone selects a row it changes for everyone viewing it.
If you have two file games as data sources, I guess that you want to combine them by the name, right?
You can do it with the blending data option. Resource > manage blends I think is the option.
Then you can create a blend data source merging it by the name.
You can add also both score fields, with different labels.
This is some documentation about it: https://support.google.com/datastudio/answer/9061420?hl=en
I'm trying to find the best data-structure/data store solution (highest performance) for the following request:
I have a list of attributes that I need to store for all individual in the US, for example:
+------------+-------+-------------+
| Attribute | Value | SSN |
+------------+-------+-------------+
| hair color | black | 123-45-6789 |
| eye color | brown | 123-45-6789 |
| height | 175 | 123-45-6789 |
| sex | M | 123-45-6789 |
| shoe size | 42 | 123-45-6789 |
As you can guess, with the general population, there are nothing unique and identifiable from those attributes.
However, let's assume that if we were to fetch from a combination of 3 or 4 attributes, then I would be able to uniquely identify a person (find their SSN).
Now here's the difficulties, the set of combinations that can uniquely identify a person will evolve over time and be adjusted.
What would be my best bet for storing and querying the data with the scenario mentioned above, that will remain highly performant (<100ms) at scale?
Current attempt with combining two attributes:
SELECT * FROM (SELECT * FROM people WHERE hair='black') p1
JOIN (SELECT * FROM people WHERE height=175) p2
ON p1.SSN = p2.SSN
But with a database with millions of rows, as you can guess.. NOT performant.
Thank you!
if the data store is not a constraint, I would use a DocumentDB, something like MongoDB, CosmosDB or even ElasticSearch.
With Mongo, for example, you could leverage its schemaless nature and have a collection of People with one property per "attribute":
{
"SSN": "123-45-6789",
"eyeColor": "brown",
"hairColor" "blond",
"sex": "M"
}
documents in this collection might have different properties, but it's not an issue. All you have to do now is to put an index on each one and run your queries.
I'm developing an application that uses a mysql database and we wanted to do an approach for history purposes, that we store the current state and the history in the same table for performance reasons (on updates the application doesn't have the id for an entity just a key pair, so it is easier just to insert a new row).
The table looks like this:
+------+-------+-----------+------------------------------+
| id |user_id| type |content |
+------+-------+-----------+------------------------------+
| 1 |'1-2-3'| position | *creation |
| 2 |'1-2-3'| position | *something_changed |
| 3 |'1-2-3'| device | *creation |
| 4 |'1-2-4'| position | *creation |
| 5 |'1-2-4'| device | *creation |
| 6 |'1-2-4'| device | *something_changed |
+------+-------+-----------+------------------------------+
Every entity is described with the user_id and type "key" pair, when something is changed in the entity a new row is inserted. The current state of an entity is selected by the highest id row from the group, which is grouped by the user_id and type. Performance wise the updates should be super fast and the selects can be slower, because those are not used often.
I would like to look up best practices and other people experiences with this method, but I don't know how to search for them. Can you help me? I'm interested in your experiences or opinions on this topic as well.
I know about Kafka and other streaming platforms, but that was sadly not an option for this.
Question
main question
How can I ephemerally materialize slowly changing dimension type 2 from from a folder of daily extracts, where each csv is one full extract of a table from from a source system?
rationale
We're designing ephemeral data warehouses as data marts for end users that can be spun up and burned down without consequence. This requires we have all data in a lake/blob/bucket.
We're ripping daily full extracts because:
we couldn't reliably extract just the changeset (for reasons out of our control), and
we'd like to maintain a data lake with the "rawest" possible data.
challenge question
Is there a solution that could give me the state as of a specific date and not just the "newest" state?
existential question
Am I thinking about this completely backwards and there's a much easier way to do this?
Possible Approaches
custom dbt materialization
There's a insert_by_period dbt materialization in the dbt.utils package, that I think might be exactly what I'm looking for? But I'm confused as it's dbt snapshot, but:
run dbt snapshot for each file incrementally, all at once; and,
built directly off of an external table?
Delta Lake
I don't know much about Databricks's Delta Lake, but it seems like it should be possible with Delta Tables?
Fix the extraction job
Is our oroblem is solved if we can make our extracts contain only what has changed since the previous extract?
Example
Suppose the following three files are in a folder of a data lake. (Gist with the 3 csvs and desired table outcome as csv).
I added the Extracted column in case parsing the timestamp from the filename is too tricky.
2020-09-14_CRM_extract.csv
| OppId | CustId | Stage | Won | LastModified | Extracted |
|-------|--------|-------------|-----|--------------|-----------|
| 1 | A | 2 - Qualify | | 9/1 | 9/14 |
| 2 | B | 3 - Propose | | 9/12 | 9/14 |
2020-09-15_CRM_extract.csv
| OppId | CustId | Stage | Won | LastModified | Extracted |
|-------|--------|-------------|-----|--------------|-----------|
| 1 | A | 2 - Qualify | | 9/1 | 9/15 |
| 2 | B | 4 - Closed | Y | 9/14 | 9/15 |
| 3 | C | 1 - Lead | | 9/14 | 9/15 |
2020-09-16_CRM_extract.csv
| OppId | CustId | Stage | Won | LastModified | Extracted |
|-------|--------|-------------|-----|--------------|-----------|
| 1 | A | 2 - Qualify | | 9/1 | 9/16 |
| 2 | B | 4 - Closed | Y | 9/14 | 9/16 |
| 3 | C | 2 - Qualify | | 9/15 | 9/16 |
End Result
Below is SCD-II for the three files as of 9/16. SCD-II as of 9/15 would be the same but OppId=3 has only one from valid_from=9/15 and valid_to=null
| OppId | CustId | Stage | Won | LastModified | valid_from | valid_to |
|-------|--------|-------------|-----|--------------|------------|----------|
| 1 | A | 2 - Qualify | | 9/1 | 9/14 | null |
| 2 | B | 3 - Propose | | 9/12 | 9/14 | 9/15 |
| 2 | B | 4 - Closed | Y | 9/14 | 9/15 | null |
| 3 | C | 1 - Lead | | 9/14 | 9/15 | 9/16 |
| 3 | C | 2 - Qualify | | 9/15 | 9/16 | null |
Interesting concept and of course it would a longer conversation than is possible in this forum to fully understand your business, stakeholders, data, etc. I can see that it might work if you had a relatively small volume of data, your source systems rarely changed, your reporting requirements (and hence, datamarts) also rarely changed and you only needed to spin up these datamarts very infrequently.
My concerns would be:
If your source or target requirements change how are you going to handle this? You will need to spin up your datamart, do full regression testing on it, apply your changes and then test them. If you do this as/when the changes are known then it's a lot of effort for a Datamart that's not being used - especially if you need to do this multiple times between uses; if you do this when the datamart is needed then you're not meeting your objective of having the datamart available for "instant" use.
Your statement "we have a DW as code that can be deleted, updated, and recreated without the complexity that goes along with traditional DW change management" I'm not sure is true. How are you going to test updates to your code without spinning up the datamart(s) and going through a standard test cycle with data - and then how is this different from traditional DW change management?
What happens if there is corrupt/unexpected data in your source systems? In a "normal" DW where you are loading data daily this would normally be noticed and fixed on the day. In your solution the dodgy data might have occurred days/weeks ago and, assuming it loaded into your datamart rather than erroring on load, you would need processes in place to spot it and then potentially have to unravel days of SCD records to fix the problem
(Only relevant if you have a significant volume of data) Given the low cost of storage, I'm not sure I see the benefit of spinning up a datamart when needed as opposed to just holding the data so it's ready for use. Loading large volumes of data everytime you spin up a datamart is going to be time-consuming and expensive. Possible hybrid approach might be to only run incremental loads when the datamart is needed rather than running them every day - so you have the data from when the datamart was last used ready to go at all times and you just add the records created/updated since the last load
I don't know whether this is the best or not, but I've seen it done. When you build your initial SCD-II table, add a column that is a stored HASH() value of all of the values of the record (you can exclude the primary key). Then, you can create an External Table over your incoming full data set each day, which includes the same HASH() function. Now, you can execute a MERGE or INSERT/UPDATE against your SCD-II based on primary key and whether the HASH value has changed.
Your main advantage doing things this way is you avoid loading all of the data into Snowflake each day to do the comparison, but it will be slower to execute this way. You could also load to a temp table with the HASH() function included in your COPY INTO statement and then update your SCD-II and then drop the temp table, which could actually be faster.