Order Line Item in Superstore; Individual Opportunity in Opportunity table
What is the grain?
100
Every row in Superstore that used to be labeled ‘Office Supplies’ is now just labeled ‘Supplies’
What is Type 1 or Overwrite?
100
Method of grabbing data, set of protocols for interacting with software applications
What are APIs?
100
The grain of this store is each individual product within an order, NOT each order
What is Superstore?
100
City where the Tableau/HyPer team is based
Where is Munich?
200
A community of interacting data pieces and their physical environment
What is a data ecosystem?
200
We document PC's favorite NFL teams. Fearghal's favorite team is now the Seahawks, no longer the Patriots. We want to be able to track who he rooted for at different time periods in his life.
What is Type 2?
200
reduction in volume; some examples are dictionary encoding, run-length, frame of reference, and delta encoding
What is Compression?
200
Two rows of data in this store would look like this: FearghalGunning,Furniture,11,162,AnikaMartynowych,OfficeSupplies,12,110
What is a Row Store?
200
Taking returned results and storing them in memory for future reference
What is Caching?
300
Where Google BigQuery, Snowflake, Google Analytics, Google Sheets are hosted
What is the Cloud?
300
Birth Name, Original Credit Score
What is Type 0 or Retain Original?
300
Fast, Columnar, Compressed, Snapshot, Architecturally aware/in memory, TQL are features of this data store
What are Extracts?
300
Two rows of data in this store would look like this:
FearghalGunning,AnikaMartynowych,Furniture,OfficeSupplies,11,12,162,110
What is a Column Store?
300
Runs OLTP queries in place and stores change buffers to accurately return OLAP queries at the same time.
What is HyPer?
400
This schema can result in a dimension table be multiple joins away from a fact table, often making it difficult for business users to understand and navigate
What is snowflake?
400
Used when a large group of attributes in a dimension rapidly changes and we want to track the changes at a summary level. Think demographic data as an example
What is Type 4?
400
Replaces values with simple tokens, like integers; good for low-cardinality columns; e.g. Furniture, Office Supplies and Technology become 1, 2, and 3
What is Dictionary Compression?
400
MongoDB uses this type of store
What is Document Store?
400
Introduced in 9.0; eliminates the need for serialization and gives the ability to run up to 16 concurrent queries
What are parallel queries?
500
Sending fewer queries by consolidating like queries together
What is Query Fusion?
500
It’s wedding season! We want to track the person’s current and previous last name, regardless of when this change occurred.
What is Type 3?
500
replaces repeated characters with a number; AAABBCCCC becomes 3A2B4C
What is Run-Length Encoding?
500
Storing an attribute together on the disk; up to 200x improvement on "Analytic queries"
What is a column store?
500
In this model, a query consists of multiple operators, and each operator presents an interface, next(), that returns a tuple at a time to the next operator in the tree