Data Warehouse Forces and Threats: interactions that, when unopposed, will change the motion of technical, economical, social, organisational and process structures with high potential of causing suffering
THREATS* Mistakes in data might lead not only to wrong business decisions, but might also have legal, financial or existential implications. *serious and real
SUFFERING ▪︎ Bad consistency and no transparency No definitions, too many definitions, obscure definitions. Vague opinions in production. ▪︎ Slow time-to-market Time from a requirement or from observing a change to deployment in the production takes too much time. ▪︎ Low performance … despite having the best hardware, systems, algorithms. (some of it)
ARCHITECTURE + METADATA separation of concerns and reduction of complexity potential reduction and annotation of problem space and facilitation of reasoning ∑ A→B
STARTING WITH ARCHITECTURE 1. Pick one: If in doubt – any known to work. Any separation of concerns is better than none. 2. Make it formal and documented. Otherwise our effort will be dissolved and the content swampified. 3. Stick with it for a while and observe. 4. Adjust as necessary.
STARTING WITH METADATA 1. Pick a problem 2. Use a spreadsheet Software at hand, no installation needed; universal, readable and editable by non-engineers. 3. Suffer through the spreadsheet-exchange drill phase Mirror of our processes – seeing the genuine pain points will be useful later. 4. Use functional approach to metadata composition and application … from those spreadsheets. Example: relational algebra library in the language of our ecosystem. 99.(later) Move spreadsheets into a metadata repository
Doing Things To Data Doing More Things To Data … Doing Things To Data Doing More Things To Data … Pipelines without metadata Pipelines with metadata metadata data
DATA QUALITY INDICATORS Doing Things To Data Doing More Things To Data … metadata data quality measurements data quality indicators data metadata definition, computation, warning/error thresholds, ownership, affected business entity, …
COMMON PATTERNS Automatically Generated Artefacts Metadata Manually Crafted Artefacts IS ∑ denormalize aggregate pivot patterns ∑ controlled growth probably the same, who knows? IS ∑ IS ∑ uncontrolled growth
VISUALISATION AND EXPLORATION Browse-ability: How can we explore a metric? How can we drill down? User Interface Metadata Physical Data Region … name Sales Revenue Visits … … 3 2 1 id Cubes Geography … name Date 2 … id … 1 Dimensions Europe Germany Berlin regions Country City Levels 2 region_code country_name … 2 Country 3 country_iso 1 key Region … name id City 2 dim label 2 region_name city_name city_code … countries cities generated which column? concept-to-user propagation
GET /cube/sales/aggregate? cut=date:2010 & split=status:1 & drilldown=date|region & page=10 page_size=100 & SQL → Metadata Logical Model Physical Physical Data Store Query Context Input Output Cube all attributes base attributes ⨝ joins database metadata Store Mapper locale parameters create schema collect and sort dependencies map attributes mappings mappings of base attributes fact table naming convention hierarchies Star Schema ̣/❄ compile attributes base attributes dependant attributes columns make star (topological sort) query attributes SQL Query Context create context base columns column expressions for attributes SELECT, GROUP BY “star” join statement FROM conditions WHERE Cubes 1.1 – SQL Query Construction A,B,C? SQL
TRANSPARENT REPRESENTATIONS Physical Data Store(s) Pre-Aggregated 3 rd Normal Form source of truth derived and managed artefacts Metadata ∑ ∑ ∑ ∑ Multi-Dimensional Query Server ∑ Aggregator metadata repository past 12 months ? ⨝s are expensive Alternative artefacts: a multi-dimensional data store
Force/Threat Architecture Metadata Change separation of concerns abstraction, generalisation Growth (structural) separation of concerns, modularity optimisation through better reasoning Complexity separation of concerns, destroy-ability reduction of problem-space, coping with heterogeneity Threats transparency, separation of quality data accounting, verifiable data quality, provable consistency, source of truth
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