Data Vault 2.0: Big Data Meets Data Warehousing
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Presented at All Things Open Presented by Dean Hallman with WireSoft, LLC 10/22/18 - 3:15 PM - Databases
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Data Vault 2.0: Big Data Meets Data Warehousing
- 1. DATA VAULT 2.0: Big Data Meets Data Warehousing DEAN HALLMAN WIRESOFT, LLC
- 2. DATA WAREHOUSING VS BIG DATA • Does Big Data replace Data Warehousing? Or do I need both? • What’s the difference: • Between the data flowing into a data warehouse vs big data tools? • Between the ingestion processes and infrastructure? • Data Lakes arrived with Big Data, so are they useful in Data Warehousing? • How should I model my data in EDW? • 3NF, Star Schema, same as my operational data stores? • Data Vault 2.0 • Graph Databases • What is an architecture that allows both to co-exists effectively?
- 3. Impressions (Big Data) Core Business Services Core Business Services Core Business Services Operational Data Stores D A T A L A K E Enterprise Data Warehouse CDC, snapshot Internet External Data Sources Big Data Toolchain Batch (SerDe) StagingVault RawVault BusinessVault InformationMart Streaming (Kafka) Streaming Analytics Batch Analytics (Hadoop) Schema-on-Read Schema-on-Write Data Source Landing Clients ETL ELT 𝛾 𝛾 𝛾 𝛾 BI Tools Monitoring Discovery Audit clickstream (SerDe) ETL ETL
- 4. Impressions (Big Data) Core Business Services Core Business Services Core Business Services Operational Data Stores D A T A L A K E Enterprise Data Warehouse CDC, snapshot Internet External Data Sources Big Data Toolchain Batch (SerDe) StagingVault RawVault BusinessVault InformationMart Streaming (Kafka) Streaming Analytics Batch Analytics (Hadoop) Schema-on-Read Schema-on-Write Data Source Landing Clients ETL ELT 𝛾 𝛾 𝛾 𝛾 BI Tools Monitoring Discovery Audit clickstream (SerDe) ETL ETL THE DATA MODEL
- 5. DATA VAULT 2.0 COMMON FOUNDATIONAL WAREHOUSE ARCHITECTURE • “The Data Vault Model is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach encompassing the best of breed between 3rd normal form (3NF) and star schema. The design is flexible, scalable, consistent and adaptable to the needs of the enterprise” -- Dan Linstedt, Creator of Data Vault • Data loaded as-is from sources, no edits or cleanup • Append-only to afford highest performance • Agile & agnostic to changes in the operational store’s data model • Essentially, a prescription for Layered Graph to Relational Mapping
- 6. DATA WAREHOUSING & DATA VAULT 2.0 • 60’s, 70’s, 80’s • E.F. Codd => 3NF • Bill Inmon invents Data Warehousing concept • Dr. Ralph Kimball popularizes Star Schema design • 90’s, 00’s: • Dan Linstedt creates Data Vault Model @ DOD • 2014: • Dan Introduces Data Vault 2.0
- 8. Source: “What are Graph Databases and Why should I care?“, by Dave Bechberger of Expero
- 9. SOLVE BY STAR SCHEMA ?
- 10. RELATIONAL VS GRAPH DATABASES • Enterprise Grade • Well-worn path • SQL has been relatively stagnant vs programming languages
- 11. GRAPH DATA MODEL Source: https://neo4j.com/developer/graph-database/
- 12. GRAPH DATABASE VS DATA VAULT
- 13. GRAPH DATABASE VS DATA VAULT
- 14. Flight Base Dest Forecast Record Source LoadDate Depart Gate LGA 2018-10-11 1:25P M B27 CAE 2018-10-24 3:30P M A14 SFO 2018-09-06 8:55P M G19 RDU 2018-08-12 4:45P M C22 SERVICED_BY Record Source Airport CAE Load Date 2018-11-17 Source Id 20181117-32-983 Aircraft Base Service FAA NTSB Record Source LoadDate Model Tailno United 2017-02-11 767 1477 Delta 2015-11-04 A6 2381 Alaska 2013-08-28 747 8312 Frontie r 2016-07-19 182 1438 Record Source United Airlines Load Date 2018-01-17 Source Id 2412c SERVICED_BY Base Dest Manifest Record Source LoadDate Begin End United 2017-02-11 2017-04-23 2017-09-23 Delta 2015-11-04 2015-12-01 2017-04-22 Alaska 2013-08-28 2013-09-14 2016-05-04 Frontie r 2016-07-19 2016-08-02 2018-04-11 Record Source United Airlines Load Date 2018-09-17 Hubs Links SatellitesTab
- 15. • Organizations which design systems ... are constrained to produce designs which are copies of the communication structures of these organizations - Mel Conway
- 16. FLIGHT Base Dest Forecast Record Source LoadDate Depart Gate LGA 2018-10- 11 1:25P M B27 CAE 2018-10- 24 3:30P M A14 FLIGHT Record Source Airport CAE Load Date 2018-11-17 Source Id 20181117-32-983 Aircraft Bas e Service FAA NTSB Record Source LoadDate Model Tailno United 2017-02- 11 767 1477 Delta 2015-11- 04 A6 2381 Alaska 2013-08- 28 747 8312 Frontie r 2016-07- 19 182 1438 Record Source United Airlines Load Date 2018-01-17 Source Id 2412c Airport Base Dest Manifest Record Source LoadDate Begin End United 2017-02-11 2017-04-23 2017-09- 23 Delta 2015-11-04 2015-12-01 2017-04- 22 Alaska 2013-08-28 2013-09-14 2016-05- 04 Frontie r 2016-07-19 2016-08-02 2018-04- 11 Record Source United Airlines Load Date 2018-09-17 Airline Base Service FAA NTS B Record Source LoadDate Model Tailno United 2017-02-11 767 1477 Delta 2015-11-04 A6 2381 Record Source United Airlines Load Date 2018-01-17 Source Id 2412c Hubs Links SatellitesTab
- 18. • Modeled after self- organizing networks • A Business Key identifies a key concept in business. • They have a business meaning • They are unique and have very low propensity to change • Business keys change only when the business change • Enables (forces) cross- source modeling Source: http://www.di.univr.it/documenti/OccorrenzaIns/matdid/matdid232240.pdf
- 22. DATA VAULT 2.0 MODELING: HUBS, LINKS & SATELLITES
- 24. Impressions (Big Data) Core Business Services Core Business Services Core Business Services Operational Data Stores D A T A L A K E Enterprise Data Warehouse CDC, snapshot Internet External Data Sources Big Data Toolchain Batch (SerDe) StagingVault RawVault BusinessVault InformationMart Streaming (Kafka) Streaming Analytics Batch Analytics (Hadoop) Schema-on-Read Schema-on-Write Data Source Landing Clients ETL ELT 𝛾 𝛾 𝛾 𝛾 BI Tools Monitoring Discovery Audit clickstream (SerDe) ETL ETL THE DATA Impressions vs Business Data
- 25. ENTERPRISE DATA SILOS Small DataLarge DataBig Data Describes the user base Describes the Enterprise Describes the Product
- 26. Instance Grain Transaction Grain Audit Grain Impression Grain Big Data Enterprise Data Warehouse Operational Data Stores Impression Analytics Business Analytics External Data Sources DATA GRANULARITY FUNNEL
- 27. Impressions (Big Data) Core Business Services Core Business Services Core Business Services Operational Data Stores D A T A L A K E Enterprise Data Warehouse CDC, snapshot Internet External Data Sources Big Data Toolchain Batch (SerDe) StagingVault RawVault BusinessVault InformationMart Streaming (Kafka) Streaming Analytics Batch Analytics (Hadoop) Schema-on-Read Schema-on-Write Data Source Landing Clients ETL ELT 𝛾 𝛾 𝛾 𝛾 BI Tools Monitoring Discovery Audit clickstream (SerDe) ETL ETL DATA INGESTION ETL vs ELT vs SerDe
- 28. ETL VS ELT VS SerDe • Beware the Turing tar-pit, in which everything is possible, but nothing of interest is easy - Alan Perlis
- 29. DATA CLASSIFICATION MATRIX: DECLARATIVE VS INTERPRETIVE Declarative Interpretive HadoopRDBMS Web Events Media Player
- 30. DATA WAREHOUSING • Deep Topic • 60’s, 70’s, 80’s • E.F. Codd => 3NF • Bill Inmon invents Data Warehousing concept • Dr. Ralph Kimball popularizes Star Schema design • 90’s, 00’s: • Dan Linstedt creates Data Vault Model @ DOD • 2014: • Dan Introduces Data Vault 2.0 • Data Warehouse vs Operational Data Stores • Data Warehouse as Version Control System • MapReduce, 2004, Google by Jeffery Dean and Sanjay, “MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS” , GFS • Nutch 2005, Hadoop 2006, 2007 - Doug Cutting • What exactly is “Big Data”? BIG DATA
- 32. Client User Time Series Event Record Analysis STRUCTURED USER EXPERIENCE losslessL p L p L e
- 33. ETL OR SERDE ? S3 Hadoop Time Series Event Record Analysis Deserializer L e L d L m Client User Serializer L p L p Eventlog.e Eventlog.d L e Single Source (Version Locked) Kafka/Kinesis LeInternet
- 35. OTHER CHALLENGES • Satellites must be loaded chronologically • Time-based scheduling vs data-availability scheduling
- 36. QUESTIONS? • Contact: Dean Hallman rdhallman@gmail.com Linkedin: https://www.linkedin.com/in/dean-hallman/
Notes de l'éditeur
- No single answer, but convention over configuration has one the day Data Warehousing --- 60’s, 70’s, 80’s E.F. Codd => 3NF Bill Inmon invents Data Warehousing concept Dr. Ralph Kimball popularizes Star Schema design 90’s, 00’s: Dan Linstedt creates Data Vault Model @ DOD 2014: Dan Introduces Data Vault 2.0 Data Warehouse vs Operational Data Stores Data Warehouse as Version Control System Big Data ----- MapReduce, 2004, Google by Jeffery Dean and Sanjay, “MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS” , GFS Nutch 2005, Hadoop 2006, 2007 - Doug Cutting What exactly is “Big Data”?
- * 30 seconds * Slow down, cover this slide thoroughly * Lambda architecture
- * 30 seconds * Slow down, cover this slide thoroughly * Lambda architecture
- Data Warehouse vs Operational Data Stores Data Warehouse as Version Control System
- Same SQL abstraction level
- Graph databases - single property namespace - could impose naming convention as a namespace
- Hubs work to counter Conway’s law
- 3-way relationship - hypergraph / hyperedge
- Provide context and instance data for the hub-link relationship
- Encode the relationship between the nouns of your system
- * 30 seconds * Slow down, cover this slide thoroughly * Lambda architecture
- * 30 seconds * Slow down, cover this slide thoroughly * Lambda architecture
- Too close to the forest, forget to see the trees Is the business intelligence scattered out in the field Or centralized in the back office? Actors in the system are intelligent? Learn lanuage, conjugate verbs, form new sentences
- Serializer/Deserialize: Reusable package to be imported into a Lambda Test suite that ensures Serializer / Deserializer agree on before/after result