DataAI Nexus

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Topics

383 topics

  1. 1. What is Data?
  2. 2. What is an Entity?
  3. 3. Entity Recognition
  4. 4. Tables, Columns and Rows
  5. 5. Primary and Foreign Keys
  6. 6. Types of Data
  7. 7. Good vs Bad Data
  8. 8. Causality Data
  9. 9. Inference Data
  10. 10. Master andReference Data Management
  11. 11. Metadata Management
  12. 12. Data vs Metadata
  13. 13. Active Metadata
  14. 14. Meta Metadata
  15. 15. Can Data be therewithout Metadata
  16. 16. How to SelectYour Metadata
  17. 17. Data Modelling
  18. 18. Data Modelling Layers
  19. 19. Business Modelling
  20. 20. Process Modelling (BPM)
  21. 21. BPMN(Business Process Model and Notation)
  22. 22. Domain-Driven Design (DDD) Modelling
  23. 23. Enterprise Data Modelling
  24. 24. Conceptual and Logical Architecture Modelling
  25. 25. Concept Modelling
  26. 26. Information Modelling (NIAM, ORM and FCO-IM)
  27. 27. NIAM Modelling
  28. 28. ORM Modelling
  29. 29. FCO-IM Modelling
  30. 30. Taxonomy Modelling
  31. 31. Ontology / Knowledge Modelling
  32. 32. Knowledge Graph Modelling
  33. 33. Metagraph Modelling
  34. 34. Canonical Data Modelling
  35. 35. Semantic Data Modelling
  36. 36. Conceptual Data Modelling (CDM)
  37. 37. Logical Data Modelling (LDM)
  38. 38. Physical Data Modelling (PDM)
  39. 39. What is Normalization?
  40. 40. What is Denormalization?
  41. 41. Deep Dive into Normalization Forms
  42. 42. UNF – Unnormalized Form
  43. 43. 1NF – First Normal Form
  44. 44. 2NF – Second Normal Form
  45. 45. 3NF – Third Normal Form
  46. 46. BCNF – Boyce–Codd Normal Form
  47. 47. 4NF – Fourth Normal Form
  48. 48. 5NF – Fifth Normal Form
  49. 49. 6NF – Sixth Normal Form
  50. 50. DKNF – Domain–Key Normal Form
  51. 51. Hierarchical Data Modelling
  52. 52. NETWORK DATA MODELLING
  53. 53. OBJECT-ORIENTED DATA MODELLING
  54. 54. NOSQL DATA MODELLING
  55. 55. DOCUMENT DATABASES
  56. 56. KEY–VALUE DATABASES
  57. 57. COLUMNAR (WIDE-COLUMN) DATABASES
  58. 58. GRAPH DATABASES
  59. 59. EVENT-DRIVEN DATA MODELLING
  60. 60. JSON / XML SCHEMA MODELLING
  61. 61. API / SERVICE DATA MODELLING
  62. 62. TEMPORAL DATA MODELLING
  63. 63. 3NF DATA MODELLING(BILL INMON)
  64. 64. DIMENSIONAL DATA MODELLING
  65. 65. WHAT IS A FACT Table?
  66. 66. WHAT IS A DIMENSION Table?
  67. 67. STAR SCHEMA
  68. 68. SNOWFLAKE SCHEMA
  69. 69. Data Vault MOdelling
  70. 70. Focal Point Data Modelling
  71. 71. Anchor Data Modelling
  72. 72. UNIFIED STAR SCHEMA (USS)
  73. 73. HOOK
  74. 74. METADATA MODELLING
  75. 75. SECURITY & ACCESS CONTROL MODELLING
  76. 76. AI / MACHINE LEARNING FEATURE MODELLING
  77. 77. Subjective andObjective Data Modelling
  78. 78. Data or Business Glossary
  79. 79. Data Dictionary
  80. 80. Data Catalog
  81. 81. Types of Architecture
  82. 82. Data Architecture
  83. 83. Medallion Architecture
  84. 84. Lambda Architecture
  85. 85. Kappa Architecture
  86. 86. Unified Data Architecture
  87. 87. Zero Trust Architecture
  88. 88. Neon-inspired architecture
  89. 89. On-Premises vs Cloud
  90. 90. IaaS(Infrastructure as A Service)
  91. 91. CaaS(Container as A Service)
  92. 92. PaaS(Platform as A Service)
  93. 93. FaaS (Function as A Service)
  94. 94. SaaS(Software as A Service)
  95. 95. DaaS (Data as A Service)
  96. 96. AAaaS(Advance Analytics as A Service)
  97. 97. Granularity
  98. 98. The Chasm and Fan Trap
  99. 99. Data Cardinality
  100. 100. Cartesian Data
  101. 101. Data Governance (DG)
  102. 102. Data Quality (DQ)
  103. 103. Data Deduplication
  104. 104. Data Observability
  105. 105. Data Lineage
  106. 106. Data Provenance
  107. 107. Data Classification,Categorization and Data Clustering
  108. 108. Data Categorization vsClassification
  109. 109. Data Segmentation
  110. 110. Data Labelling
  111. 111. Data Annotation
  112. 112. Data Entropy
  113. 113. Data Epistemology
  114. 114. Data Hierarchy
  115. 115. Data Anonymization/Data Pseudonymization/Data De-Identification
  116. 116. Data Identification
  117. 117. Data Generalization/Blurring and Specialization
  118. 118. Data Perturbation/ Data Swapping/Data Shuffling/Data Scrambling/Data Obfuscation
  119. 119. Static Data Masking (SDM)
  120. 120. Dynamic Data Masking (DDM)
  121. 121. Data Tokenization
  122. 122. Data Redaction
  123. 123. Data Pipelines
  124. 124. Data Transformation(ETL, ELT and ECL)
  125. 125. Reverse ETL
  126. 126. Data Conversion
  127. 127. Data Parsing orFormatting
  128. 128. CDC and Real-Time
  129. 129. ESP(Event-Stream Processing)
  130. 130. Data Security andData Privacy
  131. 131. DLP(Data Loss Protection/ Prevention)
  132. 132. Data Integrity
  133. 133. Data Compliance
  134. 134. Data Preservation
  135. 135. Data Sovereignty
  136. 136. Data Virtualization
  137. 137. Data Federation
  138. 138. Data Consolidation
  139. 139. Data Encryption andDecryption
  140. 140. Data Encoding and Decoding
  141. 141. Data Subsetting
  142. 142. Data or Web Scraping
  143. 143. Database and OLTP
  144. 144. Data Warehouse and Data Marts
  145. 145. OLTP 3NF vsData Warehouse 3NF
  146. 146. Immutable Data Warehouse
  147. 147. Logical Data Warehouse
  148. 148. Big Data
  149. 149. Data Lake
  150. 150. Big Data vs Data Lake
  151. 151. Data Lakehouse
  152. 152. Data Hub
  153. 153. Data Fabric
  154. 154. Delta Lake
  155. 155. Star Schema
  156. 156. Snowflake Schema
  157. 157. Galaxy Schema
  158. 158. OLAP - Cube
  159. 159. SQL and NoSQL Databases
  160. 160. Data Mesh
  161. 161. Data Swamp
  162. 162. HTAP
  163. 163. Data Room
  164. 164. Data Locality
  165. 165. Object, File and Block Storages
  166. 166. Hadoop Architecture
  167. 167. Hadoop, HDFS and Hive
  168. 168. Data Sprawl
  169. 169. Dark Data and Dormant Data
  170. 170. Data Detritus
  171. 171. Data Dividend
  172. 172. Data Assets
  173. 173. Data Liabilities
  174. 174. Data Citizens
  175. 175. Data Spread
  176. 176. Data Intuition
  177. 177. Big Data File Formats
  178. 178. Query Optimization
  179. 179. Index
  180. 180. Partitioning
  181. 181. Sharding
  182. 182. ACID
  183. 183. BaSE
  184. 184. DevOps(Development Operations)
  185. 185. CI/CD
  186. 186. DevSecOps(Development Security Operations)
  187. 187. DataOps(Data Operations)
  188. 188. Difference betweenDevOps and DataOps
  189. 189. MLOps
  190. 190. DLOps(Deep Learning Operations)
  191. 191. ModelOps(Model Operations)
  192. 192. ITOps (IT Operations)
  193. 193. AIOps(Artificial Intelligence Operations)
  194. 194. Data Science vsData Mining
  195. 195. Machine Learning vs Deep Learning
  196. 196. Supervised vsUnsupervised Learning
  197. 197. AI vs Data Science
  198. 198. Data Algorithms
  199. 199. Data Splitting forData Science
  200. 200. Feature Table inML Modelling
  201. 201. Data Scrubbing,Cleansing andCleaning
  202. 202. Data Dredging,Snooping,p-hacking, andFishing
  203. 203. Data Wrangling,Mangling orMunging
  204. 204. Data Enrichment
  205. 205. Data Democratization
  206. 206. Data Liberalization
  207. 207. Data Literacy
  208. 208. Data Driven Organization
  209. 209. Data Entitlement vsAuthorization
  210. 210. Authentication vsAuthorization
  211. 211. Data Self-Service
  212. 212. Business Intelligence andBusiness Analytics
  213. 213. Data Visualization
  214. 214. Data Blending andIntegration
  215. 215. Data Mashup
  216. 216. Data Harmonization
  217. 217. Data Discovery
  218. 218. Heat Map
  219. 219. Data vs Information vsKnowledge vsWisdom
  220. 220. Data Monetization
  221. 221. Hub-and-Spoke andPoint-to-Point
  222. 222. Critical Data Elements (CDE)
  223. 223. Data Ethics
  224. 224. Data Anomalies
  225. 225. Data Surfing
  226. 226. Semantic Layer
  227. 227. Augmented Analytics
  228. 228. Data Island
  229. 229. Data Silos
  230. 230. Data Synthetic or Mockup Data
  231. 231. SISD, SIMD, MISD and MIMD
  232. 232. Data Vectorization
  233. 233. Data Due Diligence
  234. 234. Data Maturity
  235. 235. Data Filtering
  236. 236. Data Validation
  237. 237. Data Inventory
  238. 238. Data Curation
  239. 239. Data Syndication
  240. 240. Data Supply Chain
  241. 241. DLM(Data Lifecycle Management)
  242. 242. Data Usability
  243. 243. Data Sharing
  244. 244. Data Aggregation
  245. 245. Data Profiling
  246. 246. Data Standardization
  247. 247. Geocoding
  248. 248. Data Matching and Linking
  249. 249. Data Serving or Serving Layer
  250. 250. Consumption Layer
  251. 251. MPP(Massive Parallel Process)
  252. 252. Canned Data
  253. 253. Canned Reports vs Adhoc Reports
  254. 254. Multidimensional eXpression(MDX)
  255. 255. Data Drift
  256. 256. Concept Drift
  257. 257. Scope Creep
  258. 258. Data Discrepancies
  259. 259. Data Skew Issue
  260. 260. Data Coupling
  261. 261. Data Imputation
  262. 262. Data Disambiguation
  263. 263. Data Fusion
  264. 264. Integration Hub
  265. 265. NLP
  266. 266. Data Strategy vs Data Management
  267. 267. Hype Cycle forData Management
  268. 268. Modern Data Stack
  269. 269. Why to define Success isimportant for Data Solution
  270. 270. PVP Approach
  271. 271. Data Backup and Mirroring
  272. 272. Data Integration
  273. 273. Data Integration andInteroperability
  274. 274. Data Abstraction Layer
  275. 275. Data Contextualization
  276. 276. Data Futurology
  277. 277. Data Interchange
  278. 278. Data Replication
  279. 279. Data Value Realization
  280. 280. Data Strategy vsData Implementation
  281. 281. Data Doom Pile
  282. 282. Data Collaboration
  283. 283. Data Tech Debt
  284. 284. Data Ownership vsData Governance
  285. 285. Data Archiving
  286. 286. Data Compression
  287. 287. Data Literacy Assessment
  288. 288. Data Sources
  289. 289. Data Warehouse vs Data Lake
  290. 290. Data Deactivation
  291. 291. Data Embargo
  292. 292. Data Streaming
  293. 293. Batch Processing
  294. 294. NewSQL
  295. 295. Delta Sharing
  296. 296. Cyber Security forData Solution
  297. 297. CDC vs CDC Framework
  298. 298. Slowly ChangingDimensions (SCD)
  299. 299. Text And EmbeddedVectorization
  300. 300. Concurrent vs MPP
  301. 301. Information Modelling
  302. 302. Standardization vsTransformation
  303. 303. Semantic Interoperability
  304. 304. POC,Prototype,Pilot/ MVP,Testing, andProd
  305. 305. POC
  306. 306. Prototype
  307. 307. Pilot or MVP
  308. 308. Productionized Solution
  309. 309. CRUD and CRAP
  310. 310. Accessibility and Visibility
  311. 311. Generative AI
  312. 312. ETLT
  313. 313. Synchronization andPropagation
  314. 314. Supertype and Subtype
  315. 315. ISO 55000/ 55001/ 550012/ 550013 For Data as Asset
  316. 316. ISO 11179For Metadata Management
  317. 317. ISO 3166For Locations
  318. 318. Basel Standards
  319. 319. Digitization andDigitalization
  320. 320. LLM(Large Language Model)
  321. 321. Type of Analytics
  322. 322. MRKL
  323. 323. FinOps
  324. 324. Testing vs QA
  325. 325. Difference between IT and Digital
  326. 326. Sandbox
  327. 327. House Keeping
  328. 328. SLA
  329. 329. Watermark
  330. 330. Views
  331. 331. Materialized View
  332. 332. External Tables
  333. 333. Loops
  334. 334. Aggregation Functions
  335. 335. UDF
  336. 336. Pseudocode
  337. 337. Syntax Error
  338. 338. Importance ofStaging Layer
  339. 339. GDPR
  340. 340. Web Service
  341. 341. ElectronicData Interchange (EDI)
  342. 342. Socratic Questions
  343. 343. Attribution Data Models
  344. 344. Incrementality inPerformance Marketing
  345. 345. Ontology-DrivenData Integration andHarmonization
  346. 346. The Operational Reference Model:Implementing Dynamic Information Models
  347. 347. Materialized View orTemporary Tables in SQL
  348. 348. ISO/IEC 27001 fromData Perspective
  349. 349. ILM – InformationLifecycle Management
  350. 350. Reference Architecture
  351. 351. Data Product
  352. 352. The Good, The Bad, and The Ugly
  353. 353. Data As A Product
  354. 354. Data Product Owner
  355. 355. Data Product Ownership
  356. 356. The Evolution ofData Products and it’s Ecosystem
  357. 357. Data Platform Gravity
  358. 358. Textual orContextual ETL
  359. 359. Graph
  360. 360. Graph Theory
  361. 361. Data Modellingwith Graph Theory
  362. 362. MetaGraph
  363. 363. Knowledge Graph (KG)
  364. 364. DAGs (Directed Acyclic Graphs) orExecuton Graph
  365. 365. DOGs (Data Object Graphs)
  366. 366. Difference BetweenMetaGraph,Ontology andTaxonomy
  367. 367. Data Wrapping
  368. 368. RAG(Retrieval-Augmented Generation)
  369. 369. Quantum Computing
  370. 370. Performance in Data Lake
  371. 371. Functional andNon-Functional Testing
  372. 372. Agentic AI
  373. 373. Data Storytelling
  374. 374. Data Contract
  375. 375. Data and AI Cognitive (DAC)Architecture
  376. 376. Lakebase
  377. 377. Corporate Data
  378. 378. Key Performance Indicators (KPI)
  379. 379. Objectives and Key results (OKR)
  380. 380. Management By Object (MBO)
  381. 381. KPI vs OKR vs MBO
  382. 382. Multi-Modal Data
  383. 383. Embeddings
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Every topic of the book, readable below, with a search box to find the one you need — 383 of them with a self-check at the end.

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The source: I Am Datapedia! — Series of ‘I Am Data!’ — co-authored with Bill Inmon & Marco Wobben. By Mustafa Qizilbash.