Labels

admin (1) aix (1) alert (1) always-on (2) Architecture (1) aws (3) Azure (1) backup (3) BI-DWH (10) Binary (3) Boolean (1) C# (1) cache (1) casting (3) cdc (1) certificate (1) checks (1) cloud (3) cluster (1) cmd (7) collation (1) columns (1) compilation (1) configurations (7) Connection-String (2) connections (6) constraint (6) copypaste (2) cpu (2) csv (3) CTE (1) data-types (1) datetime (23) db (547) DB2 (1) deadlock (2) Denali (7) device (6) dotNet (5) dynamicSQL (11) email (5) encoding (1) encryption (4) errors (124) excel (1) ExecutionPlan (10) extended events (1) files (7) FIPS (1) foreign key (1) fragmentation (1) functions (1) GCP (2) gMSA (2) google (2) HADR (1) hashing (3) in-memory (1) index (3) indexedViews (2) insert (3) install (10) IO (1) isql (6) javascript (1) jobs (11) join (2) LDAP (2) LinkedServers (8) Linux (15) log (6) login (1) maintenance (3) mariadb (1) memory (4) merge (3) monitoring (4) MSA (2) mssql (444) mssql2005 (5) mssql2008R2 (20) mssql2012 (2) mysql (36) MySQL Shell (5) network (1) NoSQL (1) null (2) numeric (9) object-oriented (1) offline (1) openssl (1) Operating System (4) oracle (7) ORDBMS (1) ordering (2) Outer Apply (1) Outlook (1) page (1) parameters (2) partition (1) password (1) Performance (103) permissions (10) pivot (3) PLE (1) port (4) PostgreSQL (14) profiler (1) RDS (3) read (1) Replication (12) restore (4) root (1) RPO (1) RTO (1) SAP ASE (48) SAP RS (20) SCC (4) scema (1) script (8) security (10) segment (1) server (1) service broker (2) services (4) settings (75) SQL (74) SSAS (1) SSIS (19) SSL (8) SSMS (4) SSRS (6) storage (1) String (35) sybase (57) telnet (2) tempdb (1) Theory (2) tips (120) tools (3) training (1) transaction (6) trigger (2) Tuple (2) TVP (1) unix (8) users (3) vb.net (4) versioning (1) windows (14) xml (10) XSD (1) zip (1)

Data Warehouse Process


Source DB --> SSIS --> SSAS --> SSRS

  • Application Database (Source DB).

    • Definition of needs and requirements
    • Design the Data Warehouse
      • Construction of dimensions
      • Construction of fact tables
    • ETL
      • Extract relevant data.
      • Transform data to DWH format.
      • Load data into DWH.
    • Relational Data Warehouse Management.

      • DATA Analysis
      • Dimensions and cubes in an Analysis Services solution.

        • Data Presentation – Reports.

        1 comment:

        1. You shared us a very detailed information regarding the process of a warehouse making. It looks so effective for me that is why I copy, paste and save it for future reference. Expect me to share this to others too.

          ReplyDelete