OLAP Solutions using Pentaho Analysis Services. Gabriele Pozzani

Transcription

1 OLAP Solutions using Pentaho Analysis Services Gabriele Pozzani

2 PAS Pentaho Analysis Services (PAS) provides OLAP capabilities To interactively analyze data through a cross-tab interface No need to define a query A front-end provides the interface to retrieve and format data Drill-down Drill-up Slicing Dicing

3 PAS components (I) PAS consists of four components 1. Mondrian OLAP Engine: receives MDX queries from JPivot and returns a multi-dimensional result-set Included in the Pentaho Server 2. Schema Workbench: designes and tests Mondrian cube schemas Cubes are used by Mondrian to interpret MDX and translate it into SQL queries on a RDBMS

4 PAS components (II) 3. JPivot analysis front-end: a Java-based analysis tool. Front-end for OLAP cubes 4. Aggregate designer: a designer for generating aggregate tables to speed up the analytical engine

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6 Schemas Mondrian Schemas are XML documents Describe multidimensional cubes Describe the mapping between multi-dimensional and relational model Is used to translate MDX to SQL

7 MDX MDX: Multi-Dimensional expressions A language designed for querying OLAP databases A de facto standard developed by Microsoft

8 Pentaho Schema Workbench

9 Pentaho Schema Workbench PSW is a graphical tool To create Mondrian schemas To publish schemas to the Pentaho Server

10 Connect to DB The first thing to do is to establish a connection to the database Options Connections...

11 JDBC Explorer Once the connection has been established you can explore the database File New JDBC Explorer

12 Create a new schema The schema editor can: Create a new schema File New Schema Save the schema on disk.xml Edit object attributes Switch to view the XML representation of the schema Only view. No editing

13 Main tasks Basic tasks for defining a schema are: 1. Create a schema 2. Create cubes 2.1. Choose a fact table 2.2. Add measures 3. Create dimensions 3.1. Edit the default hierarchy and choose a dimension table 3.2. Define hierarchy levels 4. Associate dimensions with cubes

14 1. Create a schema File New Schema

15 2. Create cubes 2.1!!!

16 2.1. Choose a fact table DB Schema Table name in the schema

17 2.2. Add measures

18 3. Create dimensions (I) Dimensions can be added to: A cube: "private dimensions" known only to the cube that contains them A schema: "shared dimensions" that can be associated to multiple cubes

19 3. Create dimensions (II) Fact table foreign key Date/time related dim. has TimeDimension type

20 3. Create dimensions (III) Usual dimensions have StandardDimension type 3.1!!!

21 3.1. Add/edit hierarchies A new hierarchy is created for each dimension New hierarchies can be added to dimensions Each hierarchy must have a table node and one or more levels

22 3.1. Dimension table Same settings for fact tables

23 3.2. Add hierarchy levels

24 4. Associate shared dimensions Shared dimensions can be associated to a cube adding a "Dimension usage" Shared dim.

25 Testing and deployment Once schemas have been defined they may be Tested using the MDX query tool (MDX) included in PSW Published to the Pentaho Server

26 MDX query tool (I) File New MDX Query If a schema editor is open MDX attempts to connect to the underlying DB for loading the schema definition

27 MDX query tool (II) A query can be entered in the upper pane The result is shown in the lower pane

28 Publishing the cube (I) File Publish... Server URL Password specified in publisher_config.xml User with privileges for publishing

29 Publishing the cube (II) If the connection succeeds a dialog appears Choose the location in the server's solution repository where to save the schema Specify the data source to use at the server side to execute the SQL queries (corresponding to the MDX ones)

30 JPivot

31 JPivot Once a cube has been published it can be used to build analysis applications Pentaho provides the JPivot front-end in the Pentaho User Console

32 Analysis View

33 Create a new analysis view Schema to use Cube to use defined into the schema

34 New analysis view JPivot toolbar

35 Drilling Drilling allows the user to navigate from one level of aggregation to another

36 Drilling flavors There are 4 different ways to drill, with different drill result Different drill ways can be selected in the toolbar Drill member Drill position Drill replace Drill through Apply to dimensions Apply to measures

37 Drill member & Drill position Drill member: the drilling on one instance of a member is also applied to all other instances of this member Drill position: the drilling occurs directly to the member instance and it is not applied to other instances of that member

38 Drill replace The drilled member is replaced with the drill result

39 Drill through It applies to measures It retrieves the detail rows of the rolled up measure aggregate value and shows them in a separate table

40 The OLAP Navigator (I) It is a GUI that allows to control the mapping between the cube and the pivot table Which dimension is mapped to which axis How multiple dimensions on one axis are ordered What slice of the cube is used in analysis

41 The OLAP Navigator (II) The navigator has three sections A Columns section A Rows section A Filtes section

42 Controlling placement of dimensions on axes Clicking the little square before a dimension you can move the dimension from Rows (Columns) to Columns (Rows)

43 Slicing with the OLAP Navigator (I) A slicer corresponds to the MDX WHERE clause Used to show only a subset (slice) of the data Clicking on the funnel icon you move a dimension in the Filter section

44 Slicing with the OLAP Navigator (II)

45 Specifying member sets It is also possible to specify particular members on columns and rows axes

46 MDX query pane You can also view the MDX query that represent the current state of the analysis view Useful to learn MDX syntax

47 Export Print to PDF export in MS Excel format

48 Charts JPivot allows to display data in a chart The chart can be configured

49 Alternative to JPivot Pentaho has a modular structure It may be extended with new plugins SAIKU Provide a plugin for Pentaho offering lightweight OLAP features It also provides a RESTful server that can connect with any OLAP system

50 Saiku It allows to execute OLAP analysis on any cube already defined Based on the definition of what we want to see in the analysis By specifying which dimensions/measures we want on columns, rows, and filters Drag 'n' drop UI

51 Defining the analysis (I) Once a cube has been selected the available dimensions (with hierarchies) and measures are listed

52 Defining the analysis (II) Then, we can drag'n'drop dimensions and measures as we want in columns, rows, filters We are restricted only to not put measures on both columns and rows After each change the query is updated and executed automatically

53 Defining the analysis (III)

54 Filtering Filters may be applied to visible (columns and rows) and invisible (filter) dimensions

55 Ordering Each dimension and/or measure can be used to order data But not all possible combinations are allowed We can't order both by a measure on columns and a dimension on row (or viceversa)

56 Popup menus Some options for fast filtering and adding/removing dimension levels are available by clicking on columns and rows header

57 Charts Data can be also reported in a chart

58 Statistics Saiku allows also to show some statistics about columns values

59 Other commands Other available commands include: Show MDX query Drill through on cell Export Drill-Through on cell to CSV Export XLS Export CSV

60 Saiku remarks Saiku is still in development Some features of JPivot are missing Some features have bugs or malfunctionings Charts Drill through