OLAP Systems and Multidimensional Queries I
|
|
|
- Grant Ramsey
- 7 years ago
- Views:
Transcription
1 OLAP Systems and Multidimensional Queries I Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, second semester Academic year 2014/15 (winter course) 1 / 45
2 Review of the previous lectures Mining of massive datasets Evolution of database systems: operational vs. analytical systems. Dimensional modeling. Extraction, transformation and load of data. 2 / 45
3 Outline 1 Motivation 2 OLAP Servers 3 ROLAP 4 SQL 5 Summary 3 / 45
4 Outline 1 Motivation 2 OLAP Servers 3 ROLAP 4 SQL 5 Summary 4 / 45
5 OLAP systems The next step is to provide solutions for querying and reporting multidimensional analytical data. The goal is to provide efficient solutions for physical representation and processing of these data. 5 / 45
6 Multidimensional reports OLAP servers provide an effective solution for accessing and processing large volumes of high dimensional data. OLAP systems provide tools for multidimensional reporting. 6 / 45
7 Outline 1 Motivation 2 OLAP Servers 3 ROLAP 4 SQL 5 Summary 7 / 45
8 Multidimensional cube The proper data model for multidimensional reporting is the multidimensional one. 8 / 45
9 Operators in multidimensional data model Roll up summarize data along a dimension hierarchy. Drill down go from higher level summary to lower level summary or detailed data. Slice and dice corresponds to selection and projection. Pivot reorient cube. Raking, Time functions, etc.. 9 / 45
10 Lattice of cuboids Different degrees of summarizations are presented as a lattice of cuboids. Example for the dimensions: time, product, location, supplier. Using this structure, one can easily show roll up and drill down operations. 10 / 45
11 Total number of cuboids For an n-dimensional data cube, the total number of cuboids that can be generated is: T = (L i + 1), i=1,...,n where L i is the number of levels associated with dimension i (excluding the virtual top level all since generalizing to all is equivalent to the removal of a dimension). For example, if the cube has 10 dimensions and each dimension has 4 levels, the total number of cuboids that can be generated will be: l = 5 10 = 9, / 45
12 Total number of cuboids Example: Consider a simple database with two dimensions: 12 / 45
13 Total number of cuboids Example: Consider a simple database with two dimensions: Columns in Date dimension: day, month, year Columns in Localization dimension: street, city, country. 12 / 45
14 Total number of cuboids Example: Consider a simple database with two dimensions: Columns in Date dimension: day, month, year Columns in Localization dimension: street, city, country. Without any information about hierarchies, the number of all possible group-bys is 12 / 45
15 Total number of cuboids Example: Consider a simple database with two dimensions: Columns in Date dimension: day, month, year Columns in Localization dimension: street, city, country. Without any information about hierarchies, the number of all possible group-bys is 2 6 : 12 / 45
16 Total number of cuboids Example: Consider a simple database with two dimensions: Columns in Date dimension: day, month, year Columns in Localization dimension: street, city, country. Without any information about hierarchies, the number of all possible group-bys is 2 6 : day street month city year country day, month street, city day, year street, country month, year city, country day, month, year street, city, country 12 / 45
17 Total number of cuboids Example: Consider the same relations but with defined hierarchies: 13 / 45
18 Total number of cuboids Example: Consider the same relations but with defined hierarchies: day month year street city country 13 / 45
19 Total number of cuboids Example: Consider the same relations but with defined hierarchies: day month year street city country Many combinations of columns can be excluded, e.g. group by day, year, street, country. The number of group-bys is then 13 / 45
20 Total number of cuboids Example: Consider the same relations but with defined hierarchies: day month year street city country Many combinations of columns can be excluded, e.g. group by day, year, street, country. The number of group-bys is then 4 2 : 13 / 45
21 Total number of cuboids Example: Consider the same relations but with defined hierarchies: day month year street city country Many combinations of columns can be excluded, e.g. group by day, year, street, country. The number of group-bys is then 4 2 : year country month, year city, country day, month, year street, city, country 13 / 45
22 Three types of aggregate functions distributive: count(), sum(), max(), min(), algebraic: ave(), std dev(), holistic: median(), mode(), rank(). 14 / 45
23 OLAP servers Relational OLAP (ROLAP), Multidimensional OLAP (MOLAP), Hybrid OLAP (HOLAP). 15 / 45
24 Outline 1 Motivation 2 OLAP Servers 3 ROLAP 4 SQL 5 Summary 16 / 45
25 ROLAP ROLAP servers use a relational or post-relational database management system to store and manage warehouse data. ROLAP systems use SQL and its OLAP extensions. Optimization techniques: Denormalization, Materialized views, Partitioning, Joins, Indexes, Query processing. 17 / 45
26 ROLAP Advantages of ROLAP Servers: Scalable with respect to the number of dimensions, Scalable with respect to the size of data, Sparsity is not a problem (fact tables contain only facts), Mature and well-developed technology. Disadvantage of ROLAP Servers: Worse performance than MOLAP, Additional data structures and optimization techniques used to improve the performance. 18 / 45
27 Grouping Group-by is usually performed in the following way: 19 / 45
28 Grouping Group-by is usually performed in the following way: Partition tuples on grouping attributes: tuples in same group are placed together, and in different groups separated, Scan tuples in each partition and compute aggregate expressions. 19 / 45
29 Grouping Group-by is usually performed in the following way: Partition tuples on grouping attributes: tuples in same group are placed together, and in different groups separated, Scan tuples in each partition and compute aggregate expressions. Two techniques for partitioning Sorting Sort by the grouping attributes, All tuples with same grouping attributes will appear together in sorted list. Hashing Hash by the grouping attributes, All tuples with same grouping attributes will hash to same bucket, Sort or re-hash within each bucket to resolve collisions. 19 / 45
30 Grouping Group-by is usually performed in the following way: Partition tuples on grouping attributes: tuples in same group are placed together, and in different groups separated, Scan tuples in each partition and compute aggregate expressions. Two techniques for partitioning Sorting Sort by the grouping attributes, All tuples with same grouping attributes will appear together in sorted list. Hashing Hash by the grouping attributes, All tuples with same grouping attributes will hash to same bucket, Sort or re-hash within each bucket to resolve collisions. In OLAP queries use intermediate results to compute more general group-bys 19 / 45
31 Grouping Example: Grouping by sorting (Month, City): Month City Sale March Poznań 105 March Warszawa 135 March Poznań 50 April Poznań 150 April Kraków 175 May Warszawa 100 May Poznań 70 May Warszawa / 45
32 Grouping Example: Grouping by sorting (Month, City): Month City Sale March Poznań 105 March Warszawa 135 March Poznań 50 April Poznań 150 April Kraków 175 May Warszawa 100 May Poznań 70 May Warszawa 75 Month City Sale March Poznań 105 March Poznań 50 March Warszawa 135 April Kraków 175 April Poznań 150 May Poznań 70 May Warszawa 100 May Warszawa / 45
33 Grouping Example: Grouping by sorting (Month, City): Month City Sale March Poznań 105 March Warszawa 135 March Poznań 50 April Poznań 150 April Kraków 175 May Warszawa 100 May Poznań 70 May Warszawa 75 Month City Sale March Poznań 155 March Warszawa 135 April Kraków 175 April Poznań 150 May Poznań 70 May Warszawa 175 Month City Sale March Poznań 105 March Poznań 50 March Warszawa 135 April Kraków 175 April Poznań 150 May Poznań 70 May Warszawa 100 May Warszawa / 45
34 Grouping Example: Grouping by sorting (Month; City; Month, City): Month City Sale March Poznań 155 March Warszawa 135 April Kraków 175 April Poznań 150 May Poznań 70 May Warszawa / 45
35 Grouping Example: Grouping by sorting (Month; City; Month, City): Month City Sale March Poznań 155 March Warszawa 135 April Kraków 175 April Poznań 150 May Poznań 70 May Warszawa 175 City Sale Kraków 175 Poznań 155 Poznań 150 Poznań 70 Warszawa 135 Warszawa 175 Month Sale March 155 March 135 April 150 April 175 May 175 May 70 City Sale Kraków 175 Poznań 375 Warszawa 310 Month Sale March 285 April 325 May / 45
36 Outline 1 Motivation 2 OLAP Servers 3 ROLAP 4 SQL 5 Summary 22 / 45
37 Querying the star schema SQL queries 23 / 45
38 SQL group by SELECT Name, AVG(Grade) FROM Students grades G, Student S WHERE G.Student = S.ID GROUP BY Name; Name AVG(Grade) Inmon 4.8 Kimball 4.7 Gates 4.0 Todman / 45
39 SQL group by SELECT Academic year, Name, AVG(Grade) FROM Students grades G, Academic year A, Professor P WHERE G.Professor = P.ID and G.Academic year = A.ID GROUP BY Academic year, Name; Academic year Name AVG(Grade) 2001/2 Stefanowski /3 Stefanowski /4 Stefanowski /2 S lowiński /3 S lowiński /4 S lowiński /4 Dembczyński / 45
40 OLAP extensions in SQL: GROUP BY ROLLUP, GROUP BY CUBE, GROUP BY GROUPING SETS GROUPING and DECODE/CASE OVER Ranking functions 26 / 45
41 GROUP BY CUBE SELECT Time, Product, Location, Supplier, SUM(Gain) FROM Sales GROUP BY CUBE (Time, Product, Location, Supplier); 27 / 45
42 GROUP BY CUBE SELECT Time, Product, Location, Supplier, SUM(Gain) FROM Sales GROUP BY Time, Product, Location, Supplier UNION ALL SELECT Time, Product, Location, *, SUM(Gain) FROM Sales GROUP BY Time, Product, Location UNION ALL SELECT Time, Product, *, Location, SUM(Gain) FROM Sales GROUP BY Time, Product, Location UNION ALL... UNION ALL SELECT *, *, *, *, SUM(Gain) FROM Sales; 28 / 45
43 GROUP BY CUBE SELECT Academic year, Name, AVG(Grade) FROM Students grades GROUP BY CUBE(Academic year, Name); Academic year Name AVG(Grade) 2001/2 Stefanowski /2 S lowiński /3 Stefanowski /3 S lowiński /4 Stefanowski /4 S lowiński /4 Dembczyński /2 NULL /3 NULL /4 NULL 3.8 NULL Stefanowski 3.9 NULL S lowiński 3.6 NULL Dembczyński 4.8 NULL NULL / 45
44 GROUP BY ROLLUP SELECT Time, Product, Location, Supplier, SUM(Gain) FROM Sales GROUP BY ROLLUP (Time, Product, Location, Supplier); 30 / 45
45 GROUP BY ROLLUP SELECT Time, Product, Location, Supplier, SUM(Gain) FROM Sales GROUP BY Time, Product, Location, Supplier UNION ALL SELECT Time, Product, Location, *, SUM(Gain) FROM Sales GROUP BY Time, Product, Location UNION ALL SELECT Time, Product, *, *, SUM(Gain) FROM Sales GROUP BY Time, Product UNION ALL SELECT Time, *, *, *, SUM(Gain) FROM Sales GROUP BY Time UNION ALL SELECT *, *, *, *, SUM(Gain) FROM Sales; 31 / 45
46 GROUP BY ROLLUP SELECT Academic year, Name, AVG(Grade) FROM Students grades G GROUP BY ROLLUP(Academic year, Name); Academic year Name AVG(Grade) 2001/2 Stefanowski /2 S lowiński /3 Stefanowski /3 S lowiński /4 Stefanowski /4 S lowiński /4 Dembczyński /2 NULL /3 NULL /4 NULL 3.8 NULL NULL / 45
47 GROUP BY GROUPING SETS SQL queries SELECT Time, Product, Location, Supplier, SUM(Gain) FROM Sales GROUP BY GROUPING SETS ((Time), (Product), (Location), (Supplier)); 33 / 45
48 GROUP BY GROUPING SETS SELECT Time, *, *, *, SUM(Gain) FROM Sales GROUP BY Time UNION ALL SELECT *, Product, *, *, SUM(Gain) FROM Sales GROUP BY Product UNION ALL SELECT *, *, Location, *, SUM(Gain) FROM Sales GROUP BY Location UNION ALL SELECT *, *, *, Supplier, SUM(Gain) FROM Sales GROUP BY Supplier; 34 / 45
49 GROUP BY GROUPING SETS SELECT Academic year, Name, AVG(Grade) FROM Students grades GROUP BY GROUPING SETS ((Academic year), (Name),()); Academic year Name AVG(Grade) 2001/2 NULL /3 NULL /4 NULL 3.8 NULL Stefanowski 3.9 NULL S lowiński 3.6 NULL Dembczyński 4.8 NULL NULL / 45
50 GROUPING(<column expression>) Returns a value of 1 if the value of expression in the row is a null representing the set of all values. <column expression> is a column or an expression that contains a column in a GROUP BY clause. GROUPING is used to distinguish the null values that are returned by ROLLUP, CUBE or GROUPING SETS from standard null values. The NULL returned as the result of a ROLLUP, CUBE or GROUPING SETS operation is a special use of NULL. 36 / 45
51 GROUPING(<column expression>) SELECT Extra scholarship, AVG(Grade), GROUPING(Extra scholarship) as Grouping FROM Students grades GROUP BY ROLL UP(Extra scholarship); Extra scholarship AVG(Grade) Grouping Yes No NULL NULL / 45
52 DECODE(expression, search, result [, search, result]... [, default] ) If the value of expression is equal to search, then result is returned, otherwise default is returned. The functionality is similar to CASE expression, The results of GROUPING() can be passed into a DECODE function or the CASE expression. 37 / 45
53 DECODE(expression, search, result [, search, result]... [, default] ) SELECT DECODE(GROUPING(Extra scholarship), 1, "Total Average", Extra scholarship) as Extra scholarship, AVG(Grade) FROM Students grades GROUP BY ROLL UP(Extra scholarship); Extra scholarship AVG(Grade) Yes 4.15 No 3.61 NULL 4.03 Total average / 45
54 OVER(): 38 / 45
55 OVER(): Determines the partitioning and ordering of a rowset before the associated window function is applied. 38 / 45
56 OVER(): Determines the partitioning and ordering of a rowset before the associated window function is applied. The OVER clause defines a window or user-specified set of rows within a query result set. 38 / 45
57 OVER(): Determines the partitioning and ordering of a rowset before the associated window function is applied. The OVER clause defines a window or user-specified set of rows within a query result set. A window function then computes a value for each row in the window. 38 / 45
58 OVER(): Determines the partitioning and ordering of a rowset before the associated window function is applied. The OVER clause defines a window or user-specified set of rows within a query result set. A window function then computes a value for each row in the window. The OVER clause can be used with functions to compute aggregated values such as moving averages, cumulative aggregates, running totals, or a top N per group results. 38 / 45
59 OVER(): Determines the partitioning and ordering of a rowset before the associated window function is applied. The OVER clause defines a window or user-specified set of rows within a query result set. A window function then computes a value for each row in the window. The OVER clause can be used with functions to compute aggregated values such as moving averages, cumulative aggregates, running totals, or a top N per group results. Syntax: OVER ( [ <PARTITION BY clause> ] [ <ORDER BY clause> ] [ <ROW or RANGE clause> ] ) 38 / 45
60 OVER(): 39 / 45
61 OVER(): PARTITION BY: 39 / 45
62 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. 39 / 45
63 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. ORDER BY: 39 / 45
64 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. ORDER BY: Defines the logical order of the rows within each partition of the result set, i.e., it specifies the logical order in which the window function calculation is performed. 39 / 45
65 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. ORDER BY: Defines the logical order of the rows within each partition of the result set, i.e., it specifies the logical order in which the window function calculation is performed. ROW RANGE: 39 / 45
66 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. ORDER BY: Defines the logical order of the rows within each partition of the result set, i.e., it specifies the logical order in which the window function calculation is performed. ROW RANGE: Further limits the rows within the partition by specifying start and end points within the partition. 39 / 45
67 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. ORDER BY: Defines the logical order of the rows within each partition of the result set, i.e., it specifies the logical order in which the window function calculation is performed. ROW RANGE: Further limits the rows within the partition by specifying start and end points within the partition. This is done by specifying a range of rows with respect to the current row either by logical association or physical association. 39 / 45
68 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. ORDER BY: Defines the logical order of the rows within each partition of the result set, i.e., it specifies the logical order in which the window function calculation is performed. ROW RANGE: Further limits the rows within the partition by specifying start and end points within the partition. This is done by specifying a range of rows with respect to the current row either by logical association or physical association. The ROWS clause limits the rows within a partition by specifying a fixed number of rows preceding or following the current row. 39 / 45
69 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. ORDER BY: Defines the logical order of the rows within each partition of the result set, i.e., it specifies the logical order in which the window function calculation is performed. ROW RANGE: Further limits the rows within the partition by specifying start and end points within the partition. This is done by specifying a range of rows with respect to the current row either by logical association or physical association. The ROWS clause limits the rows within a partition by specifying a fixed number of rows preceding or following the current row. The RANGE clause logically limits the rows within a partition by specifying a range of values with respect to the value in the current row. 39 / 45
70 OVER(): PARTITION BY: Divides the query result set into partitions. The window function is applied to each partition separately and computation restarts for each partition. ORDER BY: Defines the logical order of the rows within each partition of the result set, i.e., it specifies the logical order in which the window function calculation is performed. ROW RANGE: Further limits the rows within the partition by specifying start and end points within the partition. This is done by specifying a range of rows with respect to the current row either by logical association or physical association. The ROWS clause limits the rows within a partition by specifying a fixed number of rows preceding or following the current row. The RANGE clause logically limits the rows within a partition by specifying a range of values with respect to the value in the current row. Preceding and following rows are defined based on the ordering in the ORDER BY clause. 39 / 45
71 Ranking functions: 40 / 45
72 Ranking functions: RANK () OVER: 40 / 45
73 Ranking functions: RANK () OVER: Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. 40 / 45
74 Ranking functions: RANK () OVER: Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. DENSE RANK () OVER: 40 / 45
75 Ranking functions: RANK () OVER: Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. DENSE RANK () OVER: Returns the rank of rows within the partition of a result set, without any gaps in the ranking. The rank of a row is one plus the number of distinct ranks that come before the row in question. 40 / 45
76 Ranking functions: RANK () OVER: Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. DENSE RANK () OVER: Returns the rank of rows within the partition of a result set, without any gaps in the ranking. The rank of a row is one plus the number of distinct ranks that come before the row in question. NTILE (integer expression) OVER: 40 / 45
77 Ranking functions: RANK () OVER: Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. DENSE RANK () OVER: Returns the rank of rows within the partition of a result set, without any gaps in the ranking. The rank of a row is one plus the number of distinct ranks that come before the row in question. NTILE (integer expression) OVER: Distributes the rows in an ordered partition into a specified number of groups. The groups are numbered, starting at one. For each row, NTILE returns the number of the group to which the row belongs. 40 / 45
78 Ranking functions: RANK () OVER: Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. DENSE RANK () OVER: Returns the rank of rows within the partition of a result set, without any gaps in the ranking. The rank of a row is one plus the number of distinct ranks that come before the row in question. NTILE (integer expression) OVER: Distributes the rows in an ordered partition into a specified number of groups. The groups are numbered, starting at one. For each row, NTILE returns the number of the group to which the row belongs. ROW NUMBER () OVER: 40 / 45
79 Ranking functions: RANK () OVER: Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. DENSE RANK () OVER: Returns the rank of rows within the partition of a result set, without any gaps in the ranking. The rank of a row is one plus the number of distinct ranks that come before the row in question. NTILE (integer expression) OVER: Distributes the rows in an ordered partition into a specified number of groups. The groups are numbered, starting at one. For each row, NTILE returns the number of the group to which the row belongs. ROW NUMBER () OVER: Returns the sequential number of a row within a partition of a result set, starting at 1 for the first row in each partition. 40 / 45
80 Examples: Ranking of the students SELECT Student, Avg(Grade), RANK () OVER (ORDER BY Avg(Grade) DESC) FROM Students grades GROUP BY Student; To sort according to rank, we need to order the resulting relation: SELECT Student, Avg(Grade), RANK () OVER (ORDER BY Avg(Grade) DESC) AS rank of grades FROM Students grades GROUP BY Student ORDER BY rank of grades; 41 / 45
81 Examples: Ranking of students partitioned by instructors. SELECT Instructor Name, Student, Avg(Grade), RANK () OVER (PARTITION BY Instructor Name ORDER BY Avg(Grade) DESC) AS rank 1 FROM Students grades GROUP BY Student,Instructor Name ORDER BY Instructor Name, rank 1; Moving average of a student: SELECT Student, Academic year, AVG (grades) OVER (PARTITION BY Student ORDER BY Academic year DESC ROWS UNBOUNDED PRECEDING) FROM Students grades ORDER BY Student, Academic year; 42 / 45
82 Outline 1 Motivation 2 OLAP Servers 3 ROLAP 4 SQL 5 Summary 43 / 45
83 Summary OLAP Systems: Relational OLAP. SQL for analytical queries. 44 / 45
84 Bibliography J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, second edition, / 45
OLAP Systems and Multidimensional Expressions I
OLAP Systems and Multidimensional Expressions I Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master
OLAP Systems and Multidimensional Queries II
OLAP Systems and Multidimensional Queries II Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master
OLAP Systems and Multidimensional Expressions II
OLAP Systems and Multidimensional Expressions II Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master
DATA WAREHOUSING - OLAP
http://www.tutorialspoint.com/dwh/dwh_olap.htm DATA WAREHOUSING - OLAP Copyright tutorialspoint.com Online Analytical Processing Server OLAP is based on the multidimensional data model. It allows managers,
1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing
1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing 2. What is a Data warehouse a. A database application
Data Warehousing and Decision Support. Introduction. Three Complementary Trends. Chapter 23, Part A
Data Warehousing and Decision Support Chapter 23, Part A Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 1 Introduction Increasingly, organizations are analyzing current and historical
DATA WAREHOUSING AND OLAP TECHNOLOGY
DATA WAREHOUSING AND OLAP TECHNOLOGY Manya Sethi MCA Final Year Amity University, Uttar Pradesh Under Guidance of Ms. Shruti Nagpal Abstract DATA WAREHOUSING and Online Analytical Processing (OLAP) are
Data W a Ware r house house and and OLAP II Week 6 1
Data Warehouse and OLAP II Week 6 1 Team Homework Assignment #8 Using a data warehousing tool and a data set, play four OLAP operations (Roll up (drill up), Drill down (roll down), Slice and dice, Pivot
OLAP OLAP. Data Warehouse. OLAP Data Model: the Data Cube S e s s io n
OLAP OLAP On-Line Analytical Processing In contrast to on-line transaction processing (OLTP) Mostly ad hoc queries involving aggregation Response time rather than throughput is the main performance measure.
To increase performaces SQL has been extended in order to have some new operations avaiable. They are:
OLAP To increase performaces SQL has been extended in order to have some new operations avaiable. They are:. roll up: aggregates different events to reduce details used to describe them (looking at higher
CSE 544 Principles of Database Management Systems. Magdalena Balazinska Fall 2007 Lecture 16 - Data Warehousing
CSE 544 Principles of Database Management Systems Magdalena Balazinska Fall 2007 Lecture 16 - Data Warehousing Class Projects Class projects are going very well! Project presentations: 15 minutes On Wednesday
Learning Objectives. Definition of OLAP Data cubes OLAP operations MDX OLAP servers
OLAP Learning Objectives Definition of OLAP Data cubes OLAP operations MDX OLAP servers 2 What is OLAP? OLAP has two immediate consequences: online part requires the answers of queries to be fast, the
Anwendersoftware Anwendungssoftwares a. Data-Warehouse-, Data-Mining- and OLAP-Technologies. Online Analytic Processing
Anwendungssoftwares a Data-Warehouse-, Data-Mining- and OLAP-Technologies Online Analytic Processing Online Analytic Processing OLAP Online Analytic Processing Technologies and tools that support (ad-hoc)
Database Applications. Advanced Querying. Transaction Processing. Transaction Processing. Data Warehouse. Decision Support. Transaction processing
Database Applications Advanced Querying Transaction processing Online setting Supports day-to-day operation of business OLAP Data Warehousing Decision support Offline setting Strategic planning (statistics)
2074 : Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000
2074 : Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000 Introduction This course provides students with the knowledge and skills necessary to design, implement, and deploy OLAP
CHAPTER 4 Data Warehouse Architecture
CHAPTER 4 Data Warehouse Architecture 4.1 Data Warehouse Architecture 4.2 Three-tier data warehouse architecture 4.3 Types of OLAP servers: ROLAP versus MOLAP versus HOLAP 4.4 Further development of Data
Data Integration and ETL Process
Data Integration and ETL Process Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, second
New Approach of Computing Data Cubes in Data Warehousing
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 14 (2014), pp. 1411-1417 International Research Publications House http://www. irphouse.com New Approach of
Decision Support. Chapter 23. Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 1
Decision Support Chapter 23 Database Management Systems, 2 nd Edition. R. Ramakrishnan and J. Gehrke 1 Introduction Increasingly, organizations are analyzing current and historical data to identify useful
Building Data Cubes and Mining Them. Jelena Jovanovic Email: jeljov@fon.bg.ac.yu
Building Data Cubes and Mining Them Jelena Jovanovic Email: jeljov@fon.bg.ac.yu KDD Process KDD is an overall process of discovering useful knowledge from data. Data mining is a particular step in the
Multi-dimensional index structures Part I: motivation
Multi-dimensional index structures Part I: motivation 144 Motivation: Data Warehouse A definition A data warehouse is a repository of integrated enterprise data. A data warehouse is used specifically for
CSE 544 Principles of Database Management Systems. Magdalena Balazinska Winter 2009 Lecture 15 - Data Warehousing: Cubes
CSE 544 Principles of Database Management Systems Magdalena Balazinska Winter 2009 Lecture 15 - Data Warehousing: Cubes Final Exam Overview Open books and open notes No laptops and no other mobile devices
Overview. Data Warehousing and Decision Support. Introduction. Three Complementary Trends. Data Warehousing. An Example: The Store (e.g.
Overview Data Warehousing and Decision Support Chapter 25 Why data warehousing and decision support Data warehousing and the so called star schema MOLAP versus ROLAP OLAP, ROLLUP AND CUBE queries Design
M2074 - Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000 5 Day Course
Module 1: Introduction to Data Warehousing and OLAP Introducing Data Warehousing Defining OLAP Solutions Understanding Data Warehouse Design Understanding OLAP Models Applying OLAP Cubes At the end of
DATA CUBES E0 261. Jayant Haritsa Computer Science and Automation Indian Institute of Science. JAN 2014 Slide 1 DATA CUBES
E0 261 Jayant Haritsa Computer Science and Automation Indian Institute of Science JAN 2014 Slide 1 Introduction Increasingly, organizations are analyzing historical data to identify useful patterns and
LEARNING SOLUTIONS website milner.com/learning email training@milner.com phone 800 875 5042
Course 20467A: Designing Business Intelligence Solutions with Microsoft SQL Server 2012 Length: 5 Days Published: December 21, 2012 Language(s): English Audience(s): IT Professionals Overview Level: 300
Week 13: Data Warehousing. Warehousing
1 Week 13: Data Warehousing Warehousing Growing industry: $8 billion in 1998 Range from desktop to huge: Walmart: 900-CPU, 2,700 disk, 23TB Teradata system Lots of buzzwords, hype slice & dice, rollup,
Unit -3. Learning Objective. Demand for Online analytical processing Major features and functions OLAP models and implementation considerations
Unit -3 Learning Objective Demand for Online analytical processing Major features and functions OLAP models and implementation considerations Demand of On Line Analytical Processing Need for multidimensional
Data Warehousing. Read chapter 13 of Riguzzi et al Sistemi Informativi. Slides derived from those by Hector Garcia-Molina
Data Warehousing Read chapter 13 of Riguzzi et al Sistemi Informativi Slides derived from those by Hector Garcia-Molina What is a Warehouse? Collection of diverse data subject oriented aimed at executive,
Outline. Data Warehousing. What is a Warehouse? What is a Warehouse?
Outline Data Warehousing What is a data warehouse? Why a warehouse? Models & operations Implementing a warehouse 2 What is a Warehouse? Collection of diverse data subject oriented aimed at executive, decision
Mario Guarracino. Data warehousing
Data warehousing Introduction Since the mid-nineties, it became clear that the databases for analysis and business intelligence need to be separate from operational. In this lecture we will review the
Data Warehouse Logical Design. Letizia Tanca Politecnico di Milano (with the kind support of Rosalba Rossato)
Data Warehouse Logical Design Letizia Tanca Politecnico di Milano (with the kind support of Rosalba Rossato) Data Mart logical models MOLAP (Multidimensional On-Line Analytical Processing) stores data
A Technical Review on On-Line Analytical Processing (OLAP)
A Technical Review on On-Line Analytical Processing (OLAP) K. Jayapriya 1., E. Girija 2,III-M.C.A., R.Uma. 3,M.C.A.,M.Phil., Department of computer applications, Assit.Prof,Dept of M.C.A, Dhanalakshmi
Lecture Data Warehouse Systems
Lecture Data Warehouse Systems Eva Zangerle SS 2013 PART A: Architecture Chapter 1: Motivation and Definitions Motivation Goal: to build an operational general view on a company to support decisions in
OLAP and Data Warehousing! Introduction!
The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still
Week 3 lecture slides
Week 3 lecture slides Topics Data Warehouses Online Analytical Processing Introduction to Data Cubes Textbook reference: Chapter 3 Data Warehouses A data warehouse is a collection of data specifically
Monitoring Genebanks using Datamarts based in an Open Source Tool
Monitoring Genebanks using Datamarts based in an Open Source Tool April 10 th, 2008 Edwin Rojas Research Informatics Unit (RIU) International Potato Center (CIP) GPG2 Workshop 2008 Datamarts Motivation
OLAP. Business Intelligence OLAP definition & application Multidimensional data representation
OLAP Business Intelligence OLAP definition & application Multidimensional data representation 1 Business Intelligence Accompanying the growth in data warehousing is an ever-increasing demand by users for
Copyright 2007 Ramez Elmasri and Shamkant B. Navathe. Slide 29-1
Slide 29-1 Chapter 29 Overview of Data Warehousing and OLAP Chapter 29 Outline Purpose of Data Warehousing Introduction, Definitions, and Terminology Comparison with Traditional Databases Characteristics
OLAP & DATA MINING CS561-SPRING 2012 WPI, MOHAMED ELTABAKH
OLAP & DATA MINING CS561-SPRING 2012 WPI, MOHAMED ELTABAKH 1 Online Analytic Processing OLAP 2 OLAP OLAP: Online Analytic Processing OLAP queries are complex queries that Touch large amounts of data Discover
OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP
Data Warehousing and End-User Access Tools OLAP and Data Mining Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities. Key
Review. Data Warehousing. Today. Star schema. Star join indexes. Dimension hierarchies
Review Data Warehousing CPS 216 Advanced Database Systems Data warehousing: integrating data for OLAP OLAP versus OLTP Warehousing versus mediation Warehouse maintenance Warehouse data as materialized
CS2032 Data warehousing and Data Mining Unit II Page 1
UNIT II BUSINESS ANALYSIS Reporting Query tools and Applications The data warehouse is accessed using an end-user query and reporting tool from Business Objects. Business Objects provides several tools
Data Warehousing and OLAP
1 Data Warehousing and OLAP Hector Garcia-Molina Stanford University Warehousing Growing industry: $8 billion in 1998 Range from desktop to huge: Walmart: 900-CPU, 2,700 disk, 23TB Teradata system Lots
Data Warehouse design
Data Warehouse design Design of Enterprise Systems University of Pavia 21/11/2013-1- Data Warehouse design DATA PRESENTATION - 2- BI Reporting Success Factors BI platform success factors include: Performance
Basics of Dimensional Modeling
Basics of Dimensional Modeling Data warehouse and OLAP tools are based on a dimensional data model. A dimensional model is based on dimensions, facts, cubes, and schemas such as star and snowflake. Dimensional
Optimizing Your Data Warehouse Design for Superior Performance
Optimizing Your Data Warehouse Design for Superior Performance Lester Knutsen, President and Principal Database Consultant Advanced DataTools Corporation Session 2100A The Problem The database is too complex
Overview of Data Warehousing and OLAP
Overview of Data Warehousing and OLAP Chapter 28 March 24, 2008 ADBS: DW 1 Chapter Outline What is a data warehouse (DW) Conceptual structure of DW Why separate DW Data modeling for DW Online Analytical
UNIT-3 OLAP in Data Warehouse
UNIT-3 OLAP in Data Warehouse Bharati Vidyapeeth s Institute of Computer Applications and Management, New Delhi-63, by Dr.Deepali Kamthania U2.1 OLAP Demand for Online analytical processing Major features
Data Mining and Data Warehousing Henryk Maciejewski Data Warehousing and OLAP
Data Mining and Data Warehousing Henryk Maciejewski Data Warehousing and OLAP Part II Data Warehousing Contents OLAP Approach to Data Analysis Database for OLAP = Data Warehouse Logical model Physical
MS 20467: Designing Business Intelligence Solutions with Microsoft SQL Server 2012
MS 20467: Designing Business Intelligence Solutions with Microsoft SQL Server 2012 Description: This five-day instructor-led course teaches students how to design and implement a BI infrastructure. The
Web Log Data Sparsity Analysis and Performance Evaluation for OLAP
Web Log Data Sparsity Analysis and Performance Evaluation for OLAP Ji-Hyun Kim, Hwan-Seung Yong Department of Computer Science and Engineering Ewha Womans University 11-1 Daehyun-dong, Seodaemun-gu, Seoul,
Main Memory & Near Main Memory OLAP Databases. Wo Shun Luk Professor of Computing Science Simon Fraser University
Main Memory & Near Main Memory OLAP Databases Wo Shun Luk Professor of Computing Science Simon Fraser University 1 Outline What is OLAP DB? How does it work? MOLAP, ROLAP Near Main Memory DB Partial Pre
Business Intelligence, Data warehousing Concept and artifacts
Business Intelligence, Data warehousing Concept and artifacts Data Warehousing is the process of constructing and using the data warehouse. The data warehouse is constructed by integrating the data from
CS54100: Database Systems
CS54100: Database Systems Date Warehousing: Current, Future? 20 April 2012 Prof. Chris Clifton Data Warehousing: Goals OLAP vs OLTP On Line Analytical Processing (vs. Transaction) Optimize for read, not
Data Warehouse: Introduction
Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of base and data mining group,
The Design and the Implementation of an HEALTH CARE STATISTICS DATA WAREHOUSE Dr. Sreèko Natek, assistant professor, Nova Vizija, srecko@vizija.
The Design and the Implementation of an HEALTH CARE STATISTICS DATA WAREHOUSE Dr. Sreèko Natek, assistant professor, Nova Vizija, srecko@vizija.si ABSTRACT Health Care Statistics on a state level is a
When to consider OLAP?
When to consider OLAP? Author: Prakash Kewalramani Organization: Evaltech, Inc. Evaltech Research Group, Data Warehousing Practice. Date: 03/10/08 Email: erg@evaltech.com Abstract: Do you need an OLAP
What is OLAP - On-line analytical processing
What is OLAP - On-line analytical processing Vladimir Estivill-Castro School of Computing and Information Technology With contributions for J. Han 1 Introduction When a company has received/accumulated
Designing a Dimensional Model
Designing a Dimensional Model Erik Veerman Atlanta MDF member SQL Server MVP, Microsoft MCT Mentor, Solid Quality Learning Definitions Data Warehousing A subject-oriented, integrated, time-variant, and
A Design and implementation of a data warehouse for research administration universities
A Design and implementation of a data warehouse for research administration universities André Flory 1, Pierre Soupirot 2, and Anne Tchounikine 3 1 CRI : Centre de Ressources Informatiques INSA de Lyon
The Art of Designing HOLAP Databases Mark Moorman, SAS Institute Inc., Cary NC
Paper 139 The Art of Designing HOLAP Databases Mark Moorman, SAS Institute Inc., Cary NC ABSTRACT While OLAP applications offer users fast access to information across business dimensions, it can also
Designing Business Intelligence Solutions with Microsoft SQL Server 2012 Course 20467A; 5 Days
Lincoln Land Community College Capital City Training Center 130 West Mason Springfield, IL 62702 217-782-7436 www.llcc.edu/cctc Designing Business Intelligence Solutions with Microsoft SQL Server 2012
Data Warehousing: Data Models and OLAP operations. By Kishore Jaladi kishorejaladi@yahoo.com
Data Warehousing: Data Models and OLAP operations By Kishore Jaladi kishorejaladi@yahoo.com Topics Covered 1. Understanding the term Data Warehousing 2. Three-tier Decision Support Systems 3. Approaches
Chapter 3, Data Warehouse and OLAP Operations
CSI 4352, Introduction to Data Mining Chapter 3, Data Warehouse and OLAP Operations Young-Rae Cho Associate Professor Department of Computer Science Baylor University CSI 4352, Introduction to Data Mining
II. OLAP(ONLINE ANALYTICAL PROCESSING)
Association Rule Mining Method On OLAP Cube Jigna J. Jadav*, Mahesh Panchal** *( PG-CSE Student, Department of Computer Engineering, Kalol Institute of Technology & Research Centre, Gujarat, India) **
Turkish Journal of Engineering, Science and Technology
Turkish Journal of Engineering, Science and Technology 03 (2014) 106-110 Turkish Journal of Engineering, Science and Technology journal homepage: www.tujest.com Integrating Data Warehouse with OLAP Server
Data Warehousing. Paper 133-25
Paper 133-25 The Power of Hybrid OLAP in a Multidimensional World Ann Weinberger, SAS Institute Inc., Cary, NC Matthias Ender, SAS Institute Inc., Cary, NC ABSTRACT Version 8 of the SAS System brings powerful
Hybrid OLAP, An Introduction
Hybrid OLAP, An Introduction Richard Doherty SAS Institute European HQ Agenda Hybrid OLAP overview Building your data model Architectural decisions Metadata creation Report definition Hybrid OLAP overview
Data Warehousing & OLAP
Data Warehousing & OLAP Data Mining: Concepts and Techniques Chapter 3 Jiawei Han and An Introduction to Database Systems C.J.Date, Eighth Eddition, Addidon Wesley, 4 1 What is Data Warehousing? What is
Business Intelligence & Product Analytics
2010 International Conference Business Intelligence & Product Analytics Rob McAveney www. 300 Brickstone Square Suite 904 Andover, MA 01810 [978] 691 8900 www. Copyright 2010 Aras All Rights Reserved.
University of Gaziantep, Department of Business Administration
University of Gaziantep, Department of Business Administration The extensive use of information technology enables organizations to collect huge amounts of data about almost every aspect of their businesses.
Data Integration and ETL Process
Data Integration and ETL Process Krzysztof Dembczyński Institute of Computing Science Laboratory of Intelligent Decision Support Systems Politechnika Poznańska (Poznań University of Technology) Software
Part 22. Data Warehousing
Part 22 Data Warehousing The Decision Support System (DSS) Tools to assist decision-making Used at all levels in the organization Sometimes focused on a single area Sometimes focused on a single problem
Data Warehousing Systems: Foundations and Architectures
Data Warehousing Systems: Foundations and Architectures Il-Yeol Song Drexel University, http://www.ischool.drexel.edu/faculty/song/ SYNONYMS None DEFINITION A data warehouse (DW) is an integrated repository
BUILDING BLOCKS OF DATAWAREHOUSE. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT
BUILDING BLOCKS OF DATAWAREHOUSE G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT 1 Data Warehouse Subject Oriented Organized around major subjects, such as customer, product, sales. Focusing on
Data Warehousing and OLAP Technology
Data Warehousing and OLAP Technology 1. Objectives... 3 2. What is Data Warehouse?... 4 2.1. Definitions... 4 2.2. Data Warehouse Subject-Oriented... 5 2.3. Data Warehouse Integrated... 5 2.4. Data Warehouse
SAS BI Course Content; Introduction to DWH / BI Concepts
SAS BI Course Content; Introduction to DWH / BI Concepts SAS Web Report Studio 4.2 SAS EG 4.2 SAS Information Delivery Portal 4.2 SAS Data Integration Studio 4.2 SAS BI Dashboard 4.2 SAS Management Console
Data warehousing. Han, J. and M. Kamber. Data Mining: Concepts and Techniques. 2001. Morgan Kaufmann.
Data warehousing Han, J. and M. Kamber. Data Mining: Concepts and Techniques. 2001. Morgan Kaufmann. KDD process Application Pattern Evaluation Data Mining Task-relevant Data Data Warehouse Selection Data
LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES
LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES MUHAMMAD KHALEEL (0912125) SZABIST KARACHI CAMPUS Abstract. Data warehouse and online analytical processing (OLAP) both are core component for decision
IST722 Data Warehousing
IST722 Data Warehousing Components of the Data Warehouse Michael A. Fudge, Jr. Recall: Inmon s CIF The CIF is a reference architecture Understanding the Diagram The CIF is a reference architecture CIF
DATA WAREHOUSE E KNOWLEDGE DISCOVERY
DATA WAREHOUSE E KNOWLEDGE DISCOVERY Prof. Fabio A. Schreiber Dipartimento di Elettronica e Informazione Politecnico di Milano DATA WAREHOUSE (DW) A TECHNIQUE FOR CORRECTLY ASSEMBLING AND MANAGING DATA
Data Warehousing OLAP
Data Warehousing OLAP References Wei Wang. A Brief MDX Tutorial Using Mondrian. School of Computer Science & Engineering, University of New South Wales. Toon Calders. Querying OLAP Cubes. Wolf-Tilo Balke,
Data Testing on Business Intelligence & Data Warehouse Projects
Data Testing on Business Intelligence & Data Warehouse Projects Karen N. Johnson 1 Construct of a Data Warehouse A brief look at core components of a warehouse. From the left, these three boxes represent
Apache Kylin Introduction Dec 8, 2014 @ApacheKylin
Apache Kylin Introduction Dec 8, 2014 @ApacheKylin Luke Han Sr. Product Manager lukhan@ebay.com @lukehq Yang Li Architect & Tech Leader yangli9@ebay.com Agenda What s Apache Kylin? Tech Highlights Performance
ROLAP with Column Store Index Deep Dive. Alexei Khalyako SQL CAT Program Manager alexeik@microsoft.com
ROLAP with Column Store Index Deep Dive Alexei Khalyako SQL CAT Program Manager alexeik@microsoft.com What are we doing now? 1000 of concurrent users 4TB Cube High speed loading Current design 14 th Jan
Republic Polytechnic School of Information and Communications Technology C355 Business Intelligence. Module Curriculum
Republic Polytechnic School of Information and Communications Technology C355 Business Intelligence Module Curriculum This document addresses the content related abilities, with reference to the module.
Implementing Data Models and Reports with Microsoft SQL Server 2012 MOC 10778
Implementing Data Models and Reports with Microsoft SQL Server 2012 MOC 10778 Course Outline Module 1: Introduction to Business Intelligence and Data Modeling This module provides an introduction to Business
BUSINESS ANALYTICS AND DATA VISUALIZATION. ITM-761 Business Intelligence ดร. สล ล บ ญพราหมณ
1 BUSINESS ANALYTICS AND DATA VISUALIZATION ITM-761 Business Intelligence ดร. สล ล บ ญพราหมณ 2 การท าความด น น ยากและเห นผลช า แต ก จ าเป นต องท า เพราะหาไม ความช วซ งท าได ง ายจะเข ามาแทนท และจะพอกพ นข
Course 6234A: Implementing and Maintaining Microsoft SQL Server 2008 Analysis Services
Course 6234A: Implementing and Maintaining Microsoft SQL Server 2008 Analysis Services Length: Delivery Method: 3 Days Instructor-led (classroom) About this Course Elements of this syllabus are subject
Data Mining for Knowledge Management. Data Warehouses
1 Data Mining for Knowledge Management Data Warehouses Themis Palpanas University of Trento http://disi.unitn.eu/~themis Data Mining for Knowledge Management 1 Thanks for slides to: Jiawei Han Niarcas
SQL SERVER TRAINING CURRICULUM
SQL SERVER TRAINING CURRICULUM Complete SQL Server 2000/2005 for Developers Management and Administration Overview Creating databases and transaction logs Managing the file system Server and database configuration
Using distributed technologies to analyze Big Data
Using distributed technologies to analyze Big Data Abhijit Sharma Innovation Lab BMC Software 1 Data Explosion in Data Center Performance / Time Series Data Incoming data rates ~Millions of data points/
Delivering Business Intelligence With Microsoft SQL Server 2005 or 2008 HDT922 Five Days
or 2008 Five Days Prerequisites Students should have experience with any relational database management system as well as experience with data warehouses and star schemas. It would be helpful if students
Introduction to Data Warehousing. Ms Swapnil Shrivastava swapnil@konark.ncst.ernet.in
Introduction to Data Warehousing Ms Swapnil Shrivastava swapnil@konark.ncst.ernet.in Necessity is the mother of invention Why Data Warehouse? Scenario 1 ABC Pvt Ltd is a company with branches at Mumbai,
SQL Server 2012 Business Intelligence Boot Camp
SQL Server 2012 Business Intelligence Boot Camp Length: 5 Days Technology: Microsoft SQL Server 2012 Delivery Method: Instructor-led (classroom) About this Course Data warehousing is a solution organizations
Bussiness Intelligence and Data Warehouse. Tomas Bartos CIS 764, Kansas State University
Bussiness Intelligence and Data Warehouse Schedule Bussiness Intelligence (BI) BI tools Oracle vs. Microsoft Data warehouse History Tools Oracle vs. Others Discussion Business Intelligence (BI) Products
ORACLE OLAP. Oracle OLAP is embedded in the Oracle Database kernel and runs in the same database process
ORACLE OLAP KEY FEATURES AND BENEFITS FAST ANSWERS TO TOUGH QUESTIONS EASILY KEY FEATURES & BENEFITS World class analytic engine Superior query performance Simple SQL access to advanced analytics Enhanced
Data W a Ware r house house and and OLAP Week 5 1
Data Warehouse and OLAP Week 5 1 Midterm I Friday, March 4 Scope Homework assignments 1 4 Open book Team Homework Assignment #7 Read pp. 121 139, 146 150 of the text book. Do Examples 3.8, 3.10 and Exercise
ETL TESTING TRAINING
ETL TESTING TRAINING DURATION 35hrs AVAILABLE BATCHES WEEKDAYS (6.30AM TO 7.30AM) & WEEKENDS (6.30pm TO 8pm) MODE OF TRAINING AVAILABLE ONLINE INSTRUCTOR LED CLASSROOM TRAINING (MARATHAHALLI, BANGALORE)