Big Data 13. Data Warehousing
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1 Ghislain Fourny Big Data 13. Data Warehousing fotoreactor / 123RF Stock Photo
2 The road to analytics Aurelio Scetta / 123RF Stock Photo
3 Another history of data management (T. Hofmann) 1970s 2000s Age of Transactions
4 Another history of data management (T. Hofmann) 1970s 2000s Age of Transactions Age of Business Intelligence
5 Another history of data management (T. Hofmann) 1970s 2000s Age of Transactions Age of Business Intelligence 2000s - Age of Big Data
6 Paradigms OLTP
7 Paradigms vs. OLTP OLAP
8 OnLine Transaction Processing Consistent and Reliable Record-Keeping
9 OnLine Transaction Processing Transactions and results on small portions of data
10 OnLine Transaction Processing Lots of transactions on small portions of data
11 OnLine Transaction Processing Normalized Data
12 OnLine Analytical Processing Data-based Decision Support arturaliev / 123RF Stock Photo
13 OLAP is Big Large portions of the data
14 OLAP is Big Large portions of the data Possibly many joins
15 OLAP is Big Large portions of the data Possibly many joins Few long heavy queries
16 OLAP Examples Web analytics Sales analytics Management support Statistical analysis (census) Scientific databases (e.g., bio-informatics)
17 OLTP vs. OLAP OLTP Detailed Individual Records
18 OLTP vs. OLAP Detailed Individual Records vs. Historical Summarized Consolidated Data Aurelio Scetta / 123RF Stock Photo OLTP OLAP
19 OLTP vs. OLAP Lots of writes OLTP
20 OLTP vs. OLAP vs. Lots of writes OLTP Lots of reads OLAP
21 OLTP vs. OLAP Small sets of records OLTP
22 OLTP vs. OLAP Small sets of records vs. Analysis over big chunks OLTP OLAP
23 OLTP vs. OLAP fully interactive (< 1s) OLTP
24 OLTP vs. OLAP fully interactive (< 1s) vs. Slow interactive OLTP OLAP
25 OLTP vs. OLAP Redundancy Redundancy Redundancy Consistency OLTP OLAP
26 OLAP Aurelio Scetta / 123RF Stock Photo
27 A data warehouse... is a
28 A data warehouse... is a subject-oriented
29 A data warehouse... is a subject-oriented integrated
30 A data warehouse... is a subject-oriented integrated time-variant
31 A data warehouse... is a subject-oriented integrated time-variant nonvolatile
32 A data warehouse... is a subject-oriented integrated time-variant nonvolatile collection of data in support of management's decision-making process
33 Subject-oriented customers sales products events
34 Integrated
35 Integrated
36 Time-variant Time in data warehouses is paramount (not so in OLTP systems)
37 Time-variant Y-9 Y-8 Y-7 Y-6 Y-5 Y-4 Y-3 Y-2 Y Often past 5-10 years
38 Non-volatile Load. Milosh Kojadinovich / 123RF Stock Photo
39 Non-volatile Load. Access. Milosh Kojadinovich / 123RF Stock Photo
40 Non-volatile Load. Access. Period. Milosh Kojadinovich / 123RF Stock Photo
41 Non-volatile Load. Access. Period. no updates Milosh Kojadinovich / 123RF Stock Photo
42 Architecture ERP CRM OLTP Files
43 Architecture ERP CRM ETL OLTP Files
44 Architecture ERP Analyze CRM ETL Report OLTP Files Mine
45 OLAP: Redundancy
46 OLAP: Redundancy Materialized views (denormalized)
47 1st Normal Form (tabular) The Key
48 2nd Normal Form (not joined) The Whole Key
49 3rd Normal Form Nothing But The Key
50 Why materialize? Operational data sources are too heterogeneous
51 OLAP: Special-purpose indices
52 OLAP: Derived data
53 Querying OLAP 6 Slow interactive Category 1 Category 2 Category 3 Category 4 Series 1 Series 2 Series s
54 Querying OLAP 6 Slow interactive Category 1 Category 2 Category 3 Category 4 Series 1 Series 2 Series 3 vs. Continuous monitoring/tracking 1-10s hours
55 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated)
56 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support
57 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views
58 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs
59 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs Queries Simple, small results Complex and aggregating
60 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs Queries Simple, small results Complex and aggregating Design Many normalized tables Few denormalized cubes
61 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs Queries Simple, small results Complex and aggregating Design Many normalized tables Few denormalized cubes Precision ACID Sampling, confidence intervals
62 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs Queries Simple, small results Complex and aggregating Design Many normalized tables Few denormalized cubes Precision ACID Sampling, confidence intervals Freshness Serializability Reproducibility
63 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs Queries Simple, small results Complex and aggregating Design Many normalized tables Few denormalized cubes Precision ACID Sampling, confidence intervals Freshness Serializability Reproducibility Speed Very fast Often slow
64 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs Queries Simple, small results Complex and aggregating Design Many normalized tables Few denormalized cubes Precision ACID Sampling, confidence intervals Freshness Serializability Reproducibility Speed Very fast Often slow Optimization Inter-query Intra-query
65 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs Queries Simple, small results Complex and aggregating Design Many normalized tables Few denormalized cubes Precision ACID Sampling, confidence intervals Freshness Serializability Reproducibility Speed Very fast Often slow Optimization Inter-query Intra-query Space Small, archiving old data Large, less space efficient
66 Summary of differences OLTP OLAP Source Original (operational) Derived (consolidated) Purpose Business tasks Decision support Interface Snapshot Multidimensional views Writing short and fast, by end user period refreshes, by batch jobs Queries Simple, small results Complex and aggregating Design Many normalized tables Few denormalized cubes Precision ACID Sampling, confidence intervals Freshness Serializability Reproducibility Speed Very fast Often slow Optimization Inter-query Intra-query Space Small, archiving old data Large, less space efficient Backup Very important Re-ETL
67 Data Model
68 Data Cubes Data is stored in multidimensional hypercubes
69 Data Cubes Year
70 Data Cubes Country
71 Data Cubes Product
72 Fact 2016 CH Server
73 Dimensions Which currency? Where What? Who? When? etc.
74 Fact table Where? Germany 2016 Peter 1,000$ Germany 2015 Mary 15,000$ Switzerland 2016 Mary 1,500$ Switzerland 2015 Peter 3,000$ Australia 2015 Peter 6,000$ China 2015 Mary 1,000$
75 Aggregation Where? Germany 2016 Peter 1,000$ Germany 2015 Mary 15,000$ Switzerland 2016 Mary 1,500$ Switzerland 2015 Peter 3,000$ Australia 2015 Peter 6,000$ China 2015 Mary 1,000$
76 Aggregation
77 Aggregation Where? Germany 2016 Peter 1,000$ Germany 2015 Mary 15,000$ Switzerland 2016 Mary 1,500$ Switzerland 2015 Peter 3,000$ Australia 2015 Peter 6,000$ China 2015 Mary 1,000$
78 Aggregation 2016 Peter 1,000$ 2015 Mary 16,000$ 2016 Mary 1,500$ 2015 Peter 9,000$
79 Slicing
80 Slicers and Dicers Slicers Dicers
81 Slicers and Dicers Usually between 1 and 3 dicers, often 2 Slicers Dicers
82 Slicers and Dicers Slicers Servers World USD
83 Slicers and Dicers Dicers Slicers Servers World USD Peter 1,000,000$ 1,500,000$ 1,400,000$ Mary 2,000,000$ 2,300,000$ 2,200,000$
84 Products: the big three Essbase Cognos Analysis Services
85 ETLing
86 OLAP: Derived data
87 OLAP: Derived data ETL
88 ETL Extract Transform Load
89 Extract Triggers Incremental updates
90 Extract Triggers Gateways Incremental updates
91 Extract Triggers Gateways Incremental updates Log extraction
92 Transform Derivation
93 Transform Herr Derivation Mister Value transformation
94 Transform Herr Derivation Mister Value transformation Cleaning
95 Transform Herr Derivation Mister Value transformation Cleaning Filter, split, merge, join
96 Load Integrity constraints
97 Load Integrity constraints Sorting
98 Load Integrity constraints Sorting Build indices
99 Load Integrity constraints Sorting Build indices Partition
100 Considerations When?
101 Considerations When? Granularity
102 Considerations When? Granularity Infrastructure
103 Implementation
104 Two flavors of OLAP ROLAP
105 Two flavors of OLAP ROLAP MOLAP
106 Fact table (ROLAP) Dim1 Dim2 Dim3 Dim4 Dim5 Value
107 Star Schema Dim1 Dim2 Dim3 Dim4 Dim5 Value
108 Snow-flake schema Dim1 Dim2 Dim3 Dim4 Dim5 Value Normalize More
109 Querying
110 Querying cubes Tables: SQL
111 Querying cubes Tables: SQL Cubes: MDX
112 Sample Cube (ROLAP) Sales Date Product Customer Quantity iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary 3
113 Satellite tables Time Date Year Quarter Customer Name Country Phone Peter UK Mary Switzerland Product Name Brand Price iphone X Apple 1000 Samsung Galaxy S9 Samsung 800
114 Querying with SQL SELECT * FROM Sales s
115 Querying with SQL SELECT * FROM Sales s Sales Date Product Customer Quantity iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary 3
116 Joining with satellite tables SELECT t.year, s.product, s.customer, s.quantity FROM Sales s, Time t WHERE s.date = t.date
117 Joining with satellite tables SELECT t.year, s.product, s.customer, s.quantity FROM Sales s, Time t WHERE s.date = t.date Year Product Customer Quantity 2017 iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary 3
118 Slicing SELECT t.year, s.product, s.customer, s.quantity FROM Sales s, Time t WHERE s.date = t.date AND t.year = 2017
119 Slicing SELECT t.year, s.product, s.customer, s.quantity FROM Sales s, Time t WHERE s.date = t.date AND t.year = 2017 Year Product Customer Quantity 2017 iphone X Peter Samsung Galaxy S9 Peter iphone X Mary Samsung Galaxy S9 Mary 3
120 Slicing SELECT t.year, s.product, s.customer, s.quantity FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name AND t.year = 2017 AND p.brand = 'Samsung'
121 Slicing SELECT t.year, s.product, s.customer, s.quantity FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name AND t.year = 2017 AND p.brand = 'Samsung' Year Product Customer Quantity 2017 Samsung Galaxy S9 Peter Samsung Galaxy S9 Mary 3
122 Aggregating SELECT t.year, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name AND p.brand = 'Samsung' GROUP BY t.year
123 Aggregating SELECT t.year, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name AND p.brand = 'Samsung' GROUP BY t.year Date Quantity
124 Aggregating (Rolling up) SELECT SUM(s.Quantity) FROM Sales s
125 Aggregating (Rolling up) SELECT SUM(s.Quantity) FROM Sales s Quantity 34
126 Aggregating (Drilling down) SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand
127 Aggregating (Drilling down) SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand Sales Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung 4
128 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand SELECT t.year, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year SELECT SUM(s.Quantity) FROM Sales s
129 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand SELECT t.year, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year Roll up SELECT SUM(s.Quantity) FROM Sales s
130 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung 4 SELECT t.year, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year SELECT SUM(s.Quantity) FROM Sales s
131 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand SELECT t.year, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung 4 Year Quantity SELECT SUM(s.Quantity) FROM Sales s
132 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand SELECT t.year, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year SELECT SUM(s.Quantity) FROM Sales s Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung 4 Year Quantity Quantity 34
133 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung 4 SELECT t.year, null, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year SELECT null, null, SUM(s.Quantity) FROM Sales s
134 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand SELECT t.year, null, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year SELECT null, null, SUM(s.Quantity) FROM Sales s Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung 4 Year Quantity 2017 NULL NULL NULL 9 Quantity NULL NULL 34
135 Materializing all aggregates (SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand) UNION (SELECT t.year, null, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year) UNION (SELECT null, null, SUM(s.Quantity) FROM Sales s)
136 Materializing all aggregates (SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand) UNION (SELECT t.year, null, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year) UNION (SELECT null, null, SUM(s.Quantity) FROM Sales s) Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung NULL NULL NULL 9 NULL NULL 34
137 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY GROUPING SETS ( (t.year, p.brand), (t.year), () ) Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung NULL NULL NULL 9 NULL NULL 34
138 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY ROLLUP (t.year, p.brand) Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung NULL NULL NULL 9 NULL NULL 34
139 Materializing all aggregates SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand SELECT t.year, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year SELECT p.brand, SUM(s.Quantity) FROM Sales s, Product p WHERE s.product = p.name GROUP BY p.brand SELECT SUM(s.Quantity) FROM Sales s
140 Materializing all aggregates (SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand) UNION (SELECT t.year, null, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year) UNION (SELECT null, p.brand, SUM(s.Quantity) FROM Sales s, Product p WHERE s.product = p.name GROUP BY p.brand) UNION (SELECT null, null, SUM(s.Quantity) FROM Sales s)
141 Materializing all aggregates (SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY t.year, p.brand) UNION (SELECT t.year, null, SUM(s.Quantity) FROM Sales s, Time t WHERE s.date = t.date GROUP BY t.year) UNION (SELECT null, p.brand, SUM(s.Quantity) FROM Sales s, Product p WHERE s.product = p.name GROUP BY p.brand) UNION (SELECT null, null, SUM(s.Quantity) FROM Sales s) Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung NULL NULL NULL 9 NULL Apple 20 NULL Samsung 14 NULL NULL 34
142 Materializing all aggregates (SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY GROUPING SETS ( (t.year, p.brand), (t.year), (p.brand), () ) Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung NULL NULL NULL 9 NULL Apple 20 NULL Samsung 14 NULL NULL 34
143 Materializing all aggregates (SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY CUBE (t.year, p.brand) Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung NULL NULL NULL 9 NULL Apple 20 NULL Samsung 14 NULL NULL 34
144 Cross tabulation Year Brand Apple Samsung Total
145 Cross tabulation (Slicer: Peter only) Year Brand Apple Samsung Total
146 Cross tabulation (Slicer: 2017) Customer Brand Apple Samsung Peter Mary Total
147 MDX stands for... Multi-Dimensional expressions
148 Measures Amount of licenses Revenues Taxes paid...
149 Dimensions Quarter Salesperson Product Country
150 In short... A cube is a list of dimensions indexing a list of measures
151 Hierarchies Dimension values are organized in hierarchies. [Location] [Geo] [Economy] i.e., slice and aggregate by geographic region, etc i.e., slice and aggregate by economic partnership, etc
152 Members Members correspond to levels in a hierarchy. [Ocenia] [Africa] [Geo] [Europe] [Asia] [America] [Switzerland] [China] [Canada] [ZH] [India] [USA] [BE]... [Brazil] [Germany]......
153 Identifying a member [Location].[Geo].[Europe].[Switzerland].[ZH].[Zurich]
154 Tuples A list of members ([Location].[Geo].[Europe].[Switzerland].[ZH].[Zurich], [Salesmen].[People].[John], [Time].[Year].[2016].[Q4]) Associated with a dimensionality (list of hierarchies) ([Location].[Geo], [Salesmen].[People] [Time].[Year])
155 Sets A set of tuples with same dimensionality { ([Location].[Geo].[Europe].[Switzerland].[ZH].[Zurich], [Salesmen].[People].[John], [Time].[Year].[2016].[Q4]), ([Location].[Geo].[Europe].[Switzerland].[BE].[Bärn], [Salesmen].[People].[Mary], [Time].[Year].[2016].[Q4]), ([Location].[Geo].[Europe].[Germany].[Berlin], [Salesmen].[People].[John], [Time].[Year].[2016].[Q3]) }
156 MDX statements: dicing SELECT [Measures].Members ON COLUMNS, [Location].[Geo].Members ON ROWS FROM [Sales]
157 MDX statements: slicing SELECT [Measures].Members ON COLUMNS, [Location].[Geo].Members ON ROWS FROM [Sales] WHERE [Products].[Line].[Laptops].[MBP]
158 Syntax
159 XBRL Architecture Linkbase (.xml) Schema (.xsd) Instance (.xml) Discoverable Taxonomy Set
160 Technologies XML XML Schema XML Names XML Link
161 Fact Dimension Value What? Assets Who? Coca Cola When? Dec 31, 2011 Of what? USD <us-gaap:assets contextref="fi2012q4" decimals="-6" id="fact fd4d06e63b4f8f6874c6e5be74" unitref="usd"> </us-gaap:assets>
162 Context <xbrli:context id="fi2011q4"> <xbrli:entity> <xbrli:identifier scheme=" </xbrli:identifier> </xbrli:entity> <xbrli:period> <xbrli:instant> </xbrli:instant> </xbrli:period> </xbrli:context> December
163 Unit <xbrli:unit id="usd"> <xbrli:measure>iso4217:usd</xbrli:measure> </xbrli:unit>
164 Concept (XML Schema) <xs:element id='us-gaap_assets' name='assets' nillable='true' substitutiongroup='xbrli:item' type='xbrli:monetaryitemtype' xbrli:balance='debit' xbrli:periodtype='instant' />
165 Graphs
166 DAGs
167 Trees
168 Node: locator <loc xlink:href=" xlink:label="loc_usgaap_assets_102d7a4d204ed45ac0deda6bbc78f38 6" xlink:type="locator" />
169 Node: resource <link:label id="lab_ko_netchangeinoperatingassetsandliabilitiesdisclosureabstrac t_a6469a522e35cbf ee_label_en-us" xlink:label="lab_ko_netchangeinoperatingassetsandliabilitiesdisclosur eabstract_a6469a522e35cbf ee" xlink:role=" xlink:type="resource" xml:lang="en-us"> NET CHANGE IN OPERATING ASSETS AND LIABILITIES DISCLOSURE [Abstract] </link:label>
170 Edge <presentationarc order="10" preferredlabel=" xlink:arcrole=" xlink:from="loc_usgaap_assetsabstract_2f55ecb2bf7c1a62009cda6bbc757094" xlink:to="loc_usgaap_assets_102d7a4d204ed45ac0deda6bbc78f386" xlink:type="arc" />
171 Summary
172 Architecture ERP Analyze CRM ETL Report OLTP Files Mine
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