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A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.
Implementation of multidimensional databases with document-oriented NoSQL
Implémentation des entrepôts de données NoSQL dans les bases de données NoSQL orienté documents.
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.
Implementation of multidimensional databases with document-oriented NoSQL
Implémentation des entrepôts de données NoSQL dans les bases de données NoSQL orienté documents.
Data Structure is a way of collecting and organising data in such a way that we can perform operations on these data in an effective way. Data Structures is about rendering data elements in terms of some relationship, for better organization and storage. For example, we have data player's name "Virat" and age 26. Here "Virat" is of String data type and 26 is of integer data type.
We can organize this data as a record like Player record. Now we can collect and store player's records in a file or database as a data structure. For example: "Dhoni" 30, "Gambhir" 31, "Sehwag" 33
In simple language, Data Structures are structures programmed to store ordered data, so that various operations can be performed on it easily.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Data Structure is a way of collecting and organising data in such a way that we can perform operations on these data in an effective way. Data Structures is about rendering data elements in terms of some relationship, for better organization and storage. For example, we have data player's name "Virat" and age 26. Here "Virat" is of String data type and 26 is of integer data type.
We can organize this data as a record like Player record. Now we can collect and store player's records in a file or database as a data structure. For example: "Dhoni" 30, "Gambhir" 31, "Sehwag" 33
In simple language, Data Structures are structures programmed to store ordered data, so that various operations can be performed on it easily.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Data massage: How databases have been scaled from one to one million nodesUlf Wendel
A workshop from the PHP Summit 2013, Berlin.
Join me on a journey to scaling databases from one to one million nodes. The adventure begins in the 1960th and ends with Google Spanner details from a Google engineer's talk given as late as November 25th, 2013!
Contents: Relational systems and caching (briefly), what CAP means, Overlay networks, Distributed Hash Tables (Chord), Amazon Dynamo, Riak 2.0 including CRDT, BigTable (Distributed File System, Distributed Locking Service), HBase (Hive, Presto, Impala, ...), Google Spanner and how their unique TrueTime API enables ACID, what CAP really means to ACID transactions (and the NoSQL marketing fuzz), the latest impact of NoSQL on the RDBMS world. There're quite a bit of theory in the talk, but that's how things go when you walk between Distributed Systems Theory and Theory of Parallel and Distributed Databases, such as.... Two-Phase Commit, Two-Phase Locking, Virtual Synchrony, Atomic Broadcast, FLP Impossibility Theorem, Paxos, Co-Location and data models...
Data Warehouse Physical Design,Physical Data Model, Tablespaces, Integrity Constraints, ETL (Extract-Transform-Load) ,OLAP Server Architectures, MOLAP vs. ROLAP, Distributed Data Warehouse ,
In this presentation we’ll look at five ways in which we can use efficient coding to help our garbage collector spend less CPU time allocating and freeing memory, and reduce GC overhead.
The 3TU.Datacentrum repository of research data hosts datasets as well as other objects representing measuring devices, locations, time periods and the like. Virtually all metadata is in rdf so the repository can be approached as an rdf graph. We will show how this is implemented with Fedora Commons, heavily leaning on rdf queries and xslt2.0. As a result of this architecture, it is relatively easy to make the repository linked-data-enabled by generating OAI/ORE resource maps.
While most of the metadata is rdf, most of the data is in NetCDF. Although not very well known in the library world, this is very popular format in various fields of science and engineering. It comes with its own data server Opendap which offers a rich API to interact with the data. Our repository is therefore a hybrid Fedora + Opendap setup and we will show how the two are integrated into a unified view and how they are kept in sync on ingest.
This was presented at the ELAG conference, Palma de Mallorca 2012.
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3. ∗ We have mostly focused on techniques for virtual
data integration (see Ch. 1)
∗ Queries are composed with mappings on the fly and
data is fetched on demand
∗ This represents one extreme point
∗ In this chapter, we consider cases where data is
transformed and materialized “in advance” of the
queries
∗ The main scenario: the data warehouse
Data Warehousing and Materialization
4. ∗ In many organizations, we want a central “store” of all of
our entities, concepts, metadata, and historical
information
∗ For doing data validation, complex mining, analysis,
prediction, …
∗ This is the data warehouse
∗ To this point we’ve focused on scenarios where the data
“lives” in the sources – here we may have a “master”
version (and archival version) in a central database
∗ For performance reasons, availability reasons, archival
reasons, …
What Is a Data Warehouse?
5. ∗ The data warehouse as the master data instance
∗ Data warehouse architectures, design, loading
∗ Data exchange: declarative data warehousing
∗ Hybrid models: caching and partial materialization
∗ Querying externally archived data
In the Rest of this Chapter…
6. The data warehouse
∗ Motivation: Master data management
∗ Physical design
∗ Extract/transform/load
∗ Data exchange
∗ Caching & partial materialization
∗ Operating on external data
Outline
7. ∗ One of the “modern” uses of the data warehouse is not
only to support analytics but to serve as a reference to all
of the entities in the organization
∗ A cleaned, validated repository of what we know
… which can be linked to by data sources
… which may help with data cleaning
… and which may be the basis of data governance (processes
by which data is created and modified in a systematic way,
e.g., to comply with gov’t regulations)
∗ There is an emerging field called master data management
out the process of creating these
Master Data Management
8. Data Warehouse Architecture
∗ At the top – a centralized
database
∗ Generally configured for
queries and appends – not
transactions
∗ Many indices, materialized
views, etc.
∗ Data is loaded and
periodically updated via
Extract/Transform/Load
(ETL) tools
Data Warehouse
ETL ETL ETL ETL
RDBMS1 RDBMS2
HTML1 XML1
ETL pipeline
outputs
ETL
9. ∗ ETL tools are the equivalent of schema mappings in virtual
integration, but are more powerful
∗ Arbitrary pieces of code to take data from a source,
convert it into data for the warehouse:
∗ import filters – read and convert from data sources
∗ data transformations – join, aggregate, filter, convert data
∗ de-duplication – finds multiple records referring to the same
entity, merges them
∗ profiling – builds tables, histograms, etc. to summarize data
∗ quality management – test against master values, known
business rules, constraints, etc.
ETL Tools
10. Example ETL Tool Chain
∗ This is an example for e-commerce loading
∗ Note multiple stages of filtering (using selection or join-like
operations), logging bad records, before we group and load
Invoice
line items
Split
Date-
time
Filter
invalid
Join
Filter
invalid
Invalid
dates/times
Invalid
items
Item
records
Filter
non -
match
Invalid
customers
Group by
customer
Customer
balance
Customer
records
11. ∗ Two aspects:
∗ A central DBMS optimized for appends and querying
∗ The “master data” instance
∗ Or the instance for doing mining, analytics, and prediction
∗ A set of procedural ETL “pipelines” to fetch, transform, filter,
clean, and load data
∗ Often these tools are more expressive than standard
conjunctive queries (as in Chapters 2-3)
∗ … But not always!
This raises a question – can we do warehousing with declarative
mappings?
Basic Data Warehouse – Summary
12. The data warehouse
Data exchange
∗ Caching & partial materialization
∗ Operating on external data
Outline
13. ∗ Intuitively, a declarative setup for data warehousing
∗ Declarative schema mappings as in Ch. 2-3
∗ Materialized database as in the previous section
∗ Also allow for unknown values when we map from
source to target (warehouse) instance
∗ If we know a professor teaches a student, then there
must exist a course C that the student took and the
professor taught – but we may not know which…
Data Exchange
14. A data exchange setting (S,T,M,CT) has:
∗ S, source schema representing all of the source tables
jointly
∗ T, target schema
∗ A set of mappings or tuple-generating dependencies
relating S and T
∗ A set of constraints (equality-generating dependencies)
Data Exchange Formulation
(∀X)s1(X1), ..., sm (Xm ) → (∃Y) t1(Y1), ..., tk (Yk )
)Y(Y)Y(t...,,)Y()tY( jill11 =→∃
15. An Example
Source S has
Teaches(prof, student)
Adviser(adviser, student)
Target T has
Advise(adviser, student)
TeachesCourse(prof, course)
Takes(course, student)
),(),,(.,),(:
),(),(:
),(),,(.),(:
),(.),(:
4
3
2
1
studCTakesCDrseTeachesCouDCstudprofAdviserr
studprofAdvisestudprofAdviserr
studCTakesCprofrseTeachesCouCstudprofTeachesr
studDAdviseDstudprofTeachesr
∃→
→
∃→
∃→
existential variables represent unknowns
16. ∗ The goal of data exchange is to compute an instance
of the target schema, given a data exchange setting D
= (S,T,M,CT) and an instance I(S)
∗ An instance J of Schema T is a data exchange solution
for D and I if
1. the pair (I,J) satisfies schema mapping M, and
2. J satisfies constraints CT
The Data Exchange Solution
17. Instance I(S) has
Teaches
Adviser
prof student
Ann Bob
Chloe David
Back to the Example, Now with Data
adviser student
Ellen Bob
Felicia David
adviser student
Ellen Bob
Felicia David
course student
C1 Bob
C2 David
prof course
Ann C1
Chloe C2
Instance J(T) has
Advise
TeachesCourse
Takes
variables or labeled nulls
represent unknown values
18. Instance I(S) has
Teaches
Adviser
prof student
Ann Bob
Chloe David
This Is also a Solution
adviser student
Ellen Bob
Felicia David
adviser student
Ellen Bob
Felicia David
course student
C1 Bob
C1 David
prof course
Ann C1
Chloe C1
Instance J(T) has
Advise
TeachesCourse
Takes
this time the labeled
nulls are all the same!
19. ∗ Intuitively, the first solution should be better than the
second
∗ The first solution uses the same variable for the course
taught by Ann and by Chloe – they are the same course
∗ But this was not specified in the original schema!
∗ We formalize that through the notion of the universal
solution, which must not lose any information
Universal Solutions
20. First we define instance homomorphism:
∗ Let J1, J2 be two instances of schema T
∗ A mapping h: J1 J2 is a homomorphism from J1 to J2 if
∗ h(c) = c for every c ∈ C,
∗ for every tuple R(a1,…,an) ∈ J1 the tuple R(h(a1),…,h(an)) ∈ J2
∗ J1, J2 are homomorphically equivalent if there are homomorphisms
h: J1 J2 and h’: J2 J1
Def: Universal solution for data exchange setting
D = (S,T,M,CT), where I is an instance of S.
A data exchange solution J for D and I is a universal solution if, for
every other data exchange solution J’ for D and I, there exists a
homomorphism h: J J’
Formalizing the Universal Solution
21. ∗ The standard process is to use a procedure called the chase
∗ Informally:
∗ Consider every formula r of M in turn:
∗ If there is a variable substitution for the left-hand side (lhs) of r
where the right-hand side (rhs) is not in the solution – add it
∗ If we create a new tuple, for every existential variable in the
rhs, substitute a new fresh variable
∗ See Chapter 10 Algorithm 10 for full pseudocode
Computing Universal Solutions
22. ∗ Universal solutions may be of arbitrary size
∗ The core universal solution is the minimal universal
solution
Core Universal Solutions
23. ∗ As with the data warehouse, all queries are directly posed
over the target database – no reformulation necessary
∗ However, we typically assume certain answers semantics
∗ To get the certain answers (which are the same as in the
virtual integration setting with GLAV/TGD mappings) –
compute the query answers and then drop any tuples with
labeled nulls (variables)
Data Exchange and Querying
24. ∗ From an external perspective, exchange and
warehousing are essentially equivalent
∗ But there are different trade-offs in procedural vs.
declarative mappings
∗ Procedural – more expressive
∗ Declarative – easier to reason about, compose, invert,
create matieralized views for, etc. (see Chapter 6)
Data Exchange vs. Warehousing
25. The data warehouse
Data exchange
Caching & partial materialization
∗ Operating on external data
Outline
26. ∗ Many real EII systems compute and maintain materialized views,
or cache results
A “hybrid” point between the fully virtual and fully materialized
approaches
The Spectrum of Materialization
Virtual integration
(EII)
Data exchange /
data warehouse
sources materialized all mediated relations
materialized
caching or partial materialization –
some views materialized
27. Cache results of prior queries
∗ Take the results of each query, materialize them
∗ Use answering queries using views to reuse
∗ Expire using time-to-live… May not always be fresh!
Administrator-selected views
∗ Someone manually specifies views to compute and maintain,
as with a relational DBMS
∗ System automatically maintains
Automatic view selection
∗ Using query workload, update frequencies – a view
materialization wizard chooses what to materialize
Possible Techniques for Choosing
What to Materialize
28. The data warehouse
Data exchange
Caching & partial materialization
Operating on external data
Outline
29. ∗ Many Web scenarios where we have large logs of data
accesses, created by the server
∗ Goal: put these together and query them!
∗ Looks like a very simple data integration scenario –
external data, but single schema
∗ A common approach: use programming environments like
MapReduce (or SQL layers above) to query the data on a
cluster
∗ MapReduce reliably runs large jobs across 100s or 1000s of
“shared nothing” nodes in a cluster
Many “Integration-Like” Scenarios
over Historical Data
30. ∗ MapReduce is essentially a template for writing distributed
programs – corresponding to a single SQL
SELECT..FROM..WHERE..GROUP BY..HAVING block with
user-defined functions
∗ The MapReduce runtime calls a set of functions:
∗ map is given a tuple, outputs 0 or more tuples in response
∗ roughly like the WHERE clause
∗ shuffle is a stage for doing sort-based grouping on a key
(specified by the map)
∗ reduce is an aggregate function called over the set of tuples
with the same grouping key
MapReduce Basics
32. ∗ Some people use MapReduce to take data, transform it, and
load it into a warehouse
∗ … which is basically what ETL tools do!
∗ The dividing line between DBMSs, EII, MapReduce is blurring as
of the development of this book
∗ SQL MapReduce
∗ MapReduce over SQL engines
∗ Shared-nothing DBMSs
∗ NoSQL
MapReduce as ETL
33. ∗ There are benefits to centralizing & materializing data
∗ Performance, especially for analytics / mining
∗ Archival
∗ Standardization / canonicalization
∗ Data warehouses typically use procedural ETL tools to
extract, transform, load (and clean) data
∗ Data exchange replaces ETL with declarative mappings
(where feasible)
∗ Hybrid schemes exist for partial materialization
∗ Increasingly we are integrating via MapReduce and its
cousins
Warehousing & Materialization Wrap-
up