New Data Warehouse Technologies - Data Warehouse Systems: Design and Implementation - page 525

13.9 Bibliographic Notes
There is a wide corpus of academic literature and industrial white papers
on the topics covered in this chapter. An interesting study about the new
requirements for data warehousing is given in [ 193 ]. The authors of this
topic explore new challenges in data warehousing in [ 215 ], where also many
references can be found. The work by Dean et al. [ 36 ] gives a good description
of MapReduce. Hadoop is described, for example, in [ 226 ]. Hive is discussed
in [ 25 , 201 ] and Pig Latin in [ 61 ]. A discussion on MapReduce and column-
store databases is provided in [ 195 ]. An example of the use of MapReduce
in the ETL process is given in [ 118 ]. C-Store, one of the first column-store
databases, is discussed in [ 194 ]. Its commercial version Vertica is studied
in [ 111 ]. MonetDB is reviewed in [ 89 ]. IMDBSs are studied in [ 164 ], where
SAP HANA is also discussed. Oracle TimesTen is described in [ 109 ]. There are
several works on real-time data warehousing and real-time ETL [ 21 , 185 , 220 ].
Real-time partitions are discussed in the topics [ 102 , 103 ]. The notion of right-
time data warehousing is proposed in [ 200 ]. The ELT approach has been
introduced in a paper by Cohen et al. [ 33 ].
13.10 Review Questions
13.1 What is big data? How can we characterize this notion?
13.2 What are the challenges that big data poses to the future of data
warehousing?
13.3 Describe the main characteristics of the MapReduce paradigm.
13.4 Describe the main features of Hadoop.
13.5 What is Hive? What is Pig Latin? Compare Hive and Pig Latin
proposing dimensions for this comparison.
13.6 Explain the main characteristics of column-store databases.
13.7 How do column-store databases achieve better eciency than row-
store databases in the case of data warehouses? Is this the case for
OLTP databases?
13.8 How do column-store database systems compress the data?
13.9 What are IMDBSs? Which kinds of them have we studied in this
chapter?
13.10 What are business intelligence appliances?
13.11 How do optimization techniques differ between IMDBSs and disk-
based database systems?
13.12 Describe a typical IMDBS architecture.
13.13 Describe similarities and differences between SAP HANA, MonetDB,
and Vertica.