Big Data and New Data Warehousing Approaches

Ptiček, Marina; Vrdoljak, Boris

doi:10.1145/3141128.3141139

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Since its appearance, research in the area of Big Data Warehousing has been intensified, with developments aiming to bring the well-known concepts from relational databases, such as declarative query languages, tables and columns, into the unstructured environment of Hadoop. These characteristics, along with the metastore concept, i.e., the system catalog with the metadata information, contributed to the classification of Hive as a DW repository for Big Data [24]. In this sense, Hive is a distributed DW system that manages the data stored in HDFS (Hadoop Distributed File System) and provides a SQL-like language (HiveQL) for querying the data [3,26].…”

mentioning

confidence: 99%

Evaluating partitioning and bucketing strategies for Hive-based Big Data Warehousing systems

Costa

Santos

2019

J Big Data

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confidence: 99%

Evaluating partitioning and bucketing strategies for Hive-based Big Data Warehousing systems

Costa

Santos

2019

J Big Data

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“…Integration using middleware can be applied in various ways, reviewed in our previous work [8]. Middleware can be used to integrate NoSQL DBs, RDBs, and data processing engines such as Spark or Hadoop (Figure 3), with the main idea of hiding complexities of integrated systems and disburdening the users from learning specificities of underlying systems, and minimization of data movement between the processing and storage engines, which is important in case of voluminous data.…”

Section: Integration Levelsmentioning

confidence: 99%

The potential of semantic paradigm in warehousing of big data

2019

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Big data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as NoSQL databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of NoSQL databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the NoSQL gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. NoSQL databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich NoSQL database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating NoSQL data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases.

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