Big Data Analytics. Rasoul Karimi

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1 Big Data Analytics Rasoul Karimi Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany Big Data Analytics Big Data Analytics 1 / 1

2 Introduction Big Data: Storing and accessing large amounts of (unstructured) data Distributed File Systems: allow files to be accessed using the same interfaces and semantics as local files. Distributed Databases: store data in multiple computers. Big Data Analytics 0 / 1

3 Traditional Database Applications Big Data Analytics 1 / 1

4 Distributed Applications Big Data Analytics 2 / 1

5 Relational vs Big-Data Technologies Structure: In relational data bases, the data is structured but in big data technologies the data is raw. Process: Relational data bases were initially created for transactional processing, but big data technologies are for data analysis. Entities: A relational data base is big if it has many entities and many rows per each entity. But in big data technologies, the number of entities is not necessary high. The amount of information that exist for entities is big. horizontal scaling: Adding new nodes in big data technologies can be done with low cost while in relational data base, either it fixed or it is expensive. Big Data Analytics 3 / 1

6 NoSQL Databases A NoSQL or Not Only SQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Motivations for this approach include simplicity of design and horizontal scaling. The data structure differs from the RDBMS, and therefore some operations are faster in NoSQL and some in RDBMS. Most NoSQL stores lack true ACID transactions. Big Data Analytics 4 / 1

7 NoSQL Databases Graph: Allegro, Neo4J, OrientDB, Virtuoso Column: Accumulo, Cassandra, HBase Document: Clusterpoint, Couchbase, MarkLogic, MongoDB Key-value: Dynamo, FoundationDB, MemcacheDB, Redis, Riak Big Data Analytics 5 / 1

8 Graph A graph is a representation of a set of objects where some pairs of objects are connected by links. Example applications of graph in computer science: Automata: self-operating virtual machines to help in logical understanding of input and output process Markov Decision Process: modeling decision making provide a mathematical framework for XML Path Language (XPATH): is a query language for selecting nodes from an XML document. In big data, graphs are used to model entities and their relationships that are distributed in all nodes. Big Data Analytics 6 / 1

9 Graph databases A graph database is a database that uses graph structures with nodes, edges, and properties to represent and store data. Big Data Analytics 7 / 1

10 Graph databases Nodes represent entities such as people, businesses, accounts, or any other item you might want to keep track of. Properties are relevant information that relate to nodes. Edges are the lines that connect nodes to nodes Most of the important information is really stored in the edges. Meaningful patterns emerge when one examines the connections and interconnections of nodes, properties, and edges. Big Data Analytics 8 / 1

11 Graph databases Compared with relational databases, graph databases are often faster for associative data sets They map more directly to the structure of object-oriented applications. As they depend less on a rigid schema, they are more suitable to manage ad hoc and changing data with evolving schemas. Graph databases are a powerful tool for graph-like queries. Big Data Analytics 9 / 1

12 Graph queries Reachability queries shortest path queries Pattern queries Big Data Analytics 10 / 1

13 Resource Description Framework (RDF) RDF is W3C standard format for Linked Data. It is similar to classic conceptual modeling approaches like entity-relationship and class diagram. In RDF, data are subject-predicate-object expressions (triples). For example, The Shirt has the color blue in RDF is as the triple: a subject denoting the shirt, a predicate denoting has, and an object denoting the color blue. A collection of RDF statements represents a labeled, directed multi-graph. Big Data Analytics 11 / 1

14 Example There is a Person identified by: http : // 3.org/People/EM/contact#me whose name is Eric Miller, whose address is and whose title is Dr. < http : // >< http : // > EricMiller < http : // >< http : // >< mailto : > < http : // >< http : // > Dr. < http : // >< http : // rdf syntax ns#type >< http : // > Big Data Analytics 12 / 1

15 Column Databases Column databases are a tuple (a key-value pair) consisting of three elements: Unique name: Used to reference the column Value: The content of the column. Timestamp: The system timestamp used to determine the valid content. Differences to a relational database Big Data Analytics 13 / 1

16 Example { } street: name: street, value: 1234 x street, timestamp: , city: name: city, value: san francisco, timestamp: , zip: name: zip, value: 94107, timestamp: , Big Data Analytics 14 / 1

17 Document Databases A document-oriented database is a computer program designed for storing, retrieving, and managing document-oriented information. In contrast to relational databases and their notions of Relations (or Tables ), these systems are designed around an abstract notion of a Document. Documents inside a document-oriented database are not required to have all the same sections, slots, parts, or keys. Big Data Analytics 15 / 1

18 Example 1 { } FirstName: Bob, Address: 5 Oak St., Hobby: sailing Big Data Analytics 16 / 1

19 Example 2 { } FirstName: Jonathan, Address: 15 Wanamassa Point Road, Children: { Name: Michael, Age: 10, Name: Jennifer, Age: 8, Name: Samantha, Age: 5, Name: Elena, Age: 2 } Big Data Analytics 17 / 1

20 Document Databases Documents are addressed in the database via a unique key that represents that document. Documents can be retrieved by their key content Documents are organized through Collections Tags Non-visible Metadata Directory hierarchies Buckets Big Data Analytics 18 / 1

21 XML Databases XML databases are in the category of document-oriented databases. An XML database is a data persistence software system that allows data to be stored in XML format. Two major classes of XML database exist: XML-enabled: The input and output are XML, but they are stored in relational tables. Native XML: In addition to the input and output, the structure of data base is also XML. Big Data Analytics 19 / 1

22 XML-enabled Databases XML enabled databases typically offer one or more of the following approaches to storing XML within the traditional relational structure: XML is stored into a CLOB (Character large object) XML is cut into a series of Tables based on a Schema XML is stored into a native XML Type as defined by the ISO Typically an XML enabled database is best suited where the majority of data are non-xml. Big Data Analytics 20 / 1

23 Native XML databases Defines a (logical) model for an XML document and stores and retrieves documents according to that model. Has an XML document as its fundamental unit of (logical) storage. Need not have any particular underlying physical storage model. Additionally, many XML databases provide a logical model of grouping documents, called collections. Big Data Analytics 21 / 1

24 querying Languages in XML databases All XML databases now support at least one form of querying syntax: XPath XSLT XQuery Big Data Analytics 22 / 1

25 Key Value stores Key Value stores use the associative array as their fundamental data model. In this model, data is represented as a collection of key value pairs. The key value model is one of the simplest non-trivial data models. Example: { Great Expectations : John, Pride and Prejudice : Alice, Wuthering Heights : Alice } Big Data Analytics 23 / 1

26 Object Database An object database is a database management system in which information is represented in the form of objects as used in object-oriented programming. Most object databases also offer some kind of query language, allowing objects to be found using a declarative programming approach (OQL) Access to data can be faster because joins are often not needed. Many object databases offer support for versioning. They are specially suitable in applications with complex data. Big Data Analytics 24 / 1

27 NoSQL databases on the cloud NoSQL databases can be run cloud platforms like Amazon Web Services. There are three common deployment models for NoSQL on the cloud: Virtual machine image: cloud platforms allow users to rent virtual machine instances for a limited time and run a NoSQL database on these virtual machines. Database as a service: some cloud platforms offer options for using familiar NoSQL database products as a service, such as MongoDB, Redis and Cassandra, without physically launching a virtual machine instance for the database. Big Data Analytics 25 / 1

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