1. Introduction to the Course "Designing Data Bases with Advanced Data Models...Fabio Fumarola
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
Cassandra from the trenches: migrating Netflix (update)Jason Brown
Update talk on Cassandra at Netflix, presented at the Silicon Valley NoSQL meetup on 9 Feb 2012. Includes an introduction to Astyanax, an open source cassandra client written in java.
In this lecture we analyze key-values databases. At first we introduce key-value characteristics, advantages and disadvantages.
Then we analyze the major Key-Value data stores and finally we discuss about Dynamo DB.
In particular we consider how Dynamo DB: How is implemented
1. Motivation Background
2. Partitioning: Consistent Hashing
3. High Availability for writes: Vector Clocks
4. Handling temporary failures: Sloppy Quorum
5. Recovering from failures: Merkle Trees
6. Membership and failure detection: Gossip Protocol
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...Fabio Fumarola
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
Cassandra from the trenches: migrating Netflix (update)Jason Brown
Update talk on Cassandra at Netflix, presented at the Silicon Valley NoSQL meetup on 9 Feb 2012. Includes an introduction to Astyanax, an open source cassandra client written in java.
In this lecture we analyze key-values databases. At first we introduce key-value characteristics, advantages and disadvantages.
Then we analyze the major Key-Value data stores and finally we discuss about Dynamo DB.
In particular we consider how Dynamo DB: How is implemented
1. Motivation Background
2. Partitioning: Consistent Hashing
3. High Availability for writes: Vector Clocks
4. Handling temporary failures: Sloppy Quorum
5. Recovering from failures: Merkle Trees
6. Membership and failure detection: Gossip Protocol
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
Big Challenges in Data Modeling: NoSQL and Data ModelingDATAVERSITY
Big Data and NoSQL have led to big changes In the data environment, but are they all in the best interest of data? Are they technologies that "free us from the harsh limitations of relational databases?"
In this month's webinar, we will be answering questions like these, plus:
Have we managed to free organizations from having to do Data Modeling?
Is there a need for a Data Modeler on NoSQL projects?
If we build Data Models, which types will work?
If we build Data Models, how will they be used?
If we build Data Models, when will they be used?
Who will use Data Models?
Where does Data Quality happen?
Finally, we will wrap with 10 tips for data modelers in organizations incorporating NoSQL in their modern Data Architectures.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
In this lecture we analyze graph oriented databases. In particular, we consider TtitanDB as graph database. We analyze how to query using gremlin and how to create edges and vertex.
Finally, we presents how to use rexster to visualize the storeg graph/
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
Big Challenges in Data Modeling: NoSQL and Data ModelingDATAVERSITY
Big Data and NoSQL have led to big changes In the data environment, but are they all in the best interest of data? Are they technologies that "free us from the harsh limitations of relational databases?"
In this month's webinar, we will be answering questions like these, plus:
Have we managed to free organizations from having to do Data Modeling?
Is there a need for a Data Modeler on NoSQL projects?
If we build Data Models, which types will work?
If we build Data Models, how will they be used?
If we build Data Models, when will they be used?
Who will use Data Models?
Where does Data Quality happen?
Finally, we will wrap with 10 tips for data modelers in organizations incorporating NoSQL in their modern Data Architectures.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
In this lecture we analyze graph oriented databases. In particular, we consider TtitanDB as graph database. We analyze how to query using gremlin and how to create edges and vertex.
Finally, we presents how to use rexster to visualize the storeg graph/
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
In these slides we analyze why the aggregate data models change the way data is stored and manipulated. We introduce MapReduce and its open source implementation Hadoop. We consider how MapReduce jobs are written and executed by Hadoop.
Finally we introduce spark using a docker image and we show how to use anonymous function in spark.
The topics of the next slides will be
- Spark Shell (Scala, Python)
- Shark Shell
- Data Frames
- Spark Streaming
- Code Examples: Data Processing and Machine Learning
In these slides is given an overview of the different parts of Apache Spark.
We analyze spark shell both in scala and python. Then we consider Spark SQL with an introduction to Data Frame API. Finally we describe Spark Streaming and we make some code examples.
Topics:spark-shell, pyspark, HDFS, how to copy file to HDFS, spark transformations, spark actions, Spark SQL (Shark),
spark streaming, streaming transformation stateless vs stateful, sliding windows, examples
Slides for class session I taught at USC Annenberg on approaching big data for a non-technical audience so that they can learn the project planning skills to work with technical teams. The goal is to teach students the mindset they should when taking in mixed methods and applying to large datasets prior to selecting software packages and methodology. The slides take us through a previous use case and guidance moving forward from a process and cross-functional team perspective.
In these slides we introduce Column-Oriented Stores. We deeply analyze Google BigTable. We discuss about features, data model, architecture, components and its implementation. In the second part we discuss all the major open source implementation for column-oriented databases.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me to download the slides
Aloha Social Networking Portal - Design DocumentMilind Gokhale
Aloha is a web portal which allows users to connect with their friends and family through a
common platform. Furthermore, users’ can share scribbles and ChitChat with their friends. These
chats can be saved or deleted as per the users’ wishes. Users can also maintain, update or delete
their account. Spring MVC / WebSockets / AJAX / Javascript
Graph Search: The Power of Connected DataCodemotion
Today’s complex data is big, variably-structured and densely connected. In this session we’ll look at how size, structure and connectedness have converged to change the way we work with data. We’ll then go on to look at some of the new opportunities for creating end-user value that have emerged in a world of connected data, illustrated with graph search examples implemented using the Neo4j graph database.
A NOSQL Overview And The Benefits Of Graph Databases (nosql east 2009)Emil Eifrem
Presentation given at nosql east 2009 in Atlanta. Introduces the NOSQL space by offering a framework for categorization and discusses the benefits of graph databases. Oh, and also includes some tongue-in-cheek party poopers about sucky things in the NOSQL space.
Graph Databases - Where Do We Do the Modeling Part?DATAVERSITY
Graph processing and graph databases have been with us for a while. However, since their physical implementations are the same for every database in production (Node connected to node, or triplets), there's a perception that data modeling (and data modelers) have no role on projects where graph databases are used.
This month we'll talk about where graph databases are a best fit in a modern data architecture and where data models add value.
Graph databases are used to represent graph structures with nodes, edges and properties. Neo4j, an open-source graph database is reliable and fast for managing and querying highly connected data. Will explore how to install and configure, create nodes and relationships, query with the Cypher Query Language, importing data and using Neo4j in concert with SQL Server... Providing answers and insight with visual diagrams about connected data that you have in your SQL Server Databases!
Introduction to graph databases in term of neo4jAbdullah Hamidi
The records in a graph database are called Nodes .
Nodes are connected through typed, directed Relationships.
Each single Node and Relationship can have named attributes referred to as Properties.
A Label is a name that organizes nodes into groups.
The flexibility of the graph model has allowed us to add new nodes and new relationships.
Relationships in a graph naturally form paths. Querying—or traversing—the graph involves following paths.
Database Models, Client-Server Architecture, Distributed Database and Classif...Rubal Sagwal
Introduction to Data Models
-Hierarchical Model
-Network Model
-Relational Model
-Client/Server Architecture
Introduction to Distributed Database
Classification of DBMS
Data Models - Department of Computer Science & Engineeringacemindia
Why data models are important?
About the basic data-modeling building blocks.
How the major data models evolved?
How data models can be classified by level of abstraction?
Selecting the right database type for your knowledge management needs.Synaptica, LLC
This presentation looks at relational vs. graph databases and their advantages and disadvantages in storing semantic data for taxonomies and ontologies.
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
Graph algorithms are powerful tools, and there’s a lot of excitement about their applications for data science. It can sometimes be difficult, however - especially for those of us who aren’t data scientists - to know how they might be applied to a particular data set or a specific business problem. There are graph algorithms for centrality and importance measurement, community detection, similarity comparison, pathfinding, and link prediction. Which ones should you use on your data, and which ones might be most useful in answering your business questions?
In this presentation, we’ll look at a few examples of Neo4j graph algorithms, and see how they can be applied to data and business problems from the banking industry. We’ll discuss what kinds of data are appropriate for different types of algorithms, show how to model and structure data to work with graph algorithms, and run through some real-world scenarios demonstrating the use of graph algorithms on a sample banking data set.
Webinar with Joe Depeau, Neo4j, April 15, 2020
https://www.learntek.org/blog/types-of-databases/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me to download the slides
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce Fabio Fumarola
Recently, several algorithms based on the MapRe- duce framework have been proposed for frequent pattern mining in Big Data. However, the proposed solutions come with their own technical challenges, such as inter-communication costs, in- process synchronizations, balanced data distribution and input parameters tuning, which negatively affect the computation time. In this paper we present MrAdam, a novel parallel, distributed algorithm which addresses these problems. The key principle underlying the design of MrAdam is that one can make reasonable decisions in the absence of perfect answers. Indeed, given the classical threshold for minimum support and a user- specified error bound, MrAdam exploits the Chernoff bound to mine “approximate” frequent itemsets with statistical error guarantees on their actual supports. These itemsets are generated in parallel and independently from subsets of the input dataset, by exploiting the MapReduce parallel computation framework. The result collections of frequent itemsets from each subset are aggregated and filtered by using a novel technique to provide a single collection in output. MrAdam can scale well on gigabytes of data and tens of machines, as experimentally proven on real datasets. In the experiments we also show that the proposed algorithm returns a good statistically bounded approximation of the exact results.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. Outline
• Introduction
• The Lack of relationship for RDBMS and NoSQL
• Graph Databases: Features
• Relations
• Query Language
• Data Modeling with Graphs
• Conclusions
2
3. Introduction
• We live in a connected world
• Everything is connected: Social Network, Biology,
Bioinformatics
• The NoSQL databases analyzed store data using
aggregate
• Here we compare graph databases with relational
databases and aggregate NOSQL in storing graph
data
3
4. Three Facts
1. Relational Databases Lack Relationships
1. NoSQL Databases also Lack Relationships
2. Graph Databases Embrace Relationships
4
5. Relational Databases Lack Relationships
• For decades we tried to accommodate connected,
semi-structured datasets inside relational databases.
• But:
– relational databases are designed to codify tabular
structures
– They struggle when modeling ad hoc exceptional
relationships that are in real world.
5
6. Relational Databases Lack Relationships
• Relationships in relational database only mean joining tables
• But we want to model the semantic of relationships that
connect real world
• As outlier data multiplies:
1. The structure of the dataset becomes more complex and
less uniform
2. The relational data becomes more complex and less
uniform (large join tables, sparsely populated rows, a lot
o null values)
6
7. Example of customer-centric orders
• Complex joins
• Foreign key constraints
• Sparse table with null
values
• Reciprocal queries are
costly “What products did a
customer buy?”
7
8. NoSQL Databases Also Lack
Relationships
• key-value, document, or column-oriented store sets of
disconnected documents/values/columns
• One well-known strategy for adding relationships is to embed
an aggregate’s identifier inside the field belonging to another
aggregate
• But this require joins at the application level
• Some NoSQL have some concept of navigability but it is
expensive for complex joins
8
9. Example of aggregate oriented orders
• Some properties are
references to foreign
aggregates
• This relationship are
not first-class citizens
• Are not intended as
real realtionships
9
10. Example of a Small Social Network
• it’s easy to find a user’s
immediate friends
• friendship isn’t always
reflexive
• We can have brute-force
scan across the whole
dataset looking for friends
entries
10
11. Graph Databases Embrace Relationships
• The previous examples have dealt with implicitly connected
data
• We infer semantic dependencies between entities
• We model the data based on this connections
• Our application have to navigate on this flat and disconnected
data, and deal with slow queries.
• In contrast, in the graph world, connected data is stored as
connected data
11
12. Example Social Network
12
• The node user:Bob is a
Vertex with a property
Bob
• We also see relations
which are Edges:
• Boss_of
• Friend_of
• Married_to
15. Consistency
• Since Graph DBs operate on connected nodes, they
could not scale well distributing nodes across
servers.
• There are solutions supporting distribution:
– Neo4j uses one master and several slaves
– OrientDB uses MVCC for distributed eventual data
structures
– TitanDB partition data by using HBase or Cassandra
15
16. Transactions
• Most of the Graph DB are ACID-compliant
• Before doing an operation we have to start a
transaction.
• Without wrapping operations in a transaction we will
get an Exception.
16
17. Availability
• Neo4j from version 1.8 achieves availability by
providing for replicated slaves.
• Infinity Graph, FlockDB and TitanDB provides for
distribute storage of the nodes.
• Neo4J uses Zookeeper to keep track of the last
transaction Ids persisted on each slave node and the
current master node
17
19. Relations
• Relations in a graph naturally forms paths.
• Querying or traversing the graph involves following a
path.
• A query on the graph is also known as traversing the
graph
• As advantage we can change the traversing
requirements without changing nodes and edges
19
20. Relations
• In graph databases traversal operation are highly
efficient.
• In the book Neo4j in Action, Partner and Vukotic
perform an experiment comparing relational store
and Neo4j
20
21. Relations
• In a depth two (friend-of-friend), both relational db and graph
db perform well enough
• But when we do the depth three it clear that relational db can
no longer deal
21
22. Relations
• Both aggregate store and relational databases perform poorly
because of the index lookups.
• Graphs, on the other hand, use index-free adjacency list to
ensure that traversing connected data is extremely fast.
22
23. Relations another case study
• Let us consider the
purchase history of a user
as connected data.
• If we notice that users who
buy strawberry ice cream
also buy espresso beans, we
can start to recommend
those beans to users who
normally only buy the ice
cream.
23
24. Relations and Recommendations
• The previous was a one dimensional
recommendation
• We can join our graph with graph from other
domains.
• For example, we can ask to fine
– “all the flavors of ice cream liked by people who live near a
user, and enjoy espresso, but dislike Brussels sprouts.”
24
25. Relations and Patterns
• We can use relations to query graph-patterns
• Such pattern-matching queries are:
– extremely difficult to write in SQL
– And are laborious to write against aggregate stores
• In both cases they tend to perform very poorly
• In the other hand, graph databases are optimized for
such kind of queries
25
27. Query Language
• Graph DBs support query
languages such as Gremlin,
Cypher and SPARQL
• Gremlin is a DSL for
traversing graphs;
• It can traverse all the graph
databases implementing the
Blueprints
27
28. 1. Indexing: Nodes and Edges
• Indexes are necessary to find the starting node to
being traversal.
• How Indexes works:
– Can index properties of nodes and edges.
– Adds are done in transactions
• Nodes retrieved can be used to raise queries
28
29. 2. Querying In- Out- Relationships
• Having a node we can query both for Incoming and
Outgoing relationships.
• We can apply directional filters on the queries when
querying for relations
29
30. 3. Querying Breadth- Depth-
• Graph databases are really powerful to query for
incoming and outgoing relationships.
• Moreover, we can make the traverser go top-down
or sideways on the graph by using:
– BREADTH_FIRST or
– DEPTH_FIRST
30
31. 4. Querying Paths
• An other good feature of graph databases is
– finding paths between two nodes.
– Determining if there are multiple paths
– finding the shortest path
• Many Graph DBs use algorithms such as the
Dijkstra’s algorithm for finding shortest paths.
31
32. 5. Querying Paths
• Finally, with Graph DBs it is possible to use Match
operator
• The MATCH is used for matching patterns in
relationships
• The WHERE filters the properties on a node or
relationship
• The RETURN specifies what to get in the result set.
32
35. how do we model the world in
graph terms?
• Formalization of the base model
• Enrich the model
• Testing the model
35
36. Formalization of the base model
• Modeling is an abstracting activity motivated by a particular
need or goal
• We model in order to define structures that can manipulated.
• There are no natural representations of the world the way it
“really is,”
• There are just many purposeful selections, abstractions, and
simplifications that useful for satisfying a particular goal
36
37. Formalization of the base model
• Graph data modeling is different from many other
techniques.
• There is a close affinity between logical and physical
models.
• In relational databases we start from a logical model
to arrive to the physical model.
• With graph databases, this gap shrinks considerably.
37
38. The Graph Model
• A property graph is made up of nodes, relationships,
and properties.
38
40. Nodes
Nodes contain properties
•Think of nodes as documents that store properties in
the form of arbitrary key-value pairs.
•The keys are strings and the values are arbitrary data
types.
40
41. Relationships
Relationships connect and structure nodes.
•A relationship always has a direction, a label, and a
start node and an end node—there are no dangling
relationships.
•Together, a relationship’s direction and label add
semantic clarity to the structuring of nodes.
41
42. Relationships: Attributes
Like nodes, relationships can also have properties.
•The ability to add properties to relationships is
particularly useful for:
– Providing additional metadata for graph algorithms
– Adding additional semantics to relationships (including
quality and weight),
– and for constraining queries at runtime.
42
43. Modeling Steps: Outline
• The initial stage of modeling is similar to the first
stage of many other data modeling techniques, that
is:
– to understand and agree on the entities in the domain
– how they interrelate
– and the rules that govern their state transitions
43
44. Describe the Model in Terms of the
Application’s Needs
• Agile user stories provide a concise means for
expressing an outside-in, user-centered view of the
application needs.
• Here’s an example of a user story for a book review
web application:
– AS A reader who likes a book,
– I WANT to know which books other readers who like the
same book have liked,
– SO THAT I can find other books to read.
44
45. Describe the Model in Terms of the
Application’s Needs
• This story expresses a user need, which motivates
the shape and content of our data model.
• From a data modeling point of view:
– the AS A clause establishes a context comprising two
entities—a reader and a book—plus the LIKES relationship
that connects them.
– The I WANT clause exposes more LIKES relationships, and
more entities: other readers and other books.
45
46. Describe the Model in Terms of the
Application’s Needs
• The entities and relationships in analyzing the user
story quickly translate into a simple data model
46
47. Modeling Rationale
• Use nodes to represent entities
• Use relationships both:
– to express the connections between entities and
– to establish semantic context for each entity
• Use relationship direction to further clarify
relationship semantics
47
48. Describe the Model: Guidelines
• Use node properties
– to represent entity attributes, plus any necessary entity
metadata, such as timestamps, version numbers, etc
• Use relationship properties
– to express the strength, weight, or quality of a
relationship, plus any necessary relationship metadata,
such as timestamps, version numbers, etc.
48
49. Modeling Temporal Relations as
Nodes
• When two or more domain entities interact for a
period of time, a fact emerges
• We represent these facts as separate nodes
• In the following examples we show how we might
model facts and actions using intermediate nodes.
49
55. Iterative and Incremental
• We develop the data model feature by feature, user
story by user story
• This will ensure we identify the relationships our
application will use to query the graph
• With the iterative and incremental delivery of
application features we will be a corrected model
that provides the right abstraction
55
56. Data Modeling: Enrich
• The next steps diverges from the relational data
methodology
• Instead of transforming a domain model’s graph-like
representation into tables, we enrich it.
• That is, for each entity in our domain, “we ensure
that we’ve captured both the properties and the
connections to neighboring entities necessary to
support our application goals”.
56
57. Data Modeling: Enrich
• Remember, the domain model is not totally aligned
to reality.
• it is a purposeful abstraction of those aspects of our
domain relevant to our application goals.
• By enriching our domain graph with additional
properties and relationships, we effectively produce
a graph model aligned to our application’s data
needs
57
58. Data Modeling: Enrich
In graph terms, we are ensuring that:
•each node has the appropriate properties
•every node is in the correct semantic context.
we do this by creating named and directed (and often
attributed) relationships between the nodes to capture
the structural aspects of the domain.
58
59. Data Modeling: Test
• The next step is to test how suitable it is for
answering realistic queries
• Also if Graph DB are great in supporting evolving
structures there are some design decisions to
consider
• By reviewing the domain model and the resulting
graph model at this early stage, we can avoid these
pitfalls.
59
60. Data Modeling: Test
• In practice there are two techniques that we can
apply here
• The first, and simplest, is just to check that the graph
reads well.
• We pick a start node, and then follow relationships
to other nodes, reading each node’s role and each
relationship’s name as we go
• Doing so should create sensible sentences
60
61. Data Modeling: Test
• The second one is to consider queries we’ll run on
the graph.
• To validate that the graph supports the kinds of
queries we expect to run on it, we must describe
those queries.
• Given a described query if we can easily write the
query in Cypher or Gremlin we can be more certain
that the graph meets the needs of our domain.
61
63. Avoid Anti-Patterns
• In the general case, don’t encode entities into relationships.
• It’s also important to realize that graphs are a naturally
additive structure
• It’s quite natural to add facts in terms of domain entities and
how they interrelate adding nodes and relationships
• If we model in accordance with the questions we want to ask
of our data, an accurate representation of the domain will
emerge.
63
64. When to Use
• Connected Data
• Routing, Dispatch, and Location-based Services
• Recommendation Engines
64
65. When Not to Use
• When you need to update all or a subset of entities,
for example in analytics
• In situation when you need to apply operations that
work on the global graph
• When you don’t know the starting point of your
query
65
Editor's Notes
For example, users often want to see their order history, so we’ve added a linked list structure to the graph that allows us to find a user’s most recent order by following an outgoing MOST_RECENT relationship. We can then iterate through the list, going further back in time, by following each PREVIOUS relationship. If we want to move forward in time, we can follow each PREVIOUS relationship in the opposite direction, or add a reciprocal NEXT relationship.