This talk will introduce you to Couchbase Mobile - an open source, JSON Document Data store for your mobile platforms. If you have ever built or are thinking of building an iOS app with offline data storage capabilities, you have probably looked at Core Data or SQLite and you have probably realized that it’s a daunting task. There are many challenges and this talk will begin by examining some of the main requirements of an offline storage solution. This talk includes a discussion of NoSQL technologies focussing on the JSON Document Style. You will get an overview of the Couchbase Mobile architecture and examples that demonstrate the CRUD and Query API for managing your data store.
How Graph Databases efficiently store, manage and query connected data at s...jexp
Graph Databases try to make it easy for developers to leverage huge amounts of connected information for everything from routing to recommendations. Doing that poses a number of challenges on the implementation side. In this talk we want to look at the different storage, query and consistency approaches that are used behind the scenes. We’ll check out current and future solutions used in Neo4j and other graph databases for addressing global consistency, query and storage optimization, indexing and more and see which papers and research database developers take inspirations from.
The document is a presentation by Manash Ranjan Rautray on introducing graph databases and Neo4j. It discusses what a graph and graph database are, provides examples to illustrate graphs, and covers the basics of using Neo4j including its data model, query language Cypher, and real-world use cases for graph databases. The presentation aims to explain the concepts and capabilities of Neo4j for storing and querying connected data.
Polyglot Persistence with MongoDB and Neo4jCorie Pollock
Learn how to enhance your application by using Neo4j and MongoDB together. Polyglot persistence is the concept of taking advantage of the strengths of different database technologies to improve functionality and enhance your application. In this webinar we will examine some use cases where it makes sense to use a document database (MongoDB) with a graph database (Neo4j) in a single application. Specifically, we will show how MongoDB can be used to provide search and browsing functionality for a product catalog while using Neo4j to provide personalized product recommendations. Finally we will look at the Neo4j Doc Manager project which facilitates syncing data from MongoDB to Neo4j to make polyglot persistence with MongoDB and Neo4j much easier.
This document provides an overview of GraphDB and Neo4j. It discusses why graphs are useful for modeling connected data and common use cases. It also summarizes Neo4j's transactional graph database capabilities, performance advantages, and deployment options. Key topics covered include causal clustering, query planning, and driver and tooling support for developers.
This document discusses how search has evolved beyond traditional text search to support additional use cases like recommendations and analytics. It introduces LucidWorks' products like Solr and SiLK that leverage Hadoop to power search and discovery across large datasets. New features in Solr 4 like reduced memory usage and joins are highlighted. Demos are presented on applications in ecommerce, healthcare, and finance.
This document summarizes a webinar about importing crime data from Chicago into Neo4j. It discusses loading a CSV file of crime data into Neo4j using LOAD CSV and creating nodes and relationships. It also describes using Spark to preprocess the CSV into multiple Neo4j-formatted files and bulk loading them using the Neo4j Import tool. The document then covers enriching the graph with additional crime data from JSON and updating the graph with new crimes.
What's the Scoop on Hadoop? How It Works and How to WORK IT!MongoDB
MongoDB and Hadoop work powerfully together as complementary technologies. Learn how the Hadoop connector allows you to leverage the power of MapReduce to process data sourced from your MongoDB cluster.
State of Florida Neo4j Graph Briefing - Cyber IAMNeo4j
Identity is based on relationships. Graph databases ensure those connections are current, scoped to actual requirements, and secure. David Rosenblum will discuss how customers from large financial institutions to smart home security systems are IAM enabled with Neo4j.
How Graph Databases efficiently store, manage and query connected data at s...jexp
Graph Databases try to make it easy for developers to leverage huge amounts of connected information for everything from routing to recommendations. Doing that poses a number of challenges on the implementation side. In this talk we want to look at the different storage, query and consistency approaches that are used behind the scenes. We’ll check out current and future solutions used in Neo4j and other graph databases for addressing global consistency, query and storage optimization, indexing and more and see which papers and research database developers take inspirations from.
The document is a presentation by Manash Ranjan Rautray on introducing graph databases and Neo4j. It discusses what a graph and graph database are, provides examples to illustrate graphs, and covers the basics of using Neo4j including its data model, query language Cypher, and real-world use cases for graph databases. The presentation aims to explain the concepts and capabilities of Neo4j for storing and querying connected data.
Polyglot Persistence with MongoDB and Neo4jCorie Pollock
Learn how to enhance your application by using Neo4j and MongoDB together. Polyglot persistence is the concept of taking advantage of the strengths of different database technologies to improve functionality and enhance your application. In this webinar we will examine some use cases where it makes sense to use a document database (MongoDB) with a graph database (Neo4j) in a single application. Specifically, we will show how MongoDB can be used to provide search and browsing functionality for a product catalog while using Neo4j to provide personalized product recommendations. Finally we will look at the Neo4j Doc Manager project which facilitates syncing data from MongoDB to Neo4j to make polyglot persistence with MongoDB and Neo4j much easier.
This document provides an overview of GraphDB and Neo4j. It discusses why graphs are useful for modeling connected data and common use cases. It also summarizes Neo4j's transactional graph database capabilities, performance advantages, and deployment options. Key topics covered include causal clustering, query planning, and driver and tooling support for developers.
This document discusses how search has evolved beyond traditional text search to support additional use cases like recommendations and analytics. It introduces LucidWorks' products like Solr and SiLK that leverage Hadoop to power search and discovery across large datasets. New features in Solr 4 like reduced memory usage and joins are highlighted. Demos are presented on applications in ecommerce, healthcare, and finance.
This document summarizes a webinar about importing crime data from Chicago into Neo4j. It discusses loading a CSV file of crime data into Neo4j using LOAD CSV and creating nodes and relationships. It also describes using Spark to preprocess the CSV into multiple Neo4j-formatted files and bulk loading them using the Neo4j Import tool. The document then covers enriching the graph with additional crime data from JSON and updating the graph with new crimes.
What's the Scoop on Hadoop? How It Works and How to WORK IT!MongoDB
MongoDB and Hadoop work powerfully together as complementary technologies. Learn how the Hadoop connector allows you to leverage the power of MapReduce to process data sourced from your MongoDB cluster.
State of Florida Neo4j Graph Briefing - Cyber IAMNeo4j
Identity is based on relationships. Graph databases ensure those connections are current, scoped to actual requirements, and secure. David Rosenblum will discuss how customers from large financial institutions to smart home security systems are IAM enabled with Neo4j.
MongoDB & Hadoop - Understanding Your Big DataMongoDB
Big Data is the evolution of supercomputing for commercial enterprise and governments. Originally the domain of companies operating at Internet scale, today Big Data connects organizations of all sizes with discovery about their patterns, and insights into their business.
But understanding the differences between the plethora of new technologies can be daunting. Graph / columnar / key value store / document are all called NoSQL, but which is best? How does Hadoop play in this ecosystem - its low cost and high efficiency have made it very popular, but how does it fit?
In this webinar, we will explore:
The full spectrum of Big Data
Hadoop and MongoDB: friends or frenemies?
Differences between Systems of Record and Systems of Engagement
MongoDB customer examples of Systems of Engagement
Chicago Solr Meetup - June 10th: This Ain't Your Parents' Search EngineLucidworks (Archived)
The document discusses how search has evolved beyond traditional keyword search to include more complex tasks like recommendations, classifications, and analytics using distributed technologies like Hadoop. It provides an overview of new capabilities in Lucene/Solr like reduced memory usage, pluggable codecs, and spatial search upgrades. LucidWorks offers products like Solr and SiLK that integrate with Hadoop and provide search and analytics capabilities across distributed data.
Buzz Moschetti presents on using MongoDB and Hadoop together for success with big data projects. He outlines a real-time directed content system that uses MongoDB for operational data and recommendations, Hadoop for batch analytics, and integrates the two with real-time updates. The system dynamically updates user profiles and recommendations based on user clicks and periodic re-analysis of all data in Hadoop. It provides both real-time and long-term analytics capabilities through this integrated architecture.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
A 1 hour intro to search, Apache Lucene and Solr, and LucidWorks Search. Contains a quick start with LucidWorks Search and a demo using financial data (See Github prj: http://bit.ly/lws-financial) as well as some basic vocab and search explanations
Social Media, Cloud Computing, Machine Learning, Open Source, and Big Data An...Open Analytics
This document discusses using social media, cloud computing, machine learning, open source, and big data analytics to analyze Twitter data. It describes how to collect tweets using the Twitter API, classify tweets in real-time using machine learning models on AWS, store classified tweets in MongoDB on AWS, and present results. Cost estimates for real-time classification of 1 million tweets per day are provided. Use cases described include tracking food poisoning reports and disease occurrence. Future directions discussed include developing turnkey services and linking to additional open data sources.
Spectator to Participant. Contributing to Cassandra (Patrick McFadin, DataSta...DataStax
Feeling the need to contribute something to Apache Cassandra? Maybe you want to help guide the future of your favorite database? Get off the sidelines and get in the game! It's easy to say but how do you even get started? I will outline some of the ways you can help contribute to Apache Cassandra from minor to major. If you don't have the time or ability to submit code, there are a lot of ways you can participate. What if you do want to write some code? I can walk you through the process of creating a patch and submitting for final approval. Got a great idea? I'll show you propose that to the community at large. Take it from me, participating is so much more fun than just watching the project from a distance. Time to jump in!
About the Speaker
Patrick McFadin Chief Evangelist, DataStax
Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.
GraphTour - Albelli: Running Neo4j on a large scale image platformNeo4j
This document discusses running Neo4j on a large-scale image platform. The platform analyzes and organizes over 511 million photos. Neo4j was chosen to model the graph relationships between photos, users, events and other metadata. The import process required processing 150 images per second to migrate 1.3 petabytes of data to the cloud within a tight deadline. The architecture uses CQRS and multiple Neo4j clusters for high performance and scalability. Ongoing work includes upgrading Neo4j, using APOC procedures, and developing recommendation and suggestion features.
Big Data: Guidelines and Examples for the Enterprise Decision MakerMongoDB
This document provides an overview of a real-time directed content system that uses MongoDB, Hadoop, and MapReduce. It describes:
- The key participants in the system and their roles in generating, analyzing, and operating on data
- An architecture that uses MongoDB for real-time user profiling and content recommendations, Hadoop for periodic analytics on user profiles and content tags, and MapReduce jobs to update the profiles
- How the system works over time to continuously update user profiles based on their interactions with content, rerun analytics daily to update tags and baselines, and make recommendations based on the updated profiles
- How the system supports both real-time and long-term analytics needs through this integrated approach.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But, oftentimes with RDBMS, performance degrades with the increasing number and levels of data relationships and data size.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Hermes: Free the Data! Distributed Computing with MongoDBMongoDB
Moving data throughout an organization is an art form. Whether mastering the art of ETL or building micro services, we are often left with either business logic embedded where it doesn't belong or monolithic apps that do too much. In this talk, we will show you how we built a persisted messaging bus to ‘Free the Data’ from the apps, making it available across the organization without having to write custom ETL code. This in turn makes it possible for business apps to be standalone, testable and more reliable. We will discuss the basic architecture and how it works, go through some code samples (server side and client side), and present some statistics and visualizations.
This document provides an overview of the Neo4j graph database platform. It discusses how Neo4j differs from traditional databases by efficiently storing and querying connected data. It outlines the key components of the Neo4j platform, including graph transactions, the Cypher query language, and driver APIs. The document also reviews recent improvements to Neo4j's administration capabilities, multi-cluster support, visualization tools, and graph algorithms library.
The document discusses MongoDB and Hadoop. It provides an overview of how MongoDB and Hadoop can be used together, including use cases in commerce, insurance and fraud detection. It describes the MongoDB Connector for Hadoop, which allows reading and writing to MongoDB from Hadoop tools like MapReduce, Pig and Hive. The document concludes with a demo of a movie recommendation platform that uses both MongoDB and Spark on Hadoop to power a movie browsing web application and generate recommendations.
Practice of building apache sharding sphere iincubator communityjixuan1989
This talk is introduce by Liang Zhang, who is a PPMC of Apache SahrdingSphere (incubating) project, at Apache Event at Tsinghua University in China.
Liang Zhang comes from JD.com.
About the Event:
The open source ecosystem plays more and more important role in the world. Open source software is widely used in operating systems, cloud computing, big data, artificial intelligence, and industrial Internet. Many companies have gradually increased their participation in the open source community. Developers with open source experience are increasingly valued and favored by large enterprises. The Apache Software Foundation is one of the most important open source communities, contributing a large number of valuable open source software and communities to the world.
The invited guests of this lecture are all from ASF community, including the chairman of the Apache Software Foundation, three Apache members, Top 5 Apache code committers (according to Apache annual report), the first Committer in the Hadoop project in China, several Apache project mentors or VPs, and many Apache Committers. They will tell you what the open source culture is, how to join the Apache open source community, and the Apache Way.
Ubiquitous Solr - A Database's Not-So-Evil Twin: Presented by Ayon Sinha, Wal...Lucidworks
This document discusses how Walmart uses Apache Solr as a "not-so-evil twin" to complement their source of truth database and help scale their data infrastructure. It describes how Walmart abstracts the complexity of managing databases, caches, search queries, and messaging to provide scalable querying across database shards. The use of Solr has allowed Walmart to offload queries, recurring reads, analytics
The document introduces MongoDB as an open source, high performance database that is a popular NoSQL option. It discusses how MongoDB stores data as JSON-like documents, supports dynamic schemas, and scales horizontally across commodity servers. MongoDB is seen as a good alternative to SQL databases for applications dealing with large volumes of diverse data that need to scale.
Modern architectures are moving away from a "one size fits all" approach. We are well aware that we need to use the best tools for the job. Given the large selection of options available today, chances are that you will end up managing data in MongoDB for your operational workload and with Spark for your high speed data processing needs.
Description: When we model documents or data structures there are some key aspects that need to be examined not only for functional and architectural purposes but also to take into consideration the distribution of data nodes, streaming capabilities, aggregation and queryability options and how we can integrate the different data processing software, like Spark, that can benefit from subtle but substantial model changes. A clear example is when embedding or referencing documents and their implications on high speed processing.
Over the course of this talk we will detail the benefits of a good document model for the operational workload. As well as what type of transformations we should incorporate in our document model to adjust for the high speed processing capabilities of Spark.
We will look into the different options that we have to connect these two different systems, how to model according to different workloads, what kind of operators we need to be aware of for top performance and what kind of design and architectures we should put in place to make sure that all of these systems work well together.
Over the course of the talk we will showcase different libraries that enable the integration between spark and MongoDB, such as MongoDB Hadoop Connector, Stratio Connector and MongoDB Spark Native Connector.
By the end of the talk I expect the attendees to have an understanding of:
How they connect their MongoDB clusters with Spark
Which use cases show a net benefit for connecting these two systems
What kind of architecture design should be considered for making the most of Spark + MongoDB
How documents can be modeled for better performance and operational process, while processing these data sets stored in MongoDB.
The talk is suitable for:
Developers that want to understand how to leverage Spark
Architects that want to integrate their existing MongoDB cluster and have real time high speed processing needs
Data scientists that know about Spark, are playing with Spark and want to integrate with MongoDB for their persistency layer
Full-Stack Development with JavaScript and NoSQLAaron Benton
This document discusses a presentation about full-stack development with JavaScript and NoSQL using Couchbase. It promotes an upcoming Couchbase Day event in Charlotte, NC on February 28th that will include presentations and hands-on labs on Couchbase capabilities. It also advertises the Couchbase Connect conference with workshops, sessions and opportunities to engage with Couchbase engineers and thought leaders. The document then provides an overview of Couchbase Server and demonstrations of building a full-stack app with Node.js, Koa, Vue.js and Couchbase.
Why microservices architectures drive exceptional customer experiencesDenis Wilson Souza Rosa
An introduction to why microservices is a mandatory architecture for flexible and highly scalable applications and which are some of the most common problems.
MongoDB & Hadoop - Understanding Your Big DataMongoDB
Big Data is the evolution of supercomputing for commercial enterprise and governments. Originally the domain of companies operating at Internet scale, today Big Data connects organizations of all sizes with discovery about their patterns, and insights into their business.
But understanding the differences between the plethora of new technologies can be daunting. Graph / columnar / key value store / document are all called NoSQL, but which is best? How does Hadoop play in this ecosystem - its low cost and high efficiency have made it very popular, but how does it fit?
In this webinar, we will explore:
The full spectrum of Big Data
Hadoop and MongoDB: friends or frenemies?
Differences between Systems of Record and Systems of Engagement
MongoDB customer examples of Systems of Engagement
Chicago Solr Meetup - June 10th: This Ain't Your Parents' Search EngineLucidworks (Archived)
The document discusses how search has evolved beyond traditional keyword search to include more complex tasks like recommendations, classifications, and analytics using distributed technologies like Hadoop. It provides an overview of new capabilities in Lucene/Solr like reduced memory usage, pluggable codecs, and spatial search upgrades. LucidWorks offers products like Solr and SiLK that integrate with Hadoop and provide search and analytics capabilities across distributed data.
Buzz Moschetti presents on using MongoDB and Hadoop together for success with big data projects. He outlines a real-time directed content system that uses MongoDB for operational data and recommendations, Hadoop for batch analytics, and integrates the two with real-time updates. The system dynamically updates user profiles and recommendations based on user clicks and periodic re-analysis of all data in Hadoop. It provides both real-time and long-term analytics capabilities through this integrated architecture.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
A 1 hour intro to search, Apache Lucene and Solr, and LucidWorks Search. Contains a quick start with LucidWorks Search and a demo using financial data (See Github prj: http://bit.ly/lws-financial) as well as some basic vocab and search explanations
Social Media, Cloud Computing, Machine Learning, Open Source, and Big Data An...Open Analytics
This document discusses using social media, cloud computing, machine learning, open source, and big data analytics to analyze Twitter data. It describes how to collect tweets using the Twitter API, classify tweets in real-time using machine learning models on AWS, store classified tweets in MongoDB on AWS, and present results. Cost estimates for real-time classification of 1 million tweets per day are provided. Use cases described include tracking food poisoning reports and disease occurrence. Future directions discussed include developing turnkey services and linking to additional open data sources.
Spectator to Participant. Contributing to Cassandra (Patrick McFadin, DataSta...DataStax
Feeling the need to contribute something to Apache Cassandra? Maybe you want to help guide the future of your favorite database? Get off the sidelines and get in the game! It's easy to say but how do you even get started? I will outline some of the ways you can help contribute to Apache Cassandra from minor to major. If you don't have the time or ability to submit code, there are a lot of ways you can participate. What if you do want to write some code? I can walk you through the process of creating a patch and submitting for final approval. Got a great idea? I'll show you propose that to the community at large. Take it from me, participating is so much more fun than just watching the project from a distance. Time to jump in!
About the Speaker
Patrick McFadin Chief Evangelist, DataStax
Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.
GraphTour - Albelli: Running Neo4j on a large scale image platformNeo4j
This document discusses running Neo4j on a large-scale image platform. The platform analyzes and organizes over 511 million photos. Neo4j was chosen to model the graph relationships between photos, users, events and other metadata. The import process required processing 150 images per second to migrate 1.3 petabytes of data to the cloud within a tight deadline. The architecture uses CQRS and multiple Neo4j clusters for high performance and scalability. Ongoing work includes upgrading Neo4j, using APOC procedures, and developing recommendation and suggestion features.
Big Data: Guidelines and Examples for the Enterprise Decision MakerMongoDB
This document provides an overview of a real-time directed content system that uses MongoDB, Hadoop, and MapReduce. It describes:
- The key participants in the system and their roles in generating, analyzing, and operating on data
- An architecture that uses MongoDB for real-time user profiling and content recommendations, Hadoop for periodic analytics on user profiles and content tags, and MapReduce jobs to update the profiles
- How the system works over time to continuously update user profiles based on their interactions with content, rerun analytics daily to update tags and baselines, and make recommendations based on the updated profiles
- How the system supports both real-time and long-term analytics needs through this integrated approach.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But, oftentimes with RDBMS, performance degrades with the increasing number and levels of data relationships and data size.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Hermes: Free the Data! Distributed Computing with MongoDBMongoDB
Moving data throughout an organization is an art form. Whether mastering the art of ETL or building micro services, we are often left with either business logic embedded where it doesn't belong or monolithic apps that do too much. In this talk, we will show you how we built a persisted messaging bus to ‘Free the Data’ from the apps, making it available across the organization without having to write custom ETL code. This in turn makes it possible for business apps to be standalone, testable and more reliable. We will discuss the basic architecture and how it works, go through some code samples (server side and client side), and present some statistics and visualizations.
This document provides an overview of the Neo4j graph database platform. It discusses how Neo4j differs from traditional databases by efficiently storing and querying connected data. It outlines the key components of the Neo4j platform, including graph transactions, the Cypher query language, and driver APIs. The document also reviews recent improvements to Neo4j's administration capabilities, multi-cluster support, visualization tools, and graph algorithms library.
The document discusses MongoDB and Hadoop. It provides an overview of how MongoDB and Hadoop can be used together, including use cases in commerce, insurance and fraud detection. It describes the MongoDB Connector for Hadoop, which allows reading and writing to MongoDB from Hadoop tools like MapReduce, Pig and Hive. The document concludes with a demo of a movie recommendation platform that uses both MongoDB and Spark on Hadoop to power a movie browsing web application and generate recommendations.
Practice of building apache sharding sphere iincubator communityjixuan1989
This talk is introduce by Liang Zhang, who is a PPMC of Apache SahrdingSphere (incubating) project, at Apache Event at Tsinghua University in China.
Liang Zhang comes from JD.com.
About the Event:
The open source ecosystem plays more and more important role in the world. Open source software is widely used in operating systems, cloud computing, big data, artificial intelligence, and industrial Internet. Many companies have gradually increased their participation in the open source community. Developers with open source experience are increasingly valued and favored by large enterprises. The Apache Software Foundation is one of the most important open source communities, contributing a large number of valuable open source software and communities to the world.
The invited guests of this lecture are all from ASF community, including the chairman of the Apache Software Foundation, three Apache members, Top 5 Apache code committers (according to Apache annual report), the first Committer in the Hadoop project in China, several Apache project mentors or VPs, and many Apache Committers. They will tell you what the open source culture is, how to join the Apache open source community, and the Apache Way.
Ubiquitous Solr - A Database's Not-So-Evil Twin: Presented by Ayon Sinha, Wal...Lucidworks
This document discusses how Walmart uses Apache Solr as a "not-so-evil twin" to complement their source of truth database and help scale their data infrastructure. It describes how Walmart abstracts the complexity of managing databases, caches, search queries, and messaging to provide scalable querying across database shards. The use of Solr has allowed Walmart to offload queries, recurring reads, analytics
The document introduces MongoDB as an open source, high performance database that is a popular NoSQL option. It discusses how MongoDB stores data as JSON-like documents, supports dynamic schemas, and scales horizontally across commodity servers. MongoDB is seen as a good alternative to SQL databases for applications dealing with large volumes of diverse data that need to scale.
Modern architectures are moving away from a "one size fits all" approach. We are well aware that we need to use the best tools for the job. Given the large selection of options available today, chances are that you will end up managing data in MongoDB for your operational workload and with Spark for your high speed data processing needs.
Description: When we model documents or data structures there are some key aspects that need to be examined not only for functional and architectural purposes but also to take into consideration the distribution of data nodes, streaming capabilities, aggregation and queryability options and how we can integrate the different data processing software, like Spark, that can benefit from subtle but substantial model changes. A clear example is when embedding or referencing documents and their implications on high speed processing.
Over the course of this talk we will detail the benefits of a good document model for the operational workload. As well as what type of transformations we should incorporate in our document model to adjust for the high speed processing capabilities of Spark.
We will look into the different options that we have to connect these two different systems, how to model according to different workloads, what kind of operators we need to be aware of for top performance and what kind of design and architectures we should put in place to make sure that all of these systems work well together.
Over the course of the talk we will showcase different libraries that enable the integration between spark and MongoDB, such as MongoDB Hadoop Connector, Stratio Connector and MongoDB Spark Native Connector.
By the end of the talk I expect the attendees to have an understanding of:
How they connect their MongoDB clusters with Spark
Which use cases show a net benefit for connecting these two systems
What kind of architecture design should be considered for making the most of Spark + MongoDB
How documents can be modeled for better performance and operational process, while processing these data sets stored in MongoDB.
The talk is suitable for:
Developers that want to understand how to leverage Spark
Architects that want to integrate their existing MongoDB cluster and have real time high speed processing needs
Data scientists that know about Spark, are playing with Spark and want to integrate with MongoDB for their persistency layer
Full-Stack Development with JavaScript and NoSQLAaron Benton
This document discusses a presentation about full-stack development with JavaScript and NoSQL using Couchbase. It promotes an upcoming Couchbase Day event in Charlotte, NC on February 28th that will include presentations and hands-on labs on Couchbase capabilities. It also advertises the Couchbase Connect conference with workshops, sessions and opportunities to engage with Couchbase engineers and thought leaders. The document then provides an overview of Couchbase Server and demonstrations of building a full-stack app with Node.js, Koa, Vue.js and Couchbase.
Why microservices architectures drive exceptional customer experiencesDenis Wilson Souza Rosa
An introduction to why microservices is a mandatory architecture for flexible and highly scalable applications and which are some of the most common problems.
Big Data LDN 2018: BIG DATA TOO SLOW? SPRINKLE IN SOME NOSQLMatt Stubbs
Date: 14th November 2018
Location: Customer Experience Theatre
Time: 11:50 - 12:20
Speaker: Perry Krug
Organisation: Couchbase
About: Who wants to see an ad today for the shoes they bought last week? Everyone knows that customer experience is driven by data: don't waste an opportunity to get them the right data at the right time. Real-time results are critical, but raw speed isn't everything: you need power and flexibility to react to changes on the fly. Come learn how market-leading enterprises are using Couchbase as their speed layer for ingestion, incremental view and presentation layers alongside Kafka, Spark and Hadoop to liberate their data lakes.
This talk introduces a new workflow for building your machine learning models using the capabilities of modern databases that support machine learning use cases natively. There is an overview of how machine learning models are being created today to how they could look in the near future.
This talk was given at Pyjamas 2021 held virtually on December 4 2021 (https://pyjamas.live/schedule/#session-8)
This talk introduces a new workflow for building your machine learning models using the capabilities of modern databases that support machine learning use cases natively. There is an overview of how machine learning models are being created today to how they could look in the near future.
This talk was given at PyCon Lithuania 2022 held in Vilnius, Lithuania on May 26, 2022 (https://pycon.lt/)
Couchbase Cloud No Equal (Rick Jacobs, Couchbase) Kafka Summit 2020HostedbyConfluent
This session will describe and demonstrate the longstanding integration between Couchbase Server and Apache Kafka and will include descriptions of both the mechanics of the integration and practical situations when combining these products is appropriate.
Couchbase and Apache Kafka - Bridging the gap between RDBMS and NoSQLDATAVERSITY
Thousands of companies, from Uber and Netflix to Goldman Sachs and Cisco, use Apache Kafka to transform and reshape their data architectures. Kafka is frequently used as the bridge between legacy RDBMS and new NoSQL database systems, effectively transforming SQL table data into JSON documents and vice versa. Many companies also use Kafka for business-critical applications that drive real-time stream processing and analytics, intersystem messaging, high-volume data ingestion, and operational metrics collection.
Couchbase and Kafka can be used together to address high throughput, distributed data management, and transformation challenges.
In this webinar we’ll explore:
Where Kafka fits into the big data ecosystem
How companies are using Kafka for both real-time processing and as a bus for data exchange
An example of how Kafka can bridge legacy RDBMS and new NoSQL database systems
Several real-world use case architectures
This document discusses analyzing social media data from Meetup.com using graph technologies. It describes retrieving data via the Meetup API, modeling the data as a graph, analyzing the graph using algorithms and tools like PGX and PGQL, and visualizing results in Cytoscape. Potential questions that could be answered include identifying influential people and groups, relationships between groups, and hot topics. The demo environment uses Oracle Big Data Lite with Oracle NoSQL Database to store the graph and analyze it.
Slides: Moving from a Relational Model to NoSQLDATAVERSITY
Businesses are quickly moving to NoSQL databases to power their modern applications. However, a technology migration involves risk, especially if you have to change your data model. What if you could host a relatively unmodified RDBMS schema on your NoSQL database, then optimize it over time?
We’ll show you how Couchbase makes it easy to:
• Use SQL for JSON to query your data and create joins
• Optimize indexes and perform HashMap queries
• Build applications and analysis with NoSQL
NoSQL Simplified: Schema vs. Schema-lessInfiniteGraph
A look at the many facets of schema-less approaches vs a rich schema approach, ranging from performance and query support to heterogeneity and code/data migration issues. Presented by Leon Guzenda, Founder, Objectivity
The document is a presentation about big data by Sanjay Sharma from Impetus Technologies. It discusses big data concepts and technologies like Hadoop, NoSQL, and MPP databases. It also covers big data tools and ecosystems, use cases, careers, and the impact of big data on IT and businesses. The presentation contains 41 slides covering these topics in detail with examples and diagrams.
Slides: NoSQL Data Modeling Using JSON Documents – A Practical ApproachDATAVERSITY
After three decades of relational data modeling, everyone’s pretty comfortable with schemas, tables, and entity-relationships. As more and more Global 2000 companies choose NoSQL databases to power their Digital Economy applications, they need to think about how to best model their data. How do they move from a constrained, table-driven model to an agile, flexible data model based on JSON documents?
This webinar is intended for architects and application developers who want to learn about new JSON document data modeling approaches, techniques, and best practices. This webinar will show you how to get started building a JSON document data model, how to migrate a table-based data model to JSON documents, and how to optimize your design to enable fast query performance.
This webinar will provide practical, experience-based advice and best practices for modeling JSON documents, including:
- When to embed or not embed objects in your JSON document
- Data modeling using a practical data access pattern approach
- Indexing your JSON documents
- Querying your data using N1QL (SQL for JSON)
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
My toaster stores data without SQL and without tables. But making a choice based on what something doesn’t have isn’t terribly useful. “NoSQL” is an increasingly inaccurate catch-all term that covers a lot of different types of data storage. Let’s make more sense of this new breed of database management systems and go beyond the buzzword. In this session, the four main data models that make up the NoSQL movement will be covered: key-value, document, columnar and graph. How they differ and when you might want to use each one will be discussed.
This session will be looking at the whole ecosystem, with a more detailed focus on Couchbase, Cassandra, Riak KV, and Neo4j.
Billions of Messages in Real Time: Why Paypal & LinkedIn Trust an Engagement ...confluent
(Bruno Simic, Solutions Engineer, Couchbase)
Breakout during Confluent’s streaming event in Munich. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
MySQL Day Paris 2018 - MySQL JSON Document StoreOlivier DASINI
NoSQL + SQL = MySQL
MySQL Document Store allows developers to work with SQL relational tables and schema-less JSON collections. To make that possible MySQL has created the X Dev API which puts a strong focus on CRUD by providing a fluent API allowing you to work with JSON documents in a natural way. The X Protocol is a highly extensible and is optimized for CRUD as well as SQL API operations.
MySQL Document store gives users maximum flexibility developing traditional SQL relational applications and NoSQL schema-free document database applications. This eliminates the need for a separate NoSQL document database. Developers can mix and match relational data and JSON documents in the same database as well as the same application. For example, both data models can be queried in the same application and results can be in table, tabular or JSON formats.
The MySQL Document Store architecture consists of the following components:
Native JSON Document Storage - MySQL provides a native JSON datatype is efficiently stored in binary with the ability to create virtual columns that can be indexed. JSON Documents are automatically validated.
X Plugin - The X Plugin enables MySQL to use the X Protocol and uses Connectors and the Shell to act as clients to the server.
X Protocol - The X Protocol is a new client protocol based on top of the Protobuf library, and works for both, CRUD and SQL operations.
X DevAPI - The X DevAPI is a new, modern, async developer API for CRUD and SQL operations on top of X Protocol. It introduces Collections as new Schema objects. Documents are stored in Collections and have their dedicated CRUD operation set.
MySQL Shell - The MySQL Shell is an interactive Javascript, Python, or SQL interface supporting development and administration for the MySQL Server. You can use the MySQL Shell to perform data queries and updates as well as various administration operations.
MySQL Connectors - The following MySQL Connectors support the X Protocol and enable you to use X DevAPI in your chosen language.
MySQL Connector/Node.js
MySQL Connector/PHP
MySQL Connector/Python
MySQL Connector/J
MySQL Connector/NET
MySQL Connector/C++
Building Enterprise-Ready Knowledge Graph Applications in the CloudPeter Haase
The document provides an agenda for a workshop on building enterprise-ready knowledge graph applications in the cloud. The workshop will cover understanding knowledge graphs and related technologies, setting up a knowledge graph architecture on Amazon Neptune for scalable storage and querying, and using the metaphactory platform to rapidly build applications and APIs. Attendees will learn concepts for maintaining, querying and searching knowledge graphs, and building end-user and developer applications on top of knowledge graphs. The tutorial will include hands-on demonstrations and exercises to set up a small knowledge graph application.
Big Data and NoSQL for Database and BI ProsAndrew Brust
This document provides an agenda and overview for a conference session on Big Data and NoSQL for database and BI professionals held from April 10-12 in Chicago, IL. The session will include an overview of big data and NoSQL technologies, then deeper dives into Hadoop, NoSQL databases like HBase, and tools like Hive, Pig, and Sqoop. There will also be demos of technologies like HDInsight, Elastic MapReduce, Impala, and running MapReduce jobs.
Similar to No sql data-storage for-your-ios-apps-using-couchbase-mobile (20)
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.