Running complex data queries in a distributed systemArangoDB Database
With the always-growing amount of data, it is getting increasingly hard to store and get it back efficiently. While the first versions of distributed databases have put all the burden of sharding on the application code, there are now some smarter solutions that handle most of the data distribution and resilience tasks inside the database.
This poses some interesting questions, e.g.
- how are other than by-primary-key queries actually organized and executed in a distributed system, so that they can run most efficiently?
- how do the contemporary distributed databases actually achieve transactional semantics for non-trivial operations that affect different shards/servers?
This talk will give an overview of these challenges and the available solutions that some open source distributed databases have picked to solve them.
Elasticsearch and Symfony Integration - Debarko DeDebarko De
This document provides an overview of Elasticsearch and how to integrate it with Symfony. It discusses how Elasticsearch is a search engine that uses JSON documents and distributed indexing, while SQL is a relational database. It then covers how to install the Elasticsearch PHP client, connect to Elasticsearch from Symfony, perform queries, create and manage indexes, index and search documents, and delete documents.
Searching Relational Data with Elasticsearchsirensolutions
Second Galway Data Meetup, 29th April 2015
Elasticsearch was originally developed for searching flat documents. However, as real world data is inherently more complex, e.g., nested json data, relational data, interconnected documents and entities, Elasticsearch quickly evolves to support more advanced search scenarios. In this presentation, we will review existing features and plugins to support such scenarios, discuss their advantages and disadvantages, and understand which one is more appropriate for a particular scenario.
These are the slides to the webinar about Custom Pregel algorithms in ArangoDB https://youtu.be/DWJ-nWUxsO8. It provides a brief introduction to the capabilities and use cases for Pregel.
This document summarizes Hibernate, an object-relational mapping tool for Java. It discusses how Hibernate provides APIs for storing and retrieving Java objects from a database, maps Java classes to database tables, and minimizes database access through caching and fetching strategies. The document also includes examples of Hibernate configuration files and mappings that define relationships between entities.
This document discusses Elasticsearch and provides examples of its real-world uses and basic functionality. It contains:
1) An overview of Elasticsearch and how it can be used for full-text search, analytics, and structured querying of large datasets. Dell and The Guardian are discussed as real-world use cases.
2) Explanations of basic Elasticsearch concepts like indexes, types, mappings, and inverted indexes. Examples of indexing, updating, and deleting documents.
3) Details on searching and filtering documents through queries, filters, aggregations, and aliases. Query DSL and examples of common queries like term, match, range are provided.
4) A discussion of potential data modeling designs for indexing user
Elasticsearch Introduction to Data model, Search & AggregationsAlaa Elhadba
An overview of Elasticsearch features and explains performing smart search, data aggregations, and relevancy through scoring functions. How Elasticsearch works as a distributed scalable data storage. Finally, showcasing some use cases that are currently becoming core functionalities in Zalando.
Elasticsearch is a distributed, open source search and analytics engine. It allows storing and searching of documents of any schema in real-time. Documents are organized into indices which can contain multiple types of documents. Indices are partitioned into shards and replicas to allow horizontal scaling and high availability. The document consists of a JSON object which is indexed and can be queried using a RESTful API.
Elasticsearch is an open-source, distributed search and analytics engine built on Apache Lucene. It allows storing, searching, and analyzing large volumes of data quickly and in near real-time. Key concepts include being schema-free, document-oriented, and distributed. Indices can be created to store different types of documents. Mapping defines how documents are indexed. Documents can be added, retrieved, updated, and deleted via RESTful APIs. Queries can be used to search for documents matching search criteria. Faceted search provides aggregated data based on search queries. Elastica provides a PHP client for interacting with Elasticsearch.
Optiq is a dynamic query planning framework. It can potentially help integrate Pentaho Mondrian and Kettle with various SQL, NoSQL and BigData data sources.
How to integrate Splunk with any data solutionJulian Hyde
A presentation Julian Hyde gave to the Splunk 2012 User conference in Las Vegas, Tue 2012/9/11. Julian demonstrated a new technology called Optiq, described how it could be used to integrate data in Splunk with other systems, and demonstrated several queries accessing data in Splunk via SQL and JDBC.
SH 2 - SES 3 - MongoDB Aggregation Framework.pptxMongoDB
The document provides an overview of MongoDB's aggregation framework. It explains that the aggregation framework allows users to process data from MongoDB collections and databases using aggregation pipeline stages similar to data aggregation operations in SQL like GROUP BY, JOIN, and filtering. The document then discusses several aggregation pipeline stages like $project, $lookup, $match, and $group. It also provides an example comparing an aggregation pipeline to a SQL query with GROUP BY and HAVING.
The document discusses MongoDB, a document-oriented NoSQL database. It covers some key features of MongoDB including rich document queries, indexing for performance, replication for availability, auto-sharding for scalability, and geospatial indexing. It also provides MongoDB equivalents to SQL concepts and provides examples of CRUD operations and queries using aggregation, indexing, map-reduce, and replication sets.
Battle of the Giants - Apache Solr vs. Elasticsearch (ApacheCon)Sematext Group, Inc.
The document compares the Apache Solr and ElasticSearch search platforms. It discusses their architectures, including SolrCloud and ElasticSearch's cluster architecture. It also covers topics like indexing, querying, partial document updates, analysis chains, multilingual support, and other features. Overall, the document provides a detailed comparison of the two open source search technologies.
The document compares and contrasts the Apache Solr and Elasticsearch search engines. It discusses their approaches to indexing structure, configuration, discovery, querying, filtering, faceting, data handling, updates, and cluster monitoring. While both use Lucene for indexing and querying, Elasticsearch has a more dynamic schema, easier configuration changes, and integrated shard allocation controls compared to Solr's more static configuration and external Zookeeper integration.
Use Cases for Elastic Search PercolatorMaxim Shelest
The document discusses the use of Elastic Search's percolator feature. The percolator allows storing queries in an index and then indexing documents to retrieve matching queries. This is opposite of traditional search where documents are indexed and queries retrieve them. The percolator works in real-time so queries can immediately be used. Examples are provided of adding percolator queries and using them to match documents. Additional use cases discussed include alerting, notifications, and monitoring systems.
Elasticsearch is an open-source, distributed, real-time document indexer with support for online analytics. It has features like a powerful REST API, schema-less data model, full distribution and high availability, and advanced search capabilities. Documents are indexed into indexes which contain mappings and types. Queries retrieve matching documents from indexes. Analysis converts text into searchable terms using tokenizers, filters, and analyzers. Documents are distributed across shards and replicas for scalability and fault tolerance. The REST APIs can be used to index, search, and inspect the cluster.
Cool bonsai cool - an introduction to ElasticSearchclintongormley
An introduction to Clinton Gormley and the search engine Elasticsearch. It discusses how Elasticsearch works by tokenizing text, creating an inverted index, and using relevance scoring. It also summarizes how to install and use Elasticsearch for indexing, retrieving, and searching documents.
NSURLSession is the main networking API provided by Foundation that allows issuing HTTP requests and handling responses. Popular third party libraries like Alamofire and Moya provide abstractions over NSURLSession to simplify networking code. JSON parsing in Swift requires using third party libraries like Gloss to implement type checking and avoid callback hell when handling JSON responses.
This document provides examples of using aggregations in Elasticsearch to calculate statistics and group documents. It shows terms, range, and histogram facets/aggregations to group documents by fields like state or population range and calculate statistics like average density. It also demonstrates nesting aggregations to first group by one field like state and then further group and calculate stats within each state group. Finally it lists the built-in aggregation bucketizers and calculators available in Elasticsearch.
This document compares the performance and scalability of Elasticsearch and Solr for two use cases: product search and log analytics. For product search, both products performed well at high query volumes, but Elasticsearch handled the larger video dataset faster. For logs, Elasticsearch performed better by using time-based indices across hot and cold nodes to isolate newer and older data. In general, configuration was found to impact performance more than differences between the products. Proper testing with one's own data is recommended before making conclusions.
This document provides an overview of different methods for migrating content to Drupal using the Migrate module. It discusses possible methods like doing it manually, using Node Export, or Feeds. The bulk of the document then focuses on using the Migrate module, outlining the main steps which include implementing a migration class, describing the source and mappings, and using Drush commands to import/rollback content. Additional tips are provided around mapping fields, handling additional data, and available resources.
Accelerating distributed joins in Apache Hive: Runtime filtering enhancementsPanagiotis Garefalakis
Apache Hive is an open-source relational database system that is widely adopted by several organizations for big data analytic workloads. It combines traditional MPP (massively parallel processing) techniques with more recent cloud computing concepts to achieve the increased scalability and high performance needed by modern data intensive applications. Even though it was originally tailored towards long running data warehousing queries, its architecture recently changed with the introduction of LLAP (Live Long and Process) layer. Instead of regular containers, LLAP utilizes long-running executors to exploit data sharing and caching possibilities within and across queries. Executors eliminate unnecessary disk IO overhead and thus reduce the latency of interactive BI (business intelligence) queries by orders of magnitude. However, as container startup cost and IO overhead is now minimized, the need to effectively utilize memory and CPU resources across long-running executors in the cluster is becoming increasingly essential. For instance, in a variety of production workloads, we noticed that the memory bandwidth of early decoding all table columns for every row, even when this row is dropped later on, is starting to overwhelm the performance of single query execution. In this talk, we focus on some of the optimizations we introduced in Hive 4.0 to increase CPU efficiency and save memory allocations. In particular, we describe the lazy decoding (or row-level filtering) and composite bloom-filters optimizations that greatly improve the performance of queries containing broadcast joins, reducing their runtime by up to 50%. Over several production and synthetic workloads, we show the benefit of the newly introduced optimizations as part of Cloudera’s cloud-native Data Warehouse engine. At the same time, the community can directly benefit from the presented features as are they 100% open-source!
This document provides an overview of D3, an open-source JavaScript library for producing dynamic, interactive data visualizations in web browsers. It describes D3's capabilities for creating SVG or HTML elements based on input data, binding data to DOM elements, and controlling visual attributes like position, color and size using the data. Examples of bar charts, treemaps and node-link graphs are given. The document also demonstrates basic usage of D3 for selecting elements, binding data, and using scales to map data values to pixel values for visual properties. Links to additional D3 tutorials are provided.
Talk given for the #phpbenelux user group, March 27th in Gent (BE), with the goal of convincing developers that are used to build php/mysql apps to broaden their horizon when adding search to their site. Be sure to also have a look at the notes for the slides; they explain some of the screenshots, etc.
An accompanying blog post about this subject can be found at http://www.jurriaanpersyn.com/archives/2013/11/18/introduction-to-elasticsearch/
The document discusses how MapReduce can be used for various tasks related to search engines, including detecting duplicate web pages, processing document content, building inverted indexes, and analyzing search query logs. It provides examples of MapReduce jobs for normalizing document text, extracting entities, calculating ranking signals, and indexing individual words, phrases, stems and synonyms.
Catalyst is a web framework for Perl that allows developers to build dynamic web applications in a modular, reusable way. It utilizes common Perl techniques like Moose, DBIx::Class and Template Toolkit to handle tasks like object modeling, database access and view rendering. Catalyst applications can be built in a model-view-controller style to separate application logic, data access and presentation layers. This framework provides a standard way to write reusable code and build web UIs for tasks like system administration and automation.
Relevance trilogy may dream be with you! (dec17)Woonsan Ko
Introducing new BloomReach Experience Plugins which changes the game of DREAM (Digital Relevance Experience & Agility Management), to increase productivity and business agility.
Elasticsearch Introduction to Data model, Search & AggregationsAlaa Elhadba
An overview of Elasticsearch features and explains performing smart search, data aggregations, and relevancy through scoring functions. How Elasticsearch works as a distributed scalable data storage. Finally, showcasing some use cases that are currently becoming core functionalities in Zalando.
Elasticsearch is a distributed, open source search and analytics engine. It allows storing and searching of documents of any schema in real-time. Documents are organized into indices which can contain multiple types of documents. Indices are partitioned into shards and replicas to allow horizontal scaling and high availability. The document consists of a JSON object which is indexed and can be queried using a RESTful API.
Elasticsearch is an open-source, distributed search and analytics engine built on Apache Lucene. It allows storing, searching, and analyzing large volumes of data quickly and in near real-time. Key concepts include being schema-free, document-oriented, and distributed. Indices can be created to store different types of documents. Mapping defines how documents are indexed. Documents can be added, retrieved, updated, and deleted via RESTful APIs. Queries can be used to search for documents matching search criteria. Faceted search provides aggregated data based on search queries. Elastica provides a PHP client for interacting with Elasticsearch.
Optiq is a dynamic query planning framework. It can potentially help integrate Pentaho Mondrian and Kettle with various SQL, NoSQL and BigData data sources.
How to integrate Splunk with any data solutionJulian Hyde
A presentation Julian Hyde gave to the Splunk 2012 User conference in Las Vegas, Tue 2012/9/11. Julian demonstrated a new technology called Optiq, described how it could be used to integrate data in Splunk with other systems, and demonstrated several queries accessing data in Splunk via SQL and JDBC.
SH 2 - SES 3 - MongoDB Aggregation Framework.pptxMongoDB
The document provides an overview of MongoDB's aggregation framework. It explains that the aggregation framework allows users to process data from MongoDB collections and databases using aggregation pipeline stages similar to data aggregation operations in SQL like GROUP BY, JOIN, and filtering. The document then discusses several aggregation pipeline stages like $project, $lookup, $match, and $group. It also provides an example comparing an aggregation pipeline to a SQL query with GROUP BY and HAVING.
The document discusses MongoDB, a document-oriented NoSQL database. It covers some key features of MongoDB including rich document queries, indexing for performance, replication for availability, auto-sharding for scalability, and geospatial indexing. It also provides MongoDB equivalents to SQL concepts and provides examples of CRUD operations and queries using aggregation, indexing, map-reduce, and replication sets.
Battle of the Giants - Apache Solr vs. Elasticsearch (ApacheCon)Sematext Group, Inc.
The document compares the Apache Solr and ElasticSearch search platforms. It discusses their architectures, including SolrCloud and ElasticSearch's cluster architecture. It also covers topics like indexing, querying, partial document updates, analysis chains, multilingual support, and other features. Overall, the document provides a detailed comparison of the two open source search technologies.
The document compares and contrasts the Apache Solr and Elasticsearch search engines. It discusses their approaches to indexing structure, configuration, discovery, querying, filtering, faceting, data handling, updates, and cluster monitoring. While both use Lucene for indexing and querying, Elasticsearch has a more dynamic schema, easier configuration changes, and integrated shard allocation controls compared to Solr's more static configuration and external Zookeeper integration.
Use Cases for Elastic Search PercolatorMaxim Shelest
The document discusses the use of Elastic Search's percolator feature. The percolator allows storing queries in an index and then indexing documents to retrieve matching queries. This is opposite of traditional search where documents are indexed and queries retrieve them. The percolator works in real-time so queries can immediately be used. Examples are provided of adding percolator queries and using them to match documents. Additional use cases discussed include alerting, notifications, and monitoring systems.
Elasticsearch is an open-source, distributed, real-time document indexer with support for online analytics. It has features like a powerful REST API, schema-less data model, full distribution and high availability, and advanced search capabilities. Documents are indexed into indexes which contain mappings and types. Queries retrieve matching documents from indexes. Analysis converts text into searchable terms using tokenizers, filters, and analyzers. Documents are distributed across shards and replicas for scalability and fault tolerance. The REST APIs can be used to index, search, and inspect the cluster.
Cool bonsai cool - an introduction to ElasticSearchclintongormley
An introduction to Clinton Gormley and the search engine Elasticsearch. It discusses how Elasticsearch works by tokenizing text, creating an inverted index, and using relevance scoring. It also summarizes how to install and use Elasticsearch for indexing, retrieving, and searching documents.
NSURLSession is the main networking API provided by Foundation that allows issuing HTTP requests and handling responses. Popular third party libraries like Alamofire and Moya provide abstractions over NSURLSession to simplify networking code. JSON parsing in Swift requires using third party libraries like Gloss to implement type checking and avoid callback hell when handling JSON responses.
This document provides examples of using aggregations in Elasticsearch to calculate statistics and group documents. It shows terms, range, and histogram facets/aggregations to group documents by fields like state or population range and calculate statistics like average density. It also demonstrates nesting aggregations to first group by one field like state and then further group and calculate stats within each state group. Finally it lists the built-in aggregation bucketizers and calculators available in Elasticsearch.
This document compares the performance and scalability of Elasticsearch and Solr for two use cases: product search and log analytics. For product search, both products performed well at high query volumes, but Elasticsearch handled the larger video dataset faster. For logs, Elasticsearch performed better by using time-based indices across hot and cold nodes to isolate newer and older data. In general, configuration was found to impact performance more than differences between the products. Proper testing with one's own data is recommended before making conclusions.
This document provides an overview of different methods for migrating content to Drupal using the Migrate module. It discusses possible methods like doing it manually, using Node Export, or Feeds. The bulk of the document then focuses on using the Migrate module, outlining the main steps which include implementing a migration class, describing the source and mappings, and using Drush commands to import/rollback content. Additional tips are provided around mapping fields, handling additional data, and available resources.
Accelerating distributed joins in Apache Hive: Runtime filtering enhancementsPanagiotis Garefalakis
Apache Hive is an open-source relational database system that is widely adopted by several organizations for big data analytic workloads. It combines traditional MPP (massively parallel processing) techniques with more recent cloud computing concepts to achieve the increased scalability and high performance needed by modern data intensive applications. Even though it was originally tailored towards long running data warehousing queries, its architecture recently changed with the introduction of LLAP (Live Long and Process) layer. Instead of regular containers, LLAP utilizes long-running executors to exploit data sharing and caching possibilities within and across queries. Executors eliminate unnecessary disk IO overhead and thus reduce the latency of interactive BI (business intelligence) queries by orders of magnitude. However, as container startup cost and IO overhead is now minimized, the need to effectively utilize memory and CPU resources across long-running executors in the cluster is becoming increasingly essential. For instance, in a variety of production workloads, we noticed that the memory bandwidth of early decoding all table columns for every row, even when this row is dropped later on, is starting to overwhelm the performance of single query execution. In this talk, we focus on some of the optimizations we introduced in Hive 4.0 to increase CPU efficiency and save memory allocations. In particular, we describe the lazy decoding (or row-level filtering) and composite bloom-filters optimizations that greatly improve the performance of queries containing broadcast joins, reducing their runtime by up to 50%. Over several production and synthetic workloads, we show the benefit of the newly introduced optimizations as part of Cloudera’s cloud-native Data Warehouse engine. At the same time, the community can directly benefit from the presented features as are they 100% open-source!
This document provides an overview of D3, an open-source JavaScript library for producing dynamic, interactive data visualizations in web browsers. It describes D3's capabilities for creating SVG or HTML elements based on input data, binding data to DOM elements, and controlling visual attributes like position, color and size using the data. Examples of bar charts, treemaps and node-link graphs are given. The document also demonstrates basic usage of D3 for selecting elements, binding data, and using scales to map data values to pixel values for visual properties. Links to additional D3 tutorials are provided.
Talk given for the #phpbenelux user group, March 27th in Gent (BE), with the goal of convincing developers that are used to build php/mysql apps to broaden their horizon when adding search to their site. Be sure to also have a look at the notes for the slides; they explain some of the screenshots, etc.
An accompanying blog post about this subject can be found at http://www.jurriaanpersyn.com/archives/2013/11/18/introduction-to-elasticsearch/
The document discusses how MapReduce can be used for various tasks related to search engines, including detecting duplicate web pages, processing document content, building inverted indexes, and analyzing search query logs. It provides examples of MapReduce jobs for normalizing document text, extracting entities, calculating ranking signals, and indexing individual words, phrases, stems and synonyms.
Catalyst is a web framework for Perl that allows developers to build dynamic web applications in a modular, reusable way. It utilizes common Perl techniques like Moose, DBIx::Class and Template Toolkit to handle tasks like object modeling, database access and view rendering. Catalyst applications can be built in a model-view-controller style to separate application logic, data access and presentation layers. This framework provides a standard way to write reusable code and build web UIs for tasks like system administration and automation.
Relevance trilogy may dream be with you! (dec17)Woonsan Ko
Introducing new BloomReach Experience Plugins which changes the game of DREAM (Digital Relevance Experience & Agility Management), to increase productivity and business agility.
Boston Computing Review - Java Server PagesJohn Brunswick
1) JSP (Java Server Pages) is a core technology for developing web applications in Java and provides a simple way to add dynamic content to web pages through Java code and reusable components.
2) JSP pages are compiled into Java servlets that generate responses, allowing developers to focus on presentation logic while business logic is encapsulated in reusable objects.
3) Key elements of JSP include scriptlets for Java code, directives for configuration, expressions for output, and implicit objects for accessing request parameters and session information.
1) JSP (Java Server Pages) is a core technology for developing web applications in Java and provides a simple way to add dynamic content to web pages through Java code and reusable components.
2) JSP pages are compiled into Java servlets that generate responses, allowing developers to focus on presentation logic while business logic can be encapsulated in reusable objects.
3) Key elements of JSP include scriptlets for inline Java code, directives for configuration, expressions for output, declarations for methods, and implicit objects to access request and session information.
Learning To Run - XPages for Lotus Notes Client DevelopersKathy Brown
You’re an experienced Lotus Notes developer. You’ve been doing “classic” development for years. You know LotusScript better than your native language. You know @Formula like the back of your hand. But when it comes to Xpages and Javascript, you feel like you’re learning to walk all over again. This session will cover some tips and tricks to get you up and running in Xpages. Learn how to translate what you already know, into what you need to know for Xpages. Find out where to get the information to be just as skillful at Xpages as you are with Notes client development.
Nhibernatethe Orm For Net Platform 1226744632929962 8Nicolas Thon
The document provides an introduction to object-relational mapping (ORM) and NHibernate. It discusses ORM techniques for converting data between object-oriented programming languages and relational databases. It then provides an overview of NHibernate, an open source ORM framework for .NET, including its basic concepts, configuration, querying capabilities, and additional reading.
This document provides an introduction to MongoDB, including what it is, why it may be used, and how its data model works. Some key points:
- MongoDB is a non-relational database that stores data in flexible, JSON-like documents rather than fixed schema tables.
- It offers advantages like dynamic schemas, embedding of related data, and fast performance at large scales.
- Data is organized into collections of documents, which can contain sub-documents to represent one-to-many relationships without joins.
- Queries use JSON-like syntax to search for patterns in documents, and indexes can improve performance.
Using Spring Data and MongoDB with Cloud FoundryChris Harris
- The document discusses using Spring and MongoDB with Cloud Foundry. It covers challenges with data access like scaling horizontally and heterogeneous data needs.
- Spring Framework provides data access support for MongoDB through Spring Data. It includes APIs, object mapping, and generic repositories that improve productivity.
- Spring Data for MongoDB includes MongoTemplate for direct access, converters for mapping documents to POJOs, and MongoRepository for common CRUD operations. Examples demonstrate basic usage.
- The document shows how to integrate MongoDB documents with JPA entities for cross-store domain models and provides an example of saving to MongoDB via Spring on Cloud Foundry.
The web has changed! Users spend more time on mobile than on desktops and they expect to have an amazing user experience on both platforms. APIs are the heart of the new web as the central point of access data, encapsulating logic and providing the same data and same features for desktops and mobiles.
In this talk, I will show you how in only 45 minutes we can create full REST API, with documentation and admin application build with React.
This document provides an introduction to object-relational mapping (ORM) and NHibernate. It discusses ORM techniques for converting data between object-oriented programming languages and relational databases. The document then provides an overview of NHibernate, an open source ORM framework for .NET, including its basic concepts, configuration, querying capabilities, and additional reading.
Itemscript, a specification for RESTful JSON integration{item:foo}
The document discusses Itemscript, a declarative language based on JSON that separates design from construction for simple yet powerful application development. Itemscript uses JSON schemas and application markup to define application structure and behavior declaratively. It aims to provide business agility through lean development using declarations that allow developers and users to iteratively discover needs and evolve applications.
Rapid and Scalable Development with MongoDB, PyMongo, and MingRick Copeland
This talk, given at PyGotham 2011, will teach you techniques using the popular NoSQL database MongoDB and the Python library Ming to write maintainable, high-performance, and scalable applications. We will cover everything you need to become an effective Ming/MongoDB developer from basic PyMongo queries to high-level object-document mapping setups in Ming.
Tuning and optimizing webcenter spaces application white paperVinay Kumar
This white paper focuses on Oracle WebCenter Spaces performance problem and analysis after post production deployment. We will tune JVM ( JRocket). Webcenter Portal, Webcenter content and ADF task flow.
Spring Data provides a unified model for data access and management across different data access technologies such as relational, non-relational and cloud data stores. It includes utilities such as repository support, object mapping and templating to simplify data access layers. Spring Data MongoDB provides specific support for MongoDB including configuration, mapping, querying and integration with Spring MVC. It simplifies MongoDB access through MongoTemplate and provides a repository abstraction layer.
Designing CakePHP plugins for consuming APIsNeil Crookes
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like depression and anxiety.
Drupal 7 introduces the entity field and query APIs which allow content to be stored and queried in a flexible way across different database backends like MySQL and MongoDB. This makes it possible to write database agnostic queries and supports fields as first class objects that can be attached to entities like nodes. The new APIs provide a standard way to work with content that improves scalability and allows sites to choose the optimal database storage for their needs.
Hibernate is an object-relational mapping tool that allows developers to work with relational data (like SQL databases) using object-oriented programming languages like Java. It eliminates manual data handling code by directly mapping Java objects to database tables. This reduces development time and effort. Hibernate configuration involves setting up XML mapping files that describe how Java classes and their fields are mapped to database tables and columns. It provides a simpler object-oriented approach for handling data persistence tasks versus direct SQL/JDBC coding.
The document provides an overview of the MVC pattern and how it is implemented in Symfony. It discusses how Symfony separates code into models, views, and controllers and layers these components. It also describes common Symfony structures like modules, actions, and templates as well as tools like parameter holders, constants, and autoloading that are frequently used.
This document summarizes a presentation about monitoring Elasticsearch in OpenShift. It discusses the challenges of running Elasticsearch in OpenShift due to limited resources, and how Prometheus and Grafana are used to monitor Elasticsearch metrics. It also describes the Elasticsearch Operator which manages Elasticsearch clusters through custom resource definitions. Skilled personnel are needed to maintain the cluster through upgrades and troubleshooting.
This document discusses React and Flux. It introduces React as a JavaScript library created by Facebook for building user interfaces. Flux is described as an application architecture pattern for avoiding complex event chains. Key aspects of React covered include using JSX, the virtual DOM for efficient updates, and integrating with other libraries. The document emphasizes thinking about data flow and putting it in good order using Flux. It concludes by recommending enjoying life on a sunny day.
The document discusses Elasticsearch, an open source, distributed, RESTful search and analytics engine. It covers full-text search capabilities via a REST API and plugins, Elasticsearch's distributed nature which allows sharding and replication of data, and data analytics features like facets and aggregations. Real-world use cases at JBoss.org are presented, where Elasticsearch powers the search.jboss.org website and Searchisko, a custom search solution for JBoss content.
An Introduction to Apache Hadoop, Mahout and HBaseLukas Vlcek
Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It implements the MapReduce programming model pioneered by Google and a distributed file system (HDFS). Mahout builds machine learning libraries on top of Hadoop. HBase is a non-relational distributed database modeled after Google's BigTable that provides random access and real-time read/write capabilities. These projects are used by many large companies for large-scale data processing and analytics tasks.
Lukas Vlcek built a search app for public mailing lists in 15 minutes using ElasticSearch. The app allows users to search mailing lists, filter results by facets like date and author, and view document previews with highlighted search terms. Key challenges included parsing email structure and content, normalizing complex email subjects, identifying conversation threads, and determining how to handle quoted content and author disambiguation. The search application and a monitoring tool for ElasticSearch called BigDesk will be made available on GitHub.
This document provides an overview of ElasticSearch, an open source, distributed, RESTful search and analytics engine. It discusses how ElasticSearch is highly available, distributed across shards and replicas, and can be deployed in the cloud. Examples are provided showing how to index and search data via the REST API and retrieve cluster health information. Advanced features like faceting, scripting, parent/child relationships, and versioning are also summarized.
Lukáš Vlček gave a presentation on January 25th, 2010 about JBoss Snowdrop. Snowdrop is a utility package that contains JBoss-specific extensions to the Spring Framework that allow developers to easily develop, deploy, and run Spring-based applications on JBoss Application Server while utilizing its Java EE services. Snowdrop provides features such as a Spring deployer, support for the JBoss virtual file system, and the ability to inject Spring beans into EJB3.
Procurement Insights Cost To Value Guide.pptxJon Hansen
Procurement Insights integrated Historic Procurement Industry Archives, serves as a powerful complement — not a competitor — to other procurement industry firms. It fills critical gaps in depth, agility, and contextual insight that most traditional analyst and association models overlook.
Learn more about this value- driven proprietary service offering here.
HCL Nomad Web – Best Practices and Managing Multiuser Environmentspanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-and-managing-multiuser-environments/
HCL Nomad Web is heralded as the next generation of the HCL Notes client, offering numerous advantages such as eliminating the need for packaging, distribution, and installation. Nomad Web client upgrades will be installed “automatically” in the background. This significantly reduces the administrative footprint compared to traditional HCL Notes clients. However, troubleshooting issues in Nomad Web present unique challenges compared to the Notes client.
Join Christoph and Marc as they demonstrate how to simplify the troubleshooting process in HCL Nomad Web, ensuring a smoother and more efficient user experience.
In this webinar, we will explore effective strategies for diagnosing and resolving common problems in HCL Nomad Web, including
- Accessing the console
- Locating and interpreting log files
- Accessing the data folder within the browser’s cache (using OPFS)
- Understand the difference between single- and multi-user scenarios
- Utilizing Client Clocking
Complete Guide to Advanced Logistics Management Software in Riyadh.pdfSoftware Company
Explore the benefits and features of advanced logistics management software for businesses in Riyadh. This guide delves into the latest technologies, from real-time tracking and route optimization to warehouse management and inventory control, helping businesses streamline their logistics operations and reduce costs. Learn how implementing the right software solution can enhance efficiency, improve customer satisfaction, and provide a competitive edge in the growing logistics sector of Riyadh.
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john
Analyze the growth of meme coins from mere online jokes to potential assets in the digital economy. Explore the community, culture, and utility as they elevate themselves to a new era in cryptocurrency.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul
Artificial intelligence is changing how businesses operate. Companies are using AI agents to automate tasks, reduce time spent on repetitive work, and focus more on high-value activities. Noah Loul, an AI strategist and entrepreneur, has helped dozens of companies streamline their operations using smart automation. He believes AI agents aren't just tools—they're workers that take on repeatable tasks so your human team can focus on what matters. If you want to reduce time waste and increase output, AI agents are the next move.
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB
Want to learn practical tips for designing systems that can scale efficiently without compromising speed?
Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development.
As you explore key principles of designing low-latency systems with Rust, you will learn how to:
- Create and compile a real-world app with Rust
- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock
Building 10x Organizations with Modern Productivity Metrics
10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.