The document summarizes BuzzNumbers' transition from using SQL Server to MongoDB as their database. It discusses problems they faced with SQL Server like scalability issues and performance problems with large datasets. It then covers why they chose to use MongoDB, including its ability to scale horizontally and handle large volumes of writes and reads. Finally, it discusses lessons learned in moving to a NoSQL database and using MongoDB and .NET to build their analytics product.
The document discusses migrating from an RDBMS to MongoDB. It covers determining if a migration is worthwhile based on evaluating current pain points and target value. It also discusses the roles and responsibilities that will change during a migration, including data architects, developers, DBAs and more. Bulk migration techniques are reviewed including using mongoimport to import JSON data. System cutover is also mentioned as an important part of the migration process.
This document provides an overview of MongoDB, a popular NoSQL database. It discusses key features of MongoDB like its schemaless and document-oriented data model. It also covers how MongoDB supports high availability through replica sets and horizontal scaling through sharding. The document aims to help developers understand how MongoDB works and when it may be suitable for different use cases.
Postgres has been proven to process document database workloads faster than MongoDB in benchmark testing. But there are multiple benefits to using Postgres over a specialized solution for such applications.
Application developers are finding new ways to combine schema-less data with traditional relational tables and deliver innovative applications faster while meeting evolving DevOps strategies and goals. Providing a single, ACID-compliant enterprise-ready database that can manage both structured and unstructured data supports the development process and reduces overall complexity.
Learn what Postgres can help you achieve. This covers:
-- Using JSON/JSONB and HSTORE to combine schema-less data with enterprise information
-- Build on existing skillsets while using web 2.0 development technologies
-- Reduce complexity that comes with using multiple heterogeneous platform and disparate application demands
-- New performance benchmark results showing Postgres outperforms MongdoDB
Has your app taken off? Are you thinking about scaling? MongoDB makes it easy to horizontally scale out with built-in automatic sharding, but did you know that sharding isn't the only way to achieve scale with MongoDB?
In this webinar, we'll review three different ways to achieve scale with MongoDB. We'll cover how you can optimize your application design and configure your storage to achieve scale, as well as the basics of horizontal scaling. You'll walk away with a thorough understanding of options to scale your MongoDB application.
The document provides an overview of a webinar on transitioning from SQL to MongoDB. It introduces the presenter Buzz Moschetti and his background. It then discusses how developers currently spend their time integrating with different components and systems like databases, and how the mismatch between data at the business level versus the database level has been a long-standing problem. The document uses examples to show how MongoDB can help by allowing richer data structures and a more direct match between data in code and the database.
This document discusses migrating from an RDBMS to MongoDB. It begins by introducing the presenter and stating the goal is to explore issues in moving an existing RDBMS system to MongoDB. It then discusses determining the value of migrating, roles and responsibilities, bulk migration techniques, and approaches to cutting over the system. Key points made include understanding why you want to migrate, assessing the effort required versus the potential pain relief, involving all roles including developers and DBAs, using tools like mongoimport for bulk loads, and testing before any production cutover.
This document outlines the topics covered in an Edureka course on MongoDB. The course contains 8 modules that cover MongoDB fundamentals, CRUD operations, schema design, administration, scaling, indexing and aggregation, application integration, and additional concepts and case studies. Each module contains multiple topics that will be taught through online instructor-led classes, recordings, quizzes, assignments, and support.
The document summarizes BuzzNumbers' transition from using SQL Server to MongoDB as their database. It discusses problems they faced with SQL Server like scalability issues and performance problems with large datasets. It then covers why they chose to use MongoDB, including its ability to scale horizontally and handle large volumes of writes and reads. Finally, it discusses lessons learned in moving to a NoSQL database and using MongoDB and .NET to build their analytics product.
The document discusses migrating from an RDBMS to MongoDB. It covers determining if a migration is worthwhile based on evaluating current pain points and target value. It also discusses the roles and responsibilities that will change during a migration, including data architects, developers, DBAs and more. Bulk migration techniques are reviewed including using mongoimport to import JSON data. System cutover is also mentioned as an important part of the migration process.
This document provides an overview of MongoDB, a popular NoSQL database. It discusses key features of MongoDB like its schemaless and document-oriented data model. It also covers how MongoDB supports high availability through replica sets and horizontal scaling through sharding. The document aims to help developers understand how MongoDB works and when it may be suitable for different use cases.
Postgres has been proven to process document database workloads faster than MongoDB in benchmark testing. But there are multiple benefits to using Postgres over a specialized solution for such applications.
Application developers are finding new ways to combine schema-less data with traditional relational tables and deliver innovative applications faster while meeting evolving DevOps strategies and goals. Providing a single, ACID-compliant enterprise-ready database that can manage both structured and unstructured data supports the development process and reduces overall complexity.
Learn what Postgres can help you achieve. This covers:
-- Using JSON/JSONB and HSTORE to combine schema-less data with enterprise information
-- Build on existing skillsets while using web 2.0 development technologies
-- Reduce complexity that comes with using multiple heterogeneous platform and disparate application demands
-- New performance benchmark results showing Postgres outperforms MongdoDB
Has your app taken off? Are you thinking about scaling? MongoDB makes it easy to horizontally scale out with built-in automatic sharding, but did you know that sharding isn't the only way to achieve scale with MongoDB?
In this webinar, we'll review three different ways to achieve scale with MongoDB. We'll cover how you can optimize your application design and configure your storage to achieve scale, as well as the basics of horizontal scaling. You'll walk away with a thorough understanding of options to scale your MongoDB application.
The document provides an overview of a webinar on transitioning from SQL to MongoDB. It introduces the presenter Buzz Moschetti and his background. It then discusses how developers currently spend their time integrating with different components and systems like databases, and how the mismatch between data at the business level versus the database level has been a long-standing problem. The document uses examples to show how MongoDB can help by allowing richer data structures and a more direct match between data in code and the database.
This document discusses migrating from an RDBMS to MongoDB. It begins by introducing the presenter and stating the goal is to explore issues in moving an existing RDBMS system to MongoDB. It then discusses determining the value of migrating, roles and responsibilities, bulk migration techniques, and approaches to cutting over the system. Key points made include understanding why you want to migrate, assessing the effort required versus the potential pain relief, involving all roles including developers and DBAs, using tools like mongoimport for bulk loads, and testing before any production cutover.
This document outlines the topics covered in an Edureka course on MongoDB. The course contains 8 modules that cover MongoDB fundamentals, CRUD operations, schema design, administration, scaling, indexing and aggregation, application integration, and additional concepts and case studies. Each module contains multiple topics that will be taught through online instructor-led classes, recordings, quizzes, assignments, and support.
Are you in the process of evaluating or migrating to MongoDB? We will cover key aspects of migrating to MongoDB from a RDBMS, including Schema design, Indexing strategies, Data migration approaches as your implementation reaches various SDLC stages, Achieving operational agility through MongoDB Management Services (MMS).
Determining the root cause of performance issues is a critical task for Operations. In this webinar, we'll show you the tools and techniques for diagnosing and tuning the performance of your MongoDB deployment. Whether you're running into problems or just want to optimize your performance, these skills will be useful.
PostgreSQL has kept up the momentum around JSON with version 9.4 featuring JSONB as demand for working with unstructured data continues to grow. In this talk delivered during Postgres Open 2014, Vibhor Kumar, principal systems engineer at EnterpriseDB, offered some scenarios for working with JSON in PostgreSQL and demonstrated performance metrics. This session also gave some instruction on how to use different operations and explored comparisons to BSON.
Webinar: Schema Patterns and Your Storage EngineMongoDB
How do MongoDB’s different storage options change the way you model your data?
Each storage engine, WiredTiger, the In-Memory Storage engine, MMAP V1 and other community supported drivers, persists data differently, writes data to disk in different formats and handles memory resources in different ways.
This webinar will go through how to design applications around different storage engines based on your use case and data access patterns. We will be looking into concrete examples of schema design practices that were previously applied on MMAPv1 and whether those practices still apply, to other storage engines like WiredTiger.
Topics for review: Schema design patterns and strategies, real-world examples, sizing and resource allocation of infrastructure.
This document discusses how to achieve scale with MongoDB. It covers optimization tips like schema design, indexing, and monitoring. Vertical scaling involves upgrading hardware like RAM and SSDs. Horizontal scaling involves adding shards to distribute load. The document also discusses how MongoDB scales for large customers through examples of deployments handling high throughput and large datasets.
When it comes time to select database software for your project, there are a bewildering number of choices. How do you know if your project is a good fit for a relational database, or whether one of the many NoSQL options is a better choice?
In this webinar you will learn when to use MongoDB and how to evaluate if MongoDB is a fit for your project. You will see how MongoDB's flexible document model is solving business problems in ways that were not previously possible, and how MongoDB's built-in features allow running at scale.
Topics covered include:
Performance and Scalability
MongoDB's Data Model
Popular MongoDB Use Cases
Customer Stories
Ready to leverage the power of a graph database to bring your application to the next level, but all the data is still stuck in a legacy relational database?
Fortunately, Neo4j offers several ways to quickly and efficiently import relational data into a suitable graph model. It's as simple as exporting the subset of the data you want to import and ingest it either with an initial loader in seconds or minutes or apply Cypher's power to put your relational data transactionally in the right places of your graph model.
In this webinar, Michael will also demonstrate a simple tool that can load relational data directly into Neo4j, automatically transforming it into a graph representation of your normalized entity-relationship model.
Webinar: Migrating from RDBMS to MongoDB (June 2015)MongoDB
Relational databases are being pushed beyond their limits because of the way we build and run applications today, coupled with growth in data sources and user loads. To address these challenges, many companies, such as MTV and Cisco have migrated successfully from relational databases to MongoDB.
In this webinar, we walk through step by step how to migrate from a relational database to MongoDB. We start off by covering schema design and performance aspects and then dive into operational aspects, such as performing seamless migrations with no downtime.
The document provides an overview of different NoSQL database types, including key-value stores, document databases, column-oriented databases, graph databases, and caches. It discusses examples of databases for each type and common use cases. The document also covers querying graph databases, polyglot persistence using multiple database types, and concludes with when each database type is best suited and when not to use a NoSQL database.
Rick Copeland is a consultant who previously worked as a software engineer and wrote books on SQLAlchemy and Python. He discusses how MongoDB can scale better than relational databases by avoiding joins, transactions, and normalization. Some scaling techniques for MongoDB include using documents to improve data locality, optimizing indexes, being aware of working data sets, scaling disks, replication for fault tolerance, and sharding for further read and write scaling.
Slides from workshop held on 12/14 in Asbury Park, NJ
http://www.meetup.com/Jersey-Shore-Tech/events/148118762/?gj=ro2_e&a=ro2_gnl&rv=ro2_e&_af_eid=148118762&_af=event
TechEd AU 2014: Microsoft Azure DocumentDB Deep DiveIntergen
Intergen CTO Chris Auld (Microsoft MVP, Microsoft Regional Director) goes deep into Microsoft Azure DocumentDB, the new fully managed, highly-scalable, NoSQL document database service. You will learn the basics - including a single slide that will give you the most important things you should know.
Since a couple of years, the NoSQL movement has developed a variety of open-source document stores. They are focused on high availability, horizontal scalability, and are designed to run on commodity hardware. These products have gained great traction in the industry to store large amounts of flexible data. Arguably, the next step for the NoSQL community is on harnessing flexible data processing.
The aim of this presentation is to introduce JSONiq: the SQL of NoSQL.
One of MongoDB’s primary attractions for developers is that it gives them the ability to start application development without needing to define a formal, up-front schema. Operations teams appreciate the fact that they don't need to perform a time-consuming schema upgrade operation every time the developers need to store a different attribute.
Some projects reach a point where it's necessary to define rules on what's being stored in the database. This webinar explains how MongoDB 3.2 allows that document validation work to be performed by the database rather than in the application code.
This webinar focuses on the benefits of using document validation: how to set up the rules using the familiar MongoDB Query Language and how to safely roll it out into an existing, mature production environment.
OrientDB vs Neo4j - and an introduction to NoSQL databasesCurtis Mosters
NoSQL databases are a good alternative to common SQL technologies. Here you get an introduction and comparison of SQL vs NoSQL. Furthermore we have a look on Graph databases and especially OrientDB vs Neo4j.
Webinar: “ditch Oracle NOW”: Best Practices for Migrating to MongoDBMongoDB
This webinar will guide you through the best practices for migrating off of a relational database. Whether you are migrating an existing application, or considering using MongoDB in place of your traditional relational database for a new project, this webinar will get you to production faster, with less effort, cost and risk.
The proliferation of data from new data sources has generated greater demand for technologies that can handle and harvest value from unstructured data. Postgres is leading the movement of integrating unstructured data with the relational environment.
Postgres first added JSON and then enhanced it with new data types, functions and operators in recent releases. Now in beta is the JSONB “binary JSON” type. These advances follow the longstanding HStore data type added in 2006 to support key/value stores in Postgres. Now Postgres users can learn how to harness these capabilities to master unstructured data challenges with Postgres.
The presentation also covers:
* An overview of JSON data types and operators
* Examples of SELECT, UPDATE, etc
* An examination of performance considerations
For more information, please email sales@enterprisedb.com
This document discusses MongoDB best practices for deploying MongoDB in AWS. It begins with terminology comparing MongoDB and relational databases. It then shows an example data model in SQL and how that same data would be modeled in MongoDB. The document discusses concepts like cursors, indexing, and sharding in MongoDB. It emphasizes the importance of sizing RAM and disk appropriately based on working set size and data access patterns. Finally, it covers replication in MongoDB and different replication set topologies that can be used in AWS for high availability and disaster recovery.
[db tech showcase Tokyo 2017] C23: Lessons from SQLite4 by SQLite.org - Richa...Insight Technology, Inc.
SQLite4 was a project started at the beginning of 2012 and designed to provide a follow-on to SQLite3 without the constraints of backwards compatibility. SQLite4 was built around a Log Structured Merge (LSM) storage engine that is transactional, stores all content in a single file on disk, and that is faster than LevelDB. Other innovations in include the use of decimal floating-point arthimetic and a single storage engine namespace used for all tables and indexes. Expectations were initially high. However, development stopped about 2.5 years later, after finding that the design of SQLite4 would never be competitive with SQLite3. This talk overviews the technological ideas tried in SQLite4 and discusses why they did not work out for the kinds of workloads typically encountered for an embedded database engine.
This document discusses NoSQL databases as an alternative to relational databases. It provides background on new requirements from web companies that led to the rise of NoSQL, such as high availability, scalability, and flexibility in data structures. Various NoSQL database models are presented, including key-value stores, document databases, and graph databases. The document highlights tradeoffs between consistency, availability, and partition tolerance based on Brewer's CAP theorem. Examples of popular NoSQL databases like Dynamo, Cassandra, and Neo4J are also mentioned.
Webinar: How We Evaluated MongoDB as a Relational Database ReplacementMongoDB
This webinar will explain the process, methodology, and results used at Apollo Group to evaluate MongoDB and ultimately replace Oracle for a core platform component.
Are you in the process of evaluating or migrating to MongoDB? We will cover key aspects of migrating to MongoDB from a RDBMS, including Schema design, Indexing strategies, Data migration approaches as your implementation reaches various SDLC stages, Achieving operational agility through MongoDB Management Services (MMS).
Determining the root cause of performance issues is a critical task for Operations. In this webinar, we'll show you the tools and techniques for diagnosing and tuning the performance of your MongoDB deployment. Whether you're running into problems or just want to optimize your performance, these skills will be useful.
PostgreSQL has kept up the momentum around JSON with version 9.4 featuring JSONB as demand for working with unstructured data continues to grow. In this talk delivered during Postgres Open 2014, Vibhor Kumar, principal systems engineer at EnterpriseDB, offered some scenarios for working with JSON in PostgreSQL and demonstrated performance metrics. This session also gave some instruction on how to use different operations and explored comparisons to BSON.
Webinar: Schema Patterns and Your Storage EngineMongoDB
How do MongoDB’s different storage options change the way you model your data?
Each storage engine, WiredTiger, the In-Memory Storage engine, MMAP V1 and other community supported drivers, persists data differently, writes data to disk in different formats and handles memory resources in different ways.
This webinar will go through how to design applications around different storage engines based on your use case and data access patterns. We will be looking into concrete examples of schema design practices that were previously applied on MMAPv1 and whether those practices still apply, to other storage engines like WiredTiger.
Topics for review: Schema design patterns and strategies, real-world examples, sizing and resource allocation of infrastructure.
This document discusses how to achieve scale with MongoDB. It covers optimization tips like schema design, indexing, and monitoring. Vertical scaling involves upgrading hardware like RAM and SSDs. Horizontal scaling involves adding shards to distribute load. The document also discusses how MongoDB scales for large customers through examples of deployments handling high throughput and large datasets.
When it comes time to select database software for your project, there are a bewildering number of choices. How do you know if your project is a good fit for a relational database, or whether one of the many NoSQL options is a better choice?
In this webinar you will learn when to use MongoDB and how to evaluate if MongoDB is a fit for your project. You will see how MongoDB's flexible document model is solving business problems in ways that were not previously possible, and how MongoDB's built-in features allow running at scale.
Topics covered include:
Performance and Scalability
MongoDB's Data Model
Popular MongoDB Use Cases
Customer Stories
Ready to leverage the power of a graph database to bring your application to the next level, but all the data is still stuck in a legacy relational database?
Fortunately, Neo4j offers several ways to quickly and efficiently import relational data into a suitable graph model. It's as simple as exporting the subset of the data you want to import and ingest it either with an initial loader in seconds or minutes or apply Cypher's power to put your relational data transactionally in the right places of your graph model.
In this webinar, Michael will also demonstrate a simple tool that can load relational data directly into Neo4j, automatically transforming it into a graph representation of your normalized entity-relationship model.
Webinar: Migrating from RDBMS to MongoDB (June 2015)MongoDB
Relational databases are being pushed beyond their limits because of the way we build and run applications today, coupled with growth in data sources and user loads. To address these challenges, many companies, such as MTV and Cisco have migrated successfully from relational databases to MongoDB.
In this webinar, we walk through step by step how to migrate from a relational database to MongoDB. We start off by covering schema design and performance aspects and then dive into operational aspects, such as performing seamless migrations with no downtime.
The document provides an overview of different NoSQL database types, including key-value stores, document databases, column-oriented databases, graph databases, and caches. It discusses examples of databases for each type and common use cases. The document also covers querying graph databases, polyglot persistence using multiple database types, and concludes with when each database type is best suited and when not to use a NoSQL database.
Rick Copeland is a consultant who previously worked as a software engineer and wrote books on SQLAlchemy and Python. He discusses how MongoDB can scale better than relational databases by avoiding joins, transactions, and normalization. Some scaling techniques for MongoDB include using documents to improve data locality, optimizing indexes, being aware of working data sets, scaling disks, replication for fault tolerance, and sharding for further read and write scaling.
Slides from workshop held on 12/14 in Asbury Park, NJ
http://www.meetup.com/Jersey-Shore-Tech/events/148118762/?gj=ro2_e&a=ro2_gnl&rv=ro2_e&_af_eid=148118762&_af=event
TechEd AU 2014: Microsoft Azure DocumentDB Deep DiveIntergen
Intergen CTO Chris Auld (Microsoft MVP, Microsoft Regional Director) goes deep into Microsoft Azure DocumentDB, the new fully managed, highly-scalable, NoSQL document database service. You will learn the basics - including a single slide that will give you the most important things you should know.
Since a couple of years, the NoSQL movement has developed a variety of open-source document stores. They are focused on high availability, horizontal scalability, and are designed to run on commodity hardware. These products have gained great traction in the industry to store large amounts of flexible data. Arguably, the next step for the NoSQL community is on harnessing flexible data processing.
The aim of this presentation is to introduce JSONiq: the SQL of NoSQL.
One of MongoDB’s primary attractions for developers is that it gives them the ability to start application development without needing to define a formal, up-front schema. Operations teams appreciate the fact that they don't need to perform a time-consuming schema upgrade operation every time the developers need to store a different attribute.
Some projects reach a point where it's necessary to define rules on what's being stored in the database. This webinar explains how MongoDB 3.2 allows that document validation work to be performed by the database rather than in the application code.
This webinar focuses on the benefits of using document validation: how to set up the rules using the familiar MongoDB Query Language and how to safely roll it out into an existing, mature production environment.
OrientDB vs Neo4j - and an introduction to NoSQL databasesCurtis Mosters
NoSQL databases are a good alternative to common SQL technologies. Here you get an introduction and comparison of SQL vs NoSQL. Furthermore we have a look on Graph databases and especially OrientDB vs Neo4j.
Webinar: “ditch Oracle NOW”: Best Practices for Migrating to MongoDBMongoDB
This webinar will guide you through the best practices for migrating off of a relational database. Whether you are migrating an existing application, or considering using MongoDB in place of your traditional relational database for a new project, this webinar will get you to production faster, with less effort, cost and risk.
The proliferation of data from new data sources has generated greater demand for technologies that can handle and harvest value from unstructured data. Postgres is leading the movement of integrating unstructured data with the relational environment.
Postgres first added JSON and then enhanced it with new data types, functions and operators in recent releases. Now in beta is the JSONB “binary JSON” type. These advances follow the longstanding HStore data type added in 2006 to support key/value stores in Postgres. Now Postgres users can learn how to harness these capabilities to master unstructured data challenges with Postgres.
The presentation also covers:
* An overview of JSON data types and operators
* Examples of SELECT, UPDATE, etc
* An examination of performance considerations
For more information, please email sales@enterprisedb.com
This document discusses MongoDB best practices for deploying MongoDB in AWS. It begins with terminology comparing MongoDB and relational databases. It then shows an example data model in SQL and how that same data would be modeled in MongoDB. The document discusses concepts like cursors, indexing, and sharding in MongoDB. It emphasizes the importance of sizing RAM and disk appropriately based on working set size and data access patterns. Finally, it covers replication in MongoDB and different replication set topologies that can be used in AWS for high availability and disaster recovery.
[db tech showcase Tokyo 2017] C23: Lessons from SQLite4 by SQLite.org - Richa...Insight Technology, Inc.
SQLite4 was a project started at the beginning of 2012 and designed to provide a follow-on to SQLite3 without the constraints of backwards compatibility. SQLite4 was built around a Log Structured Merge (LSM) storage engine that is transactional, stores all content in a single file on disk, and that is faster than LevelDB. Other innovations in include the use of decimal floating-point arthimetic and a single storage engine namespace used for all tables and indexes. Expectations were initially high. However, development stopped about 2.5 years later, after finding that the design of SQLite4 would never be competitive with SQLite3. This talk overviews the technological ideas tried in SQLite4 and discusses why they did not work out for the kinds of workloads typically encountered for an embedded database engine.
This document discusses NoSQL databases as an alternative to relational databases. It provides background on new requirements from web companies that led to the rise of NoSQL, such as high availability, scalability, and flexibility in data structures. Various NoSQL database models are presented, including key-value stores, document databases, and graph databases. The document highlights tradeoffs between consistency, availability, and partition tolerance based on Brewer's CAP theorem. Examples of popular NoSQL databases like Dynamo, Cassandra, and Neo4J are also mentioned.
Webinar: How We Evaluated MongoDB as a Relational Database ReplacementMongoDB
This webinar will explain the process, methodology, and results used at Apollo Group to evaluate MongoDB and ultimately replace Oracle for a core platform component.
MongoDB World 2018: Breaking the Mold - Redesigning Dell's E-Commerce PlatformMongoDB
This document discusses Dell's redesign of its e-commerce platform by moving from a relational database to MongoDB. It outlines the advantages of a resource-oriented architecture and document stores. It then provides details of Dell's DCQO application that was migrated from an XML-based relational database to MongoDB, including the architecture, schema design, data migration strategy, and operations management tools.
ГАННА КАПЛУН «noSQL vs SQL: порівняння використання реляційних та нереляційни...QADay
This document compares noSQL and SQL databases, providing examples of each. NoSQL databases are non-relational and have dynamic schemas while SQL databases are relational and have predefined schemas. Some common noSQL databases mentioned are MongoDB, DynamoDB, Cassandra, and Neo4j, while examples of SQL databases include Oracle, MySQL, PostgreSQL, and SQLite. The document then discusses using MongoDB for a production application that stores tree-structured and dynamic data more easily than a SQL database would. It also covers combining MongoDB and Oracle by storing documents in Oracle and metadata in MongoDB.
At Adjust we use PostgreSQL the way a lot of people may use Hadoop or other big data platforms. This presentation goes through three of our environments to discuss how we go about doing this and the challenges we face.
This talk will cover lessons learned at Community Engine regarding MongoDB, including: why we moved away from an Hybrid solution using SQL and MongoDB; an outline of the technologies and what we learned using MongoDB on Amazon Web Services; the MongoDB C# driver; MongoDB with SOLR for Full Text Search; how we do migration, deployment and more.
If everyone write their documents with the intent that they be standardized and converted, conversion to S1000D would be easy. But the reality is that most legacy data lacks the details needed for a full conversion or contains anomalies and irrelevant text. This leads us to the question one must ask: should I convert, rewrite, or manually convert the legacy data? In this presentation, we will attempt to answer this question by reviewing:
o A very quick introduction to S1000D conversions
o What the technical headaches are
o Whether to convert or rewrite
o Planning for a good conversion experience
o What the timeline looks like
o Some tools to help
Student Industrial Training Presentation SlideKhairul Filhan
1) The document summarizes the student's 6-month internship at MIMOS KHTP working on the Software Development Team.
2) During the internship, the student worked on several projects involving NoSQL databases like MongoDB and MapDB, serialization with Gson and Avro, Elasticsearch, and parsing HTML with Jsoup.
3) The student faced challenges from the professional work environment and new technologies but achieved skills in databases, frameworks, testing, and coding best practices.
MongoDB: How We Did It – Reanimating Identity at AOLMongoDB
AOL experienced explosive growth and needed a new database that was both flexible and easy to deploy with little effort. They chose MongoDB. Due to the complexity of internal systems and the data, most of the migration process was spent building a new identity platform and adapters for legacy apps to talk to MongoDB. Systems were migrated in 4 phases to ensure that users were not impacted during the switch. Turning on dual reads/writes to both legacy databases and MongoDB also helped get production traffic into MongoDB during the process. Ultimately, the project was successful with the help of MongoDB support. Today, the team has 15 shards, with 60-70 GB per shard.
Overview of the TREC 2019 Deep Learning TrackNick Craswell
This document summarizes the goals and setup of the 2019 TREC deep learning track for passage and document retrieval. It describes the datasets used, which include over 300,000 queries with human labels for training and new test sets. It discusses the types of models submitted, with "nnlm" models using BERT performing best. Metrics are analyzed showing these models outperform traditional "trad" baselines. The document also considers implications for real-world search systems.
Open Source North - MongoDB Advanced Schema Design PatternsMatthew Kalan
The hardest part of moving from a tabular database world to a modern world of objects and JSON is how to model your data. This year at OSN, Matt from MongoDB will take data modeling one step further than prior years and focus specifically on advanced schema design patterns to optimize the ease-of-use and performance of your data access layer and application.
Meteor Revolution: From DDP to Blaze Reactive Rendering Massimo Sgrelli
Meteor is an open-source platform for building mobile and web applications using JavaScript. It uses a reactive programming model and Distributed Data Protocol (DDP) for real-time data synchronization between client and server. Key features include live data updates, shared code between client and server, and latency compensation through local caching of data on the client. Meteor provides a full-stack solution with support for front-end templating, routing, and transparent reactivity as well as backend services through integration with MongoDB.
The document summarizes the development of new symbology and suggest web services using MongoDB to replace older services that had performance and scalability issues. Key points:
- The services provide financial reference data and suggestions via symbols/codes accessed millions of times daily.
- MongoDB was chosen for its document model, performance of 1ms average response time, and ability to store data fully in memory.
- The symbology service optimizes data storage to reduce space and enable fast searches through field normalization and compression.
- The suggest service uses an inverted index for partial text searches and generates suggestions from the symbology data through Amazon EMR.
- MongoDB drivers for .NET provided good performance without bottlenecks.
The document discusses strategies for optimizing large Neo4j databases. It analyzes three scenarios involving large databases and identifies issues like overuse of indexes, property-based queries, and lack of understanding about locking and property access. For each scenario, it proposes data model optimizations like pre-aggregating data, using relationships to link data instead of properties, and stored procedures to reduce working data set sizes and improve query performance as the databases scale. The key recommendations are to review how the database is growing, optimize data models and queries, and leverage patterns like traversal over filtering to avoid performance bottlenecks in large databases.
MongoDB World 2019: Fast Machine Learning Development with MongoDBMongoDB
Today an increasingly large number of products use machine learning to deliver a great personalized user experience, and workplace software is no exception. Learn how Spoke uses MongoDB to do dynamic model training in real time from user interaction data and automatically train and serve thousands of models, with multiple customized models per client.
Enriching Solr with Deep Learning for a Question Answering System - Sanket Sh...Lucidworks
The document discusses research into using deep learning to improve question answering systems. It describes using Solr to retrieve documents and then using machine learning models to rerank the results. The research compared various supervised and unsupervised models for question similarity and answer selection tasks. For question similarity, ensemble models using TFIDF and sentence embeddings performed best. For answer selection, deep learning models outperformed traditional models when sufficient training data was available.
The document discusses secrets and best practices for optimizing the performance of an OLTP system. It describes how the speaker's team was able to reduce response times by 50% through focused tuning of the application to database interface. Some techniques that helped include identifying redundant database calls, reducing round trips by passing data in arrays, processing data in bulk using INSERT statements, and returning less unused data. The document provides recommendations for locking strategies, using JDBC features like arrays and batching, and setting the optimal row prefetch.
Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...Amazon Web Services
Join us for the first-ever Amazon DynamoDB practical hands-on workshop. This session is designed for developers, engineers, and database administrators who are involved in designing and maintaining DynamoDB applications. We begin with a walkthrough of proven NoSQL design patterns for at-scale applications. Next, we use step-by-step instructions to apply lessons learned to design DynamoDB tables and indexes that are optimized for performance and cost. Expect to leave this session with the knowledge to build and monitor DynamoDB applications that can grow to any size and scale. Attendees should have a basic understanding of DynamoDB. To attend this workshop, bring your laptop.
AWS re:Invent 2016| DAT318 | Migrating from RDBMS to NoSQL: How Sony Moved fr...Amazon Web Services
In this session, you will learn the key differences between a relational database management service (RDBMS) and non-relational (NoSQL) databases like Amazon DynamoDB. You will learn about suitable and unsuitable use cases for NoSQL databases. You'll learn strategies for migrating from an RDBMS to DynamoDB through a 5-phase, iterative approach. See how Sony migrated an on-premises MySQL database to the cloud with Amazon DynamoDB, and see the results of this migration.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
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.
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.
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.
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.
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
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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.