As a software adventurer, Charles “Indy” Sarrazin, has brought numerous customers through the MongoDB world, using his extensive knowledge to make sure they always got the most out of their databases. Let us embark on a journey inside the Document Model, where we will identify, analyze and fix anti-patterns. I will also provide you with tools to ease migration strategies towards the Temple of Lost Performance! Be warned, though! You might want to learn about design patterns before, in order to survive this exhilarating trial!
MongoDB is an open-source, document-oriented database that provides high performance and horizontal scalability. It uses a document-model where data is organized in flexible, JSON-like documents rather than rigidly defined rows and tables. Documents can contain multiple types of nested objects and arrays. MongoDB is best suited for applications that need to store large amounts of unstructured or semi-structured data and benefit from horizontal scalability and high performance.
Oracle Real Application Clusters (RAC) has been Oracle's premier database availability and scalability solution for more than two decades as it provides near linear horizontal scalability without the need to change the application code. This session explains why Oracle RAC 19c is the basis for Oracle's Autonomous Database by introducing some of its latest features, some of which were specifically designed for ATP-D, as well as by taking a peek under the hood of the dedicated Autonomous Database Service (ATP-D).
Databricks used to use a static manually maintained wiki page for internal data exploration. We will discuss how we leverage Amundsen, an open source data discovery tool from Linux Foundation AI & Data, to improve productivity with trust by surfacing the most relevant dataset and SQL analytics dashboard with its important information programmatically at Databricks internally. We will also talk about how we integrate Amundsen with Databricks world class infrastructure to surface metadata including: Surface the most popular tables used within Databricks Support fuzzy search and facet search for dataset- Surface rich metadata on datasets: Lineage information (downstream table, upstream table, downstream jobs, downstream users) Dataset owner Dataset frequent users Delta extend metadata (e.g change history) ETL job that generates the dataset Column stats on numeric type columns Dashboards that use the given dataset Use Databricks data tab to show the sample data Surface metadata on dashboards including: create time, last update time, tables used, etc Last but not least, we will discuss how we incorporate internal user feedback and provide the same discovery productivity improvements for Databricks customers in the future.
A talk given on 2018-06-16 in HK Open Source Conference 2018. The rise of the Apache Kafka starts a new generation of data pipeline - the stream-processing pipeline. In this talk, Dr. Mole Wong will walk you through the concept of the stream-processing data pipeline, and how this data pipeline can be set up. He will also discuss the use cases of such a data pipeline.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
Oracle Database 19c, builds upon key architectural, distributed data and performance innovations established in earlier versions Oracle Database 12c and 18c releases. Oracle 19c has many new features, in this presentation we have covered below areas Automated Installation, Configuration and Patching AutoUpgrade and Database Utilities
The document discusses the right and wrong use cases for MongoDB. It outlines some of the key benefits of MongoDB, including its performance, scalability, data model and query model. Specific use cases that are well-suited for MongoDB include building a single customer view, powering mobile applications, and performing real-time analytics. Cache-only workloads are identified as not being a good use case. The document provides examples of large companies successfully using MongoDB for these right use cases.
This document provides an overview of Apache Sentry, an open source authorization module for Hadoop. It discusses how Sentry provides fine-grained, role-based authorization across Hadoop components like Hive, Impala and Solr to address the fragmented authorization in Hadoop. Sentry stores authorization policies that map users and groups to roles with privileges for resources like databases, tables and collections. It evaluates rules to determine access for a user based on their group memberships and role privileges.
Oracle GoldenGate is the leading real-time data integration software provider in the industry - customers include 3 of the top 5 commercial banks, 3 of the top 3 busiest ATM networks, and 4 of the top 5 telecommunications providers. Oracle GoldenGate moves transactional data in real-time across heterogeneous database, hardware and operating systems with minimal impact. The software platform captures, routes, and delivers data in real time, enabling organizations to maintain continuous uptime for critical applications during planned and unplanned outages. Additionally, it moves data from transaction processing environments to read-only reporting databases and analytical applications for accurate, timely reporting and improved business intelligence for the enterprise.
This document provides an overview and instructions for deploying, upgrading, and troubleshooting a MongoDB sharded cluster. It describes the components of a sharded cluster including shards, config servers, and mongos processes. It provides recommendations for initial deployment including using replica sets for shards and config servers, DNS names instead of IPs, and proper user authorization. The document also outlines best practices for upgrading between minor and major versions, including stopping the balancer, upgrading processes in rolling fashion, and handling incompatible changes when downgrading major versions.
The document is a slide presentation on MongoDB that introduces the topic and provides an overview. It defines MongoDB as a document-oriented, open source database that provides high performance, high availability, and easy scalability. It also discusses MongoDB's use for big data applications, how it is non-relational and stores data as JSON-like documents in collections without a defined schema. The presentation provides steps for installing MongoDB and describes some basic concepts like databases, collections, documents and commands.
Presentation from RheoData Webinar on 8/18/2021 Topic: Oracle GoldeGate 21c New Features and Best Practices
This document provides an overview and introduction to MongoDB. It discusses how new types of applications, data, volumes, development methods and architectures necessitated new database technologies like NoSQL. It then defines MongoDB and describes its features, including using documents to store data, dynamic schemas, querying capabilities, indexing, auto-sharding for scalability, replication for availability, and using memory for performance. Use cases are presented for companies like Foursquare and Craigslist that have migrated large volumes of data and traffic to MongoDB to gain benefits like flexibility, scalability, availability and ease of use over traditional relational database systems.
The document discusses MongoDB concepts including: - MongoDB uses a document-oriented data model with dynamic schemas and supports embedding and linking of related data. - Replication allows for high availability and data redundancy across multiple nodes. - Sharding provides horizontal scalability by distributing data across nodes in a cluster. - MongoDB supports both eventual and immediate consistency models.
OGG Microservices Architecture introduces new types of processes to replace those in the classic architecture. The main components are the Service Manager, Administration Server, Distribution Server, Receiver Server, and Performance Metrics Server. The Administration Server and Admin Client allow managing GoldenGate processes through a web interface and command line tool respectively. A demo is shown configuring the source and target databases, creating credentials, extract, path, and replicat to replicate a table from the source to target.
Intro to MongoDB Get a jumpstart on MongoDB, use cases, and next steps for building your first app with Buzz Moschetti, MongoDB Enterprise Architect. @BuzzMoschetti
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database. ---------------------------------------------------------- Get Socialistic Our website: http://valuebound.com/ LinkedIn: http://bit.ly/2eKgdux Facebook: https://www.facebook.com/valuebound/ Twitter: http://bit.ly/2gFPTi8
This document discusses MongoDB's cloud database offerings including MongoDB Atlas, Ops Manager, and Cloud Manager. It provides an overview of key features such as automated backups, point-in-time restore, queryable snapshots, global availability, security, and elastic scaling. The document also demonstrates MongoDB's managed backup capabilities in Atlas including cloud provider snapshots on AWS and Azure, as well as a roadmap for future disaster recovery features.
Compilation of information to provide an introduction to MongoDB with particular emphasis on its C# driver.
This document provides a summary of a presentation on Big Data and NoSQL databases. It introduces the presenters, Melissa Demsak and Don Demsak, and their backgrounds. It then discusses how data storage needs have changed with the rise of Big Data, including the problems created by large volumes of data. The presentation contrasts traditional relational database implementations with NoSQL data stores, identifying five categories of NoSQL data models: document, key-value, graph, and column family. It provides examples of databases that fall under each category. The presentation concludes with a comparison of real-world scenarios and which data storage solutions might be best suited to each scenario.
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.
Backup is an important part of your MongoDB deployment. Come and learn about the different offerings MongoDB has to help meet your backup requirements.
NoSQL databases are non-relational databases designed for large volumes of data across many servers. They emerged to address scaling and reliability issues with relational databases. While different technologies, NoSQL databases are designed for distribution without a single point of failure and to sacrifice consistency for availability if needed. Examples include Dynamo, BigTable, Cassandra and CouchDB.
comprehensive Introduction to NoSQL solutions inside the big data landscape. Graph store? Column store? key Value store? Document Store? redis or memcache? dynamo db? mongo db ? hbase? Cloud or open source?
Backup is an important part of your MongoDB deployment. Come and learn about the different offerings MongoDB has to help meet your backup requirements.
Backup is an important part of your MongoDB deployment. Come and learn about the different offerings MongoDB has to help meet your backup requirements.
This document summarizes best practices for scaling MongoDB deployments. It discusses Behance's use of MongoDB for their activity feed, including moving from 40 nodes with 250M documents on ext3 to 60 nodes with 400M documents on ext4. It covers topics like sharding, replica sets, indexing, maintenance, and hardware considerations for large MongoDB clusters.
Lessons Learned from Migrating 2+ Billion Documents at Craigslist outlines Craigslist's migration from MySQL to MongoDB. Some key lessons include: knowing your hardware limitations, that replica sets provide high availability during reboots, understanding your data types and sizes, and being aware of limitations with sharding and replica set re-sync processes. The migration addressed issues with their archive data storage and provided a more scalable and performant system.
1. The document discusses using MongoDB and data lakes for enterprise data management. It outlines the current issues with relational databases and how MongoDB addresses challenges like flexibility, scalability and performance. 2. Various architectures for enterprise data management with MongoDB are presented, including using it for raw, transformed and aggregated data stores. 3. The benefits of combining MongoDB and Hadoop in a data lake are greater agility, insight from handling different data structures, scalability and low latency for real-time decisions.
High level presentation of non-relational databases focusing on key vale, document, column oriented, and graph.
Find out which is faster, SQL or NoSQL, for traditional reporting tasks. Discover how you can optimise MongoDB aggregation pipelines and how to push complex computation down to the database.
Haytham ElFadeel presented on the limitations of traditional database systems and introduced key-value storage systems as a next generation storage solution. Traditional databases are complex, have poor performance, and do not scale well. In contrast, key-value storage systems have a simple data model of key-value pairs, are designed from the start to scale horizontally across many machines, and can provide much better performance. Examples of popular key-value storage systems discussed included Amazon Dynamo, Facebook Cassandra, Redis, and MongoDB.
The document provides an agenda for a two-day training on NoSQL and MongoDB. Day 1 covers an introduction to NoSQL concepts like distributed and decentralized databases, CAP theorem, and different types of NoSQL databases including key-value, column-oriented, and document-oriented databases. It also covers functions and indexing in MongoDB. Day 2 focuses on specific MongoDB topics like aggregation framework, sharding, queries, schema-less design, and indexing.
This document provides an introduction and overview of MongoDB. It begins with definitions of NoSQL databases and describes the main types: key-value stores, wide column stores, document stores, and graph stores. It then discusses MongoDB specifically, describing it as a free, open-source, document-oriented database that uses JSON-like documents with dynamic schemas. The document outlines how to quickly install MongoDB using Docker, and how to perform basic CRUD operations like creating databases and collections, inserting, reading, updating, and deleting documents. It also discusses some key MongoDB concepts like its support for the CAP theorem prioritizing availability and partition tolerance over strong consistency.
The document discusses the challenges of scaling a PostgreSQL database for a SAAS backend with growing data. It describes how the company initially separated OLTP and OLAP data into separate databases but later unified them into a single database approach. It discusses partitioning the data using separate databases for each customer account and the benefits and limitations of this approach. It also covers additional performance issues encountered and solutions implemented including advisory locks, bulk loading optimizations, and maintaining spare databases to speed up new account creation. The document emphasizes the importance of schemas for code versioning and staging releases.
Eliot Horowitz discusses various techniques for scaling MongoDB deployments, including optimization of schema design, indexes, hardware configuration, embedding documents, and different replication architectures like replica sets and sharding. The key techniques for scaling reads are to optimize schemas and indexes, use replica sets to distribute reads to slaves, and scale by adding more slaves. For writes, sharding allows scaling by partitioning data across multiple shard clusters.
MongoDB is a document-oriented NoSQL database that uses flexible schemas and provides high performance, high availability, and easy scalability. It uses either MMAP or WiredTiger storage engines and supports features like sharding, aggregation pipelines, geospatial indexing, and GridFS for large files. While MongoDB has better performance than Cassandra or Couchbase according to benchmarks, it has limitations such as a single-threaded aggregation and lack of joins across collections.
This presentation discusses migrating data from other data stores to MongoDB Atlas. It begins by explaining why MongoDB and Atlas are good choices for data management. Several preparation steps are covered, including sizing the target Atlas cluster, increasing the source oplog, and testing connectivity. Live migration, mongomirror, and dump/restore options are presented for migrating between replicasets or sharded clusters. Post-migration steps like monitoring and backups are also discussed. Finally, migrating from other data stores like AWS DocumentDB, Azure CosmosDB, DynamoDB, and relational databases are briefly covered.
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.