Backup is an important part of your MongoDB deployment. Come and learn about the different offerings MongoDB has to help meet your backup requirements.
Solving Your Backup Needs Using MongoDB Ops Manager, Cloud Manager and AtlasMongoDB
Backup is an important part of your MongoDB deployment. Come and learn about the different offerings MongoDB has to help meet your backup requirements.
- Rediff News uses MongoDB for its publishing system to manage the lifecycle of articles, store article metadata and roles, acquire external feeds, enable tagging and notifications, and power search and data visualization on maps.
- The system allows users to upload Excel data, match it to map attributes, generate articles using data science insights, and visualize data on interactive maps.
- Rediff's architecture uses POJOs to define schemas, custom collections to store different data types, and a REST layer to expose data resources and abstract storage from applications.
This document summarizes Johan Gustavsson's presentation on scaling Hadoop in the cloud. It discusses replacing an on-premise Hadoop cluster with Plazma storage on S3 and job execution in isolated pools. It also covers Treasure Data's Patchset project which aims to support multiple Hadoop versions and allow job-preserving restarts of the Elephant server.
HBaseConAsia2018 Track3-2: HBase at China TelecomMichael Stack
HBase is used at China Telecom for various applications including persistence for streaming jobs, online reading and writing, and as a data store for their core system. They operate several HBase clusters storing over 500 TB of data ingesting 1 TB per day. They monitor HBase using Ganglia for basic metrics and Zabbix for critical alerts. When issues arise, such as a system hang, they investigate debug cases and perform optimizations like changing the garbage collector from CMS to G1 and implementing read/write splitting.
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
HBaseConAsia2018 Track2-3: Bringing MySQL Compatibility to HBase using Databa...Michael Stack
This document discusses AntsDB, an open source project that brings MySQL compatibility to HBase in order to address the need for relational database capabilities in NoSQL systems. It describes AntsDB's architecture, which uses caching and other techniques to provide low-latency transactions and joins on HBase. Performance tests show AntsDB can achieve high throughput for writes and OLTP workloads. AntsDB aims to be complementary to HBase by virtualizing MySQL atop HBase while simulating MySQL behaviors and allowing applications built for MySQL to run unchanged on HBase.
HBaseConAsia2018 Keynote 2: Recent Development of HBase in Alibaba and CloudMichael Stack
New Journey of HBase in Alibaba and Cloud discusses Alibaba's use of HBase over 8 years and improvements made. Key points discussed include:
- Alibaba began using HBase in 2010 and has since contributed to the open source community while developing internal improvements.
- Challenges addressed include JVM garbage collection pauses, separating computing and storage, and adding cold/hot data tiering. A diagnostic system was also created.
- Alibaba uses HBase across many core scenarios and has integrated it with other databases in a multi-model approach to support different workloads.
- Benefits of running HBase on cloud include flexibility, cost savings, and making it
Solving Your Backup Needs Using MongoDB Ops Manager, Cloud Manager and AtlasMongoDB
Backup is an important part of your MongoDB deployment. Come and learn about the different offerings MongoDB has to help meet your backup requirements.
- Rediff News uses MongoDB for its publishing system to manage the lifecycle of articles, store article metadata and roles, acquire external feeds, enable tagging and notifications, and power search and data visualization on maps.
- The system allows users to upload Excel data, match it to map attributes, generate articles using data science insights, and visualize data on interactive maps.
- Rediff's architecture uses POJOs to define schemas, custom collections to store different data types, and a REST layer to expose data resources and abstract storage from applications.
This document summarizes Johan Gustavsson's presentation on scaling Hadoop in the cloud. It discusses replacing an on-premise Hadoop cluster with Plazma storage on S3 and job execution in isolated pools. It also covers Treasure Data's Patchset project which aims to support multiple Hadoop versions and allow job-preserving restarts of the Elephant server.
HBaseConAsia2018 Track3-2: HBase at China TelecomMichael Stack
HBase is used at China Telecom for various applications including persistence for streaming jobs, online reading and writing, and as a data store for their core system. They operate several HBase clusters storing over 500 TB of data ingesting 1 TB per day. They monitor HBase using Ganglia for basic metrics and Zabbix for critical alerts. When issues arise, such as a system hang, they investigate debug cases and perform optimizations like changing the garbage collector from CMS to G1 and implementing read/write splitting.
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
HBaseConAsia2018 Track2-3: Bringing MySQL Compatibility to HBase using Databa...Michael Stack
This document discusses AntsDB, an open source project that brings MySQL compatibility to HBase in order to address the need for relational database capabilities in NoSQL systems. It describes AntsDB's architecture, which uses caching and other techniques to provide low-latency transactions and joins on HBase. Performance tests show AntsDB can achieve high throughput for writes and OLTP workloads. AntsDB aims to be complementary to HBase by virtualizing MySQL atop HBase while simulating MySQL behaviors and allowing applications built for MySQL to run unchanged on HBase.
HBaseConAsia2018 Keynote 2: Recent Development of HBase in Alibaba and CloudMichael Stack
New Journey of HBase in Alibaba and Cloud discusses Alibaba's use of HBase over 8 years and improvements made. Key points discussed include:
- Alibaba began using HBase in 2010 and has since contributed to the open source community while developing internal improvements.
- Challenges addressed include JVM garbage collection pauses, separating computing and storage, and adding cold/hot data tiering. A diagnostic system was also created.
- Alibaba uses HBase across many core scenarios and has integrated it with other databases in a multi-model approach to support different workloads.
- Benefits of running HBase on cloud include flexibility, cost savings, and making it
MongoDB .local Bengaluru 2019: Lift & Shift MongoDB to AtlasMongoDB
Managing and scaling the infrastructure for critical business data can be a real pain. To handle this massive scale of data effectively, thousands of users of MongoDB from all around the world have migrated their large and small databases to MongoDB Atlas.
By the end of this talk, you'll have a better understanding of the “how” and “why” of it, and will be able to leverage it in your organisation with elevated confidence. I'll demo the migration of a realtime application using MongoDB from existing cloud infrastructure to MongoDB Atlas.
If you're a developer, DBA or a business stakeholder, and your organisation is using/planning to use MongoDB on-premise or with any other cloud vendor, this talk will help you to gain insights into the best way to run MongoDB.
Interactive Visualization of Streaming Data Powered by Spark by Ruhollah Farc...Spark Summit
This document discusses how to visualize streaming data using Spark. It describes how Spark Streaming can be used to process streaming data in real-time and integrate it with visualization tools. Key points include:
- Spark Streaming receives streaming data from sources like Kafka and processes it using in-memory computations in a single JVM cluster.
- The processed data can be stored in buffers like MongoDB or output to systems like MemSQL, Solr to enable interactive visualizations that update in real-time.
- A demo is shown of Twitter data being streamed and analyzed using Spark Streaming with results stored in MemSQL and Solr for visualization.
- Benefits of this approach include being able to work with streaming data
HBaseConAsia2018 Track3-5: HBase Practice at LianjiaMichael Stack
This document discusses different big data scenarios using HBase including:
1. Architecture evolution over time including olap and real-time ETL scenarios
2. The olap scenario requirements like handling billion records with sub-second queries and examples using Kylin
3. The monitor scenario showing how different systems are monitored using technologies like Grafana
4. Brief mentions of data mining and HDI scenarios
Real Time Data Processing With Spark Streaming, Node.js and Redis with Visual...Brandon O'Brien
Contact:
https://www.linkedin.com/in/brandonjobrien
@hakczar
Code examples available at https://github.com/br4nd0n/spark-streaming and https://github.com/br4nd0n/spark-viz
A demo and explanation of building a streaming application using Spark Streaming, Node.js and Redis with a real time visualization. Includes discussion of internals of Spark and Spark streaming including RDD partitioning and code and data distribution and cluster resource allocation.
NoSQL datastores fall under the following categories: Key-value stores, document databases, column-family stores and graph databases. The traditional TPC-* tests are not sufficient for these heterogeneous database systems. MongoDB, CouchDB, Cassandra, HBase, Memcaches etc belong to one of 4 families and a common workload can be generated by ycsb to simulate your usecase and benchmark them.
In the world of big data we need to build services that will be able to collect massive data, save it and pass it to processing and analysis. However, building manageable, reliable services that are scalable and cost effective is not an easy task. The choice of eco-system, frameworks and programming language, as well as using solid engineering principles is also crucial for achieving this goal.
I will share our journey and insights from rebuilding a cloud service in Linux eco-system using Scala, Akka Actors and Aerospike DB, at the end of which we gained 10 folds improvement of server usage with a much lighter, stable and reliable system that handles tens of millions of requests per hour.
Meteor is a full-stack JavaScript platform that allows for sharing code between client and server. It uses Distributed Data Protocol (DDP) over WebSockets to manage bidirectional data synchronization and remote procedure calls. The Meteor stack includes templates, helpers, events, collections, publications and subscriptions, methods, routing, latency compensation, and more out-of-the-box functionality for building real-time web applications.
Building Realtime Data Pipelines with Kafka Connect and Spark StreamingJen Aman
This document discusses building real-time data pipelines with Kafka Connect and Spark Streaming. It introduces Kafka Connect as a tool for large-scale streaming data import and export for Kafka. Kafka Connect uses connectors to move data between Kafka and other data systems in a scalable, parallel, and fault-tolerant manner. It then discusses how Kafka Connect can be used together with Spark Streaming to provide real-time data integration capabilities.
HBaseConAsia2018 Track3-6: HBase at MeituanMichael Stack
The document discusses HBase multi-tenancy features including RSGroup for compute resource isolation, DNGroup for storage isolation, and replication isolation. It also covers object storage solutions in HBase like MOB and YARN log storage, as well as techniques for isolating large queries. Bugs and fixes are mentioned relating to these features.
The document summarizes MongoDB as a modern database designed to solve problems of volume, velocity, and variety of data that traditional relational databases are not well-suited for. It highlights key MongoDB features like scalability, flexible schemas, and high availability. The document also discusses how MongoDB compares favorably to other databases in security capabilities and is a good fit for applications involving user data management, content delivery, and mobile apps.
HBaseConAsia2018 Track3-7: The application of HBase in New Energy Vehicle Mon...Michael Stack
This document discusses the use of HBase in a vehicle monitoring system. It describes challenges including handling huge amounts of vehicle data from 100k vehicles generating 2TB of data daily. It outlines decisions around using Java, Kafka, HBase, and microservices. The system architecture is shown storing vehicle data in HBase with data backup. Challenges with HBase like query speed are discussed. Prospects include rewriting components in Go, splitting to microservices, and data analysis.
Gobblin provides a data ingestion and lifecycle management platform for LinkedIn's Hadoop clusters. It supports ingesting data from various sources into HDFS, and provides additional capabilities like replication, retention, optimization and compliance. Gobblin treats each dataset independently and orchestrates operators like ingestion, replication and retention through shared metadata. This allows for flexible and extensible management of LinkedIn's large and growing volume of datasets and data flows through their entire lifecycle.
Our new product (Clicktale Experience cloud) requires processing up to half a million messages per second, sessionizing each "users" journey throughout a web page. In this talk we'll discuss how we have achieved that using Spark's stateful streaming capabilities with only few servers in production, the challenges we've faced and how we've solved them. We'll also take a look at Spark 2.2 (the brand new version) and its new stateful aggregation and talk about how we've used it in order to improve performance significantly.
Azure Cosmos DB: Features, Practical Use and Optimization "GlobalLogic Ukraine
This presentation is dedicated to Azure Cosmos DB, it's history, characteristics, tasks and solutions. The presentation deals with performance optimization, practical experience of usage and an overview of the news about Cosmos DB from Microsoft Build 2017 conference (https://build.microsoft.com).
This presentation by Andriy Gorda (Engineering Manager & Lead Software Engineer, Consultant, GlobalLogic Kharkiv) was delivered at GlobalLogic Kharkiv MS TechTalk on June 13, 2017.
HBaseConAsia2018 Track2-6: Scaling 30TB's of data lake with Apache HBase and ...Michael Stack
This document summarizes a presentation on scaling a 30 TB data lake using Apache HBase and Scala. It introduces Apache HBase and Spark as technologies for building fast data platforms. It then describes a case study where they were used to architect a retail analytics platform capable of processing 4.6 billion events weekly from 30 TB of historical data. Key aspects included using HBase for data deduplication and as a master data management system, and connecting Spark to HBase using a Scala DSL for efficient querying and updates at scale. Performance was improved 5x by reengineering the data pipeline to be highly concurrent and asynchronous.
HBaseConAsia2018 Track2-1: Kerberos-based Big Data Security Solution and Prac...Michael Stack
This document discusses security practices for HBase clusters. It introduces the Hadoop Authentication Service (HAS) as a way to integrate enterprise identity management with Kerberos authentication in Hadoop. HAS supports plugins to authenticate with various systems. Alibaba uses HAS for authentication in ApsaraDB for HBase, along with optimizations like high availability for the backend storage and whitelists. The document outlines how HAS works and the benefits it provides for securing HBase deployments.
Introduction to CosmosDB - Azure Bootcamp 2018Josh Carlisle
Josh Carlisle introduces Azure Cosmos DB, a globally distributed, multi-model database service. Cosmos DB offers turnkey global distribution, high availability up to 99.999%, and low latency reads and writes typically under 10ms. It uses request units to reserve throughput and ensure service level agreements. Cosmos DB supports multiple APIs including MongoDB, SQL, Cassandra, and table storage and scales elastically.
Good Things and Hard Things of SaaS Development/OperationsSATOSHI TAGOMORI
This document discusses the good and hard things about developing and operating a SaaS platform. It describes how the backend team at Treasure Data owns and manages various components of their distributed platform. It also discusses how they have modernized their deployment process from a periodic Chef-based approach to using CodeDeploy for more frequent deployments. This allows them to move faster by doing many small releases that minimize the number of affected components and customers.
(PFC308) How Dropbox Scales Massive Workloads Using Amazon SQS | AWS re:Inven...Amazon Web Services
In this session, learn how Dropbox scales to provide one of the largest cloud storage and file sharing services in the world. Hear how Dropbox leverages Amazon EC2 to run varied workloads including thumbnail generation and document prevent, as well as document indexing to support full-text search. Dropbox presents ''Livefill'' - a generic framework built on top of Amazon SQS. Livefill enables them to trigger customizable data-processing workloads on data stored in Amazon S3 and helps them support more than 200,000 workload requests per second, spread across thousands of machines.
MongoDB World 2018: Solving Your Backup Needs Using MongoDB Ops Manager, Clou...MongoDB
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.
Technical feature review of features introduced by MongoDB 3.4 on graph capabilities, MongoDB UI tool: Compass, improvements on the replication and aggregation framework stages and utils. Operations improvements on Ops Manager and MongoDB Atlas.
MongoDB .local Bengaluru 2019: Lift & Shift MongoDB to AtlasMongoDB
Managing and scaling the infrastructure for critical business data can be a real pain. To handle this massive scale of data effectively, thousands of users of MongoDB from all around the world have migrated their large and small databases to MongoDB Atlas.
By the end of this talk, you'll have a better understanding of the “how” and “why” of it, and will be able to leverage it in your organisation with elevated confidence. I'll demo the migration of a realtime application using MongoDB from existing cloud infrastructure to MongoDB Atlas.
If you're a developer, DBA or a business stakeholder, and your organisation is using/planning to use MongoDB on-premise or with any other cloud vendor, this talk will help you to gain insights into the best way to run MongoDB.
Interactive Visualization of Streaming Data Powered by Spark by Ruhollah Farc...Spark Summit
This document discusses how to visualize streaming data using Spark. It describes how Spark Streaming can be used to process streaming data in real-time and integrate it with visualization tools. Key points include:
- Spark Streaming receives streaming data from sources like Kafka and processes it using in-memory computations in a single JVM cluster.
- The processed data can be stored in buffers like MongoDB or output to systems like MemSQL, Solr to enable interactive visualizations that update in real-time.
- A demo is shown of Twitter data being streamed and analyzed using Spark Streaming with results stored in MemSQL and Solr for visualization.
- Benefits of this approach include being able to work with streaming data
HBaseConAsia2018 Track3-5: HBase Practice at LianjiaMichael Stack
This document discusses different big data scenarios using HBase including:
1. Architecture evolution over time including olap and real-time ETL scenarios
2. The olap scenario requirements like handling billion records with sub-second queries and examples using Kylin
3. The monitor scenario showing how different systems are monitored using technologies like Grafana
4. Brief mentions of data mining and HDI scenarios
Real Time Data Processing With Spark Streaming, Node.js and Redis with Visual...Brandon O'Brien
Contact:
https://www.linkedin.com/in/brandonjobrien
@hakczar
Code examples available at https://github.com/br4nd0n/spark-streaming and https://github.com/br4nd0n/spark-viz
A demo and explanation of building a streaming application using Spark Streaming, Node.js and Redis with a real time visualization. Includes discussion of internals of Spark and Spark streaming including RDD partitioning and code and data distribution and cluster resource allocation.
NoSQL datastores fall under the following categories: Key-value stores, document databases, column-family stores and graph databases. The traditional TPC-* tests are not sufficient for these heterogeneous database systems. MongoDB, CouchDB, Cassandra, HBase, Memcaches etc belong to one of 4 families and a common workload can be generated by ycsb to simulate your usecase and benchmark them.
In the world of big data we need to build services that will be able to collect massive data, save it and pass it to processing and analysis. However, building manageable, reliable services that are scalable and cost effective is not an easy task. The choice of eco-system, frameworks and programming language, as well as using solid engineering principles is also crucial for achieving this goal.
I will share our journey and insights from rebuilding a cloud service in Linux eco-system using Scala, Akka Actors and Aerospike DB, at the end of which we gained 10 folds improvement of server usage with a much lighter, stable and reliable system that handles tens of millions of requests per hour.
Meteor is a full-stack JavaScript platform that allows for sharing code between client and server. It uses Distributed Data Protocol (DDP) over WebSockets to manage bidirectional data synchronization and remote procedure calls. The Meteor stack includes templates, helpers, events, collections, publications and subscriptions, methods, routing, latency compensation, and more out-of-the-box functionality for building real-time web applications.
Building Realtime Data Pipelines with Kafka Connect and Spark StreamingJen Aman
This document discusses building real-time data pipelines with Kafka Connect and Spark Streaming. It introduces Kafka Connect as a tool for large-scale streaming data import and export for Kafka. Kafka Connect uses connectors to move data between Kafka and other data systems in a scalable, parallel, and fault-tolerant manner. It then discusses how Kafka Connect can be used together with Spark Streaming to provide real-time data integration capabilities.
HBaseConAsia2018 Track3-6: HBase at MeituanMichael Stack
The document discusses HBase multi-tenancy features including RSGroup for compute resource isolation, DNGroup for storage isolation, and replication isolation. It also covers object storage solutions in HBase like MOB and YARN log storage, as well as techniques for isolating large queries. Bugs and fixes are mentioned relating to these features.
The document summarizes MongoDB as a modern database designed to solve problems of volume, velocity, and variety of data that traditional relational databases are not well-suited for. It highlights key MongoDB features like scalability, flexible schemas, and high availability. The document also discusses how MongoDB compares favorably to other databases in security capabilities and is a good fit for applications involving user data management, content delivery, and mobile apps.
HBaseConAsia2018 Track3-7: The application of HBase in New Energy Vehicle Mon...Michael Stack
This document discusses the use of HBase in a vehicle monitoring system. It describes challenges including handling huge amounts of vehicle data from 100k vehicles generating 2TB of data daily. It outlines decisions around using Java, Kafka, HBase, and microservices. The system architecture is shown storing vehicle data in HBase with data backup. Challenges with HBase like query speed are discussed. Prospects include rewriting components in Go, splitting to microservices, and data analysis.
Gobblin provides a data ingestion and lifecycle management platform for LinkedIn's Hadoop clusters. It supports ingesting data from various sources into HDFS, and provides additional capabilities like replication, retention, optimization and compliance. Gobblin treats each dataset independently and orchestrates operators like ingestion, replication and retention through shared metadata. This allows for flexible and extensible management of LinkedIn's large and growing volume of datasets and data flows through their entire lifecycle.
Our new product (Clicktale Experience cloud) requires processing up to half a million messages per second, sessionizing each "users" journey throughout a web page. In this talk we'll discuss how we have achieved that using Spark's stateful streaming capabilities with only few servers in production, the challenges we've faced and how we've solved them. We'll also take a look at Spark 2.2 (the brand new version) and its new stateful aggregation and talk about how we've used it in order to improve performance significantly.
Azure Cosmos DB: Features, Practical Use and Optimization "GlobalLogic Ukraine
This presentation is dedicated to Azure Cosmos DB, it's history, characteristics, tasks and solutions. The presentation deals with performance optimization, practical experience of usage and an overview of the news about Cosmos DB from Microsoft Build 2017 conference (https://build.microsoft.com).
This presentation by Andriy Gorda (Engineering Manager & Lead Software Engineer, Consultant, GlobalLogic Kharkiv) was delivered at GlobalLogic Kharkiv MS TechTalk on June 13, 2017.
HBaseConAsia2018 Track2-6: Scaling 30TB's of data lake with Apache HBase and ...Michael Stack
This document summarizes a presentation on scaling a 30 TB data lake using Apache HBase and Scala. It introduces Apache HBase and Spark as technologies for building fast data platforms. It then describes a case study where they were used to architect a retail analytics platform capable of processing 4.6 billion events weekly from 30 TB of historical data. Key aspects included using HBase for data deduplication and as a master data management system, and connecting Spark to HBase using a Scala DSL for efficient querying and updates at scale. Performance was improved 5x by reengineering the data pipeline to be highly concurrent and asynchronous.
HBaseConAsia2018 Track2-1: Kerberos-based Big Data Security Solution and Prac...Michael Stack
This document discusses security practices for HBase clusters. It introduces the Hadoop Authentication Service (HAS) as a way to integrate enterprise identity management with Kerberos authentication in Hadoop. HAS supports plugins to authenticate with various systems. Alibaba uses HAS for authentication in ApsaraDB for HBase, along with optimizations like high availability for the backend storage and whitelists. The document outlines how HAS works and the benefits it provides for securing HBase deployments.
Introduction to CosmosDB - Azure Bootcamp 2018Josh Carlisle
Josh Carlisle introduces Azure Cosmos DB, a globally distributed, multi-model database service. Cosmos DB offers turnkey global distribution, high availability up to 99.999%, and low latency reads and writes typically under 10ms. It uses request units to reserve throughput and ensure service level agreements. Cosmos DB supports multiple APIs including MongoDB, SQL, Cassandra, and table storage and scales elastically.
Good Things and Hard Things of SaaS Development/OperationsSATOSHI TAGOMORI
This document discusses the good and hard things about developing and operating a SaaS platform. It describes how the backend team at Treasure Data owns and manages various components of their distributed platform. It also discusses how they have modernized their deployment process from a periodic Chef-based approach to using CodeDeploy for more frequent deployments. This allows them to move faster by doing many small releases that minimize the number of affected components and customers.
(PFC308) How Dropbox Scales Massive Workloads Using Amazon SQS | AWS re:Inven...Amazon Web Services
In this session, learn how Dropbox scales to provide one of the largest cloud storage and file sharing services in the world. Hear how Dropbox leverages Amazon EC2 to run varied workloads including thumbnail generation and document prevent, as well as document indexing to support full-text search. Dropbox presents ''Livefill'' - a generic framework built on top of Amazon SQS. Livefill enables them to trigger customizable data-processing workloads on data stored in Amazon S3 and helps them support more than 200,000 workload requests per second, spread across thousands of machines.
MongoDB World 2018: Solving Your Backup Needs Using MongoDB Ops Manager, Clou...MongoDB
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.
Technical feature review of features introduced by MongoDB 3.4 on graph capabilities, MongoDB UI tool: Compass, improvements on the replication and aggregation framework stages and utils. Operations improvements on Ops Manager and MongoDB Atlas.
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.
MongoDB.local Atlanta: Modern Data Backup and Recovery from On-Premises to th...MongoDB
This document summarizes MongoDB's modern data backup and recovery strategies, both for on-premises and cloud environments. It outlines MongoDB's current on-premises backup architecture and its limitations. The document then introduces MongoDB's new backup strategy of taking WiredTiger checkpoints directly to long-term storage to reduce overhead. It also discusses MongoDB Atlas, which provides automatic multi-region backups and point-in-time restore capabilities. The document demos these backup features and previews future improvements like direct agent backups to optimize the process.
Solving Your Backup Needs Using Ops Manager, Cloud Manager and AtlasMongoDB
This document discusses MongoDB's disaster recovery options including Ops Manager, Cloud Manager, and Atlas. It explains that replication is not ideal for disaster recovery as corrupted data will spread, while these products offer queryable backups and point-in-time restore. Ops Manager and Cloud Manager provide continuous backups with RPOs of just a few seconds. Atlas now offers cloud provider snapshots that provide localized, faster restores at a lower cost than continuous backups. All MongoDB products aim to satisfy various RPO and RTO requirements for disaster recovery.
This document summarizes a presentation about using event sourcing with Azure Cosmos DB change feed and Azure Functions. The presentation introduces event sourcing and how Cosmos DB can be used as an event store. It describes how to consume the Cosmos DB change feed using the change feed processor library or Azure Functions. It also demonstrates how to generate materialized views of the data using the change feed to optimize queries. The demos show ingesting telemetry into Cosmos DB, consuming the change feed with Functions, and creating materialized views for current location and delivery status.
This document provides an overview and introduction to Cosmos DB. It discusses what Cosmos DB is, its data models, APIs, partitioning, and global distribution. It explains why Cosmos DB was created to address limitations of traditional databases. Key aspects covered include throughput and consistency levels, indexing, backups, failovers, and using Cosmos DB for developers and database administrators. The document also discusses migration tools, limitations, and integrations with PowerBI and geospatial data.
The document discusses new features in Oracle Database 12c including the introduction of a multitenant architecture. Key points include:
- 12c introduces a multitenant architecture that allows a single database to host many pluggable databases (PDBs). This improves consolidation and resource utilization.
- PDBs can be quickly provisioned from seed databases or cloned from other PDBs. Common operations can be performed at the container database level.
- Adaptive execution plans allow queries to dynamically switch plans at runtime if optimizer estimates prove inaccurate based on statistics collected during execution.
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.
The event, held on 27th April 2019, was part of the Global Azure Bootcamp and covered Microsoft's Cosmos DB, more specifically:
- Introduction to Cosmos DB, its features, internals, resource models, and request units.
- DEMO: Create an SQL API. Download sample .NET app. Simple queries.
- Covered Change Feed and showcased various use case scenarios.
- Detailed Global Distribution and Consistency Models implications.
- DEMO: Mongo - Lift and shift. Run simple .NET code against a MongoDB (in docker container) and cosmos.
- Introduction to Tinkerpop graphs
- DEMO: Graphs API. Download sample .NET app. Simple queries.
https://techspark.mt/global-azure-bootcamp-27th-april-2019/
This document compares DynamoDB and MongoDB, two NoSQL databases. It outlines key requirements, data models, operations, features, and internals of each. DynamoDB uses a key-value data model with tables, items, and attributes. MongoDB uses a document model with collections, documents, and fields. Both support indexing, queries, and scaling out. The document provides details on data types, partitioning, querying capabilities, and management tools of DynamoDB and MongoDB.
For our eReader development project, we had to find a persistent storage for our JSON documents. After initial scanning we zeroed into two products DynamoDB and MongoDB. These slides take a deeper dive in the selection of our JSON data store.
Azure SQL Database is a relational database-as-a-service hosted in the Azure cloud that reduces costs by eliminating the need to manage virtual machines, operating systems, or database software. It provides automatic backups, high availability through geo-replication, and the ability to scale performance by changing service tiers. Azure Cosmos DB is a globally distributed, multi-model database that supports automatic indexing, multiple data models via different APIs, and configurable consistency levels with strong performance guarantees. Azure Redis Cache uses the open-source Redis data structure store with managed caching instances in Azure for improved application performance.
This presentation was given at the LDS Tech SORT Conference 2011 in Salt Lake City. The slides are quite comprehensive covering many topics on MongoDB. Rather than a traditional presentation, this was presented as more of a Q & A session. Topics covered include. Introduction to MongoDB, Use Cases, Schema design, High availability (replication) and Horizontal Scaling (sharding).
Conceptos básicos. Seminario web 6: Despliegue de producciónMongoDB
Este es el último seminario web de la serie Conceptos básicos, en la que se realiza una introducción a la base de datos MongoDB. En este seminario web le guiaremos por el despliegue en producción.
This document discusses MongoDB and provides information on why it is useful, how it works, and best practices. Specifically, it notes that MongoDB is a noSQL database that is easy to use, scalable, and supports high performance and availability. It is well-suited for flexible schemas, embedded documents, and complex relationships. The document also covers topics like BSON, CRUD operations, indexing, map reduce, transactions, replication, and sharding in MongoDB.
The document summarizes new features in JBoss Operations Network (JBoss ON), including:
1) New chart types have been added to visualize metrics data. Storage nodes using Cassandra have also been added to improve scalability of storing large volumes of metrics data in a distributed manner.
2) Finer-grained bundle permissions allow restricting bundle creation, deployment and management based on resource groups and roles.
3) The REST API is now fully supported for both retrieving and inputting configuration data to enable out-of-band processing.
4) Upcoming versions of JBoss ON aim to reduce the agent footprint, improve support for EAP 6, and integrate with the Red Hat Access portal.
Webinar: Enterprise Data Management in the Era of MongoDB and Data LakesMongoDB
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.
The Care + Feeding of a Mongodb ClusterChris Henry
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.
This presentation contains a preview of MongoDB 3.2 upcoming release where we explore the new storage engines, aggregation framework enhancements and utility features like document validation and partial indexes.
Similar to MongoDB.local DC 2018: Solving Your Backup Needs Using MongoDB Ops Manager, Cloud Manager, and Atlas (20)
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
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.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
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.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
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.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
The document discusses guidelines for ordering fields in compound indexes to optimize query performance. It recommends the E-S-R approach: placing equality fields first, followed by sort fields, and range fields last. This allows indexes to leverage equality matches, provide non-blocking sorts, and minimize scanning. Examples show how indexes ordered by these guidelines can support queries more efficiently by narrowing the search bounds.
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
Aggregation pipeline has been able to power your analysis of data since version 2.2. In 4.2 we added more power and now you can use it for more powerful queries, updates, and outputting your data to existing collections. Come hear how you can do everything with the pipeline, including single-view, ETL, data roll-ups and materialized views.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
The document describes a methodology for data modeling with MongoDB. It begins by recognizing the differences between document and tabular databases, then outlines a three step methodology: 1) describe the workload by listing queries, 2) identify and model relationships between entities, and 3) apply relevant patterns when modeling for MongoDB. The document uses examples around modeling a coffee shop franchise to illustrate modeling approaches and techniques.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms.
How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms?
In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $.
La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
4. About Me
• MongoDB just about a year
• VMware 5 years - Focusing on Management and SaaS platform. Launched the first
Organically developed SaaS Application
• BMC / BladeLogic 5 years - Consultant, Support, Solutions Architect and Product
• Various financial organizations, operations
5. Private DBaaS: On-Prem Public DBaaS: Fully Managed
Built on the Same Code Base, Same API, Same Management UI
MongoDB Offerings
Hybrid DBaaS
6. Data Explorer
Inspect schema &
index utilization
Real-Time Performance Panel
Live telemetry: in-flight operations &
resource consumption
Performance Advisor
Always-on index
recommendations
Shared Functionality
7. Primary
Secondary
Secondary
Replication vs Disaster Recovery
MongoDB includes native replication
and automated failover to ensure
availability
• Secondaries apply operations from
the primary asynchronously
• Delayed secondaries can be
configured to reflect an earlier state of
the data set
14. Backup and Restore
Continuous & Consistent Backups with Point in Time Restore
Faster backups and
recovery
Queryable
snapshots
Backup to object
store
Cross-project
restores
15. Point-in-Time Data Recovery
• Lets you select a restore time based on your PIT window
• Restores the closest snapshot and rolls ahead
• Reduces the possibility of data loss
16. What About Small Disasters?
• The application is working fine
• But there is data missing or has
been altered
• No time to do a full restore
17. Queryable Backups
• Ability to query your snapshots and
restore data at the document level in
minutes.
• Reduces the operational overhead
associated with:
• Identifying whether data of interest
has been altered
• Pinpointing the best point in time to
restore a database
18. Sample Queryable Script
db = source.locations
db2 = destination.locations
zips = db.zipcodes
zips2 = db2.zipcodes
def restore():
print "Finding Missing Data"
query = {'state': 'CO'}
try:
cursor = zips.find(query)
except Exception as e:
print "Unexpected error:", type(e), e
for doc in cursor:
zips2.insert(doc)
21. The Latest MongoDB Features
• MongoDB Atlas comes out-of-the-box with
MongoDB 3.4, 3.6, 4.0 (When Available)
• Transactions (4.0)
• Change Streams (3.6)
• JSON Schema (3.6)
• Expressive nested array updates (3.6)
• Expressive joins: $lookup (3.6)
• Graph queries (3.4)
• Facets & expressive aggregations (3.4)
• Minor updates and major upgrades
without downtime
22. Self-service and elastic
• Deploy in minutes
• Scale up/down without
downtime
• Automated upgrades
MongoDB Atlas: Database as a
service
Global and highly available
• 50+ Regions worldwide
• Replica sets optimized for
availability
• Cross-region replication
Secure by default
• Network isolation and Peering
• Encryption in flight and at rest
• Role-based access control
• SOC 2 Type 1 / Privacy Shield
Comprehensive Monitoring
• Performance Advisor
• Dashboards w/ 100+ metrics
• Real Time Performance
• Customizable alerting
Managed Backup
• Point in Time Restore
• Queryable backups
• Consistent snapshots
Cloud Agnostic
• Easy migrations
• Consistent experience
23. Self-service and elastic
• Deploy in minutes
• Scale up/down without
downtime
• Automated upgrades
MongoDB Atlas: Managed backup
Global and highly available
• 50+ Regions worldwide
• Replica sets optimized for
availability
• Cross-region replication
Secure by default
• Network isolation and Peering
• Encryption in flight and at rest
• Role-based access control
• SOC 2 Type 1 / Privacy Shield
Comprehensive Monitoring
• Performance Advisor
• Dashboards w/ 100+ metrics
• Real Time Performance
• Customizable alerting
Cloud Agnostic
• Easy migrations
• Consistent experience
Managed Backup
• Point in Time Restore
• Queryable backups
• Consistent snapshots
24. Private DBaaS: On-Prem Public DBaaS: Fully Managed
Built on the Same Code Base, Same API, Same Management UI
The Same Features as Cloud
Manager and Ops Manager
Hybrid DBaaS
26. Cloud Provider Snapshots
• At Seattle.Local announced Cloud Provider Snapshots
• Available only on Azure
• Utilizes each providers native snapshot capabilities
• Granular Backup Region Selection
• Faster Restores
• Data Sovereignty
• Pricing is based on snapshot size, not datasize
• Less Expensive, starting at $0.34 per GB of snapshot size
33. Fully Managed Disaster Recovery
Flexibility to choose how you want to
backup your data, depending on your
requirements
Continuous
• Point-in-time restore
• Queryable snapshots
• Satisfy nearly any RPO / RTO
Snapshot
• Localized backup
• Fast restores
• The cost effective option