Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
Redis and Bloom Filters - Atlanta Java Users Group 9/2014Christopher Curtin
This document discusses using Redis to enable failing fast with Bloom filters. It provides an overview of Redis as a data structure server and key-value cache, as well as Bloom filters as a space-efficient probabilistic data structure for membership testing. By storing Bloom filters in Redis, they can be made updatable and persistent while avoiding the high costs of updating and querying large Bloom filters stored in databases. Examples are given where Redis-backed Bloom filters can be used to optimize queries and decide whether expensive operations need to be performed.
The document provides an introduction to big data concepts including what big data is, how issues with processing large amounts of data were addressed, and what Apache Hadoop is. It then discusses two use case scenarios involving using Hadoop and Apache Cassandra to solve challenges around product recommendations and managing large volumes of email data at scale.
UnConference for Georgia Southern Computer Science March 31, 2015Christopher Curtin
I presented to the Georgia Southern Computer Science ACM group. Rather than one topic for 90 minutes, I decided to do an UnConference. I presented them a list of 8-9 topics, let them vote on what to talk about, then repeated.
Each presentation was ~8 minutes, (Except Career) and was by no means an attempt to explain the full concept or technology. Only to wake up their interest.
MapReduce is a programming model that allows processing of large datasets across clusters of machines. It involves specifying map and reduce functions - map processes key-value pairs to generate intermediate pairs, and reduce merges all intermediate values with the same key. Hadoop is an open-source implementation of MapReduce that uses a distributed file system to spread data across machines and push processing to the data. Cascading provides an abstraction layer on top of Hadoop to more easily define multi-step logic without worrying about mapping and reducing. It can help with testing and avoids coding overhead of Hadoop's data structures.
This document discusses cloud computing, Hadoop, MapReduce, and Cascading. It provides an overview of these technologies and how Cascading can be used to process large datasets across clusters more easily than with traditional databases. Cascading allows defining data flows and operations to run on Hadoop in a way that handles parallelization and dependencies. This makes it easier to handle complex ETL tasks on large datasets than with a single database. The document provides examples of using Cascading for analytics on customer email marketing data.
Samba Tech is a leading video distribution and management company in Latin America. They needed a scalable and reliable infrastructure to support large video traffic fluctuations and analytics processing. Using AWS, Samba Tech was able to build a fault-tolerant architecture with quick access to additional capacity. This doubled their SLA performance and allowed deployments 15 times faster, helping them better meet customer demands.
Hadoop and Cascading provide frameworks for processing large datasets in parallel across clusters of computers. Cascading builds on Hadoop by adding additional functionality like dynamic tuple definitions, mixing non-Hadoop code between MapReduce jobs, and reusable flows. It handles scheduling jobs across Hadoop more intelligently. Real examples are given demonstrating how complex analytics that would be difficult or slow in a SQL database can be solved efficiently using Cascading on Hadoop.
Chris Curtin discusses Silverpop's journey with Hadoop. They initially used Hadoop to build flexible reports on customer data despite varying schemas. This helped with queries but was difficult to maintain. They then used Cascading to dynamically define schemas and job steps. Next, they profiled customer interactions over time which challenged Hadoop due to many small files and lack of appending. They switched to MapR which helped but recovery remained an issue. Current work includes optimizing imports, packaging the solution, and watching new real-time Hadoop technologies. The main challenges have been helping customers understand and use insights from large and complex data.
Redis and Bloom Filters - Atlanta Java Users Group 9/2014Christopher Curtin
This document discusses using Redis to enable failing fast with Bloom filters. It provides an overview of Redis as a data structure server and key-value cache, as well as Bloom filters as a space-efficient probabilistic data structure for membership testing. By storing Bloom filters in Redis, they can be made updatable and persistent while avoiding the high costs of updating and querying large Bloom filters stored in databases. Examples are given where Redis-backed Bloom filters can be used to optimize queries and decide whether expensive operations need to be performed.
The document provides an introduction to big data concepts including what big data is, how issues with processing large amounts of data were addressed, and what Apache Hadoop is. It then discusses two use case scenarios involving using Hadoop and Apache Cassandra to solve challenges around product recommendations and managing large volumes of email data at scale.
UnConference for Georgia Southern Computer Science March 31, 2015Christopher Curtin
I presented to the Georgia Southern Computer Science ACM group. Rather than one topic for 90 minutes, I decided to do an UnConference. I presented them a list of 8-9 topics, let them vote on what to talk about, then repeated.
Each presentation was ~8 minutes, (Except Career) and was by no means an attempt to explain the full concept or technology. Only to wake up their interest.
MapReduce is a programming model that allows processing of large datasets across clusters of machines. It involves specifying map and reduce functions - map processes key-value pairs to generate intermediate pairs, and reduce merges all intermediate values with the same key. Hadoop is an open-source implementation of MapReduce that uses a distributed file system to spread data across machines and push processing to the data. Cascading provides an abstraction layer on top of Hadoop to more easily define multi-step logic without worrying about mapping and reducing. It can help with testing and avoids coding overhead of Hadoop's data structures.
This document discusses cloud computing, Hadoop, MapReduce, and Cascading. It provides an overview of these technologies and how Cascading can be used to process large datasets across clusters more easily than with traditional databases. Cascading allows defining data flows and operations to run on Hadoop in a way that handles parallelization and dependencies. This makes it easier to handle complex ETL tasks on large datasets than with a single database. The document provides examples of using Cascading for analytics on customer email marketing data.
Samba Tech is a leading video distribution and management company in Latin America. They needed a scalable and reliable infrastructure to support large video traffic fluctuations and analytics processing. Using AWS, Samba Tech was able to build a fault-tolerant architecture with quick access to additional capacity. This doubled their SLA performance and allowed deployments 15 times faster, helping them better meet customer demands.
Hadoop and Cascading provide frameworks for processing large datasets in parallel across clusters of computers. Cascading builds on Hadoop by adding additional functionality like dynamic tuple definitions, mixing non-Hadoop code between MapReduce jobs, and reusable flows. It handles scheduling jobs across Hadoop more intelligently. Real examples are given demonstrating how complex analytics that would be difficult or slow in a SQL database can be solved efficiently using Cascading on Hadoop.
Chris Curtin discusses Silverpop's journey with Hadoop. They initially used Hadoop to build flexible reports on customer data despite varying schemas. This helped with queries but was difficult to maintain. They then used Cascading to dynamically define schemas and job steps. Next, they profiled customer interactions over time which challenged Hadoop due to many small files and lack of appending. They switched to MapR which helped but recovery remained an issue. Current work includes optimizing imports, packaging the solution, and watching new real-time Hadoop technologies. The main challenges have been helping customers understand and use insights from large and complex data.
This session is recommended for anyone interested in building real-time streaming applications using AWS. In this session, you will get a deep understanding of how data can be ingested by Amazon Kinesis and made available for real-time analysis and processing. We’ll also show how you can leverage the Kinesis client to make your applications highly available and fault tolerant. We’ll explore various design considerations in implementing real-time solutions and explain key concepts against the backdrop of an actual use case. Finally, we’ll situate stream processing in the broader context of your big data applications.
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
Python Awareness for Exploration and Production Students and ProfessionalsYohanes Nuwara
This was presented in a series of webinar organized by SPE Asia Pacific University and SPE Northern Emirates virtually in Malaysia. In this webinar, I presented a motivation presentation for students and young professionals on the education of programming for petroleum engineering and geoscience domains. I showcased some of my open-source works written in Python.
Are you running a database in the cloud? Worried that you're doing it wrong?
Engine Yard supports a broad set of databases with flexibility for customers to modify and configure. However, freedom to adapt and extend standard functionality comes with unexpected negative consequences: modifications can seriously affect durability and performance. I've observed common problems, patterns and best practices with big (and not so big) data. I'll highlight the most common pitfalls and discuss how to avoid them.
Video for this talk is available here: http://vimeo.com/83755776
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
• Reference architectures for popular use cases, including: connected devices (IoT), log streaming, real-time intelligence, and analytics.
• The AWS big data portfolio of services, including Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR) and Redshift.
• The latest relational database engine, Amazon Aurora - a MySQL-compatible, highly-available relational database engine which provides up to five times better performance than MySQL at a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Business intelligence is often described as a set of methodologies and technologies that transform raw data into meaningful and useful information for business purposes. But this simple description hides many technical challenges IT teams struggle with. This session will show how to build business intelligence applications leveraging AWS, from the raw data import, consumption and storage down to the information production. We will also cover best practices for services such as Amazon Redshift or Amazon RDS, and how to use applications such as SAP Hana, Jaspersoft and others.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
How Hadoop Revolutionized Data Warehousing at Yahoo and FacebookAmr Awadallah
Hadoop was developed to solve problems with data warehousing systems at Yahoo and Facebook that were limited in processing large amounts of raw data in real-time. Hadoop uses HDFS for scalable storage and MapReduce for distributed processing. It allows for agile access to raw data at scale for ad-hoc queries, data mining and analytics without being constrained by traditional database schemas. Hadoop has been widely adopted for large-scale data processing and analytics across many companies.
2 years ago if someone had claimed they could stand up a petabyte scale data warehouse in under an hour and then have a non-technical business user querying it live 30 minutes later without knowing any SQL or coding language, they would have been laughed out of the room. These days, that’s called taking advantage of disruptive technology. Amazon Web Services and Tableau Software have shifted the entire paradigm by which organizations not only store and access their data, but ultimately how they innovate with it. The fast, scalable, and inexpensive services that AWS provides for housing data combined with Tableau’s unbelievably flexible and user friendly visual analytic solution means that within hours an organization can securely put the power of their massive data assets into the hands of their domain experts without expensive overhead or lengthy ramp-up time. Attend this webinar to learn how Amazon Web Services and Tableau Software are leveraged together everyday to: • Empower visual ad-hoc data discovery against big data • Revolutionize corporate reporting and dashboards • Promote data driven decision making at every level The presentation will include: • A live demonstration of AWS and Tableau working together • A real customer case study focused on fraud detection and online video metrics • Live Q&A and an opportunity to trial both solutions
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
Amazon Kinesis Data Analytics gives us to tools to run SQL queries against active data streams. We'll look at how we can performance real-time log analytics and q build entire streaming applications using SQL, so that you can gain actionable insights promptly.
Fishing Graphs in a Hadoop Data Lake by Jörg Schad and Max Neunhoeffer at Big...Big Data Spain
The document discusses using a graph database to store and query graph data stored in a Hadoop data lake more efficiently. It describes the limitations of the typical approach of using Spark/GraphFrames on HDFS for graph queries. A graph database allows for faster ad hoc graph queries by leveraging graph traversals. The document proposes using a multi-model database that combines a document store, graph database, and key-value store with a common query language. It suggests this approach could run on a DC/OS cluster for easy deployment and management of resources. Examples show importing data into ArangoDB and running graph queries.
This document provides an overview of 4 solutions for processing big data using Hadoop and compares them. Solution 1 involves using core Hadoop processing without data staging or movement. Solution 2 uses BI tools to analyze Hadoop data after a single CSV transformation. Solution 3 creates a data warehouse in Hadoop after a single transformation. Solution 4 implements a traditional data warehouse. The solutions are then compared based on benefits like cloud readiness, parallel processing, and investment required. The document also includes steps for installing a Hadoop cluster and running sample MapReduce jobs and Excel processing.
For all organizations looking to glean insights from their data, it is essential to deploy the right environment to successfully support analytics workloads. Learn about the different block storage options from AWS and discuss with our experts on how to select the best option for your big data analytics workloads. We will demonstrate how to setup, select, and modify volume types to right size your environment needs.
The document discusses Amazon's use of AWS analytics technologies. It describes Amazon's enterprise data warehouse, which stores over 5 petabytes of integrated data from multiple sources. It faces challenges from rapid data growth and limited IT budgets. Amazon is addressing this by building a data lake called "Andes" that stores data in S3 and enables analytics using services like Redshift, EMR, and Athena. This provides scalability and choices for SQL, machine learning, and other analytic approaches.
The document discusses business intelligence for big data using Hadoop. It describes how 90% of companies are using or plan to use Hadoop to transform structured or semi-structured data for analysis and reporting. While Hadoop provides scalability through distributed processing and storage, its MapReduce programming model makes data transformation difficult for developers accustomed to graphical tools. The document traces how Google and Yahoo developed MapReduce for specific use cases of indexing the internet at massive scales, and how it has since been generalized beyond those specific needs.
Collecting, maintaining, and analyzing data is key to keeping pace within any industry today. In addition to being a critical competitive asset, maintaining corporate data requires careful foundational planning to ensure that the data is secure at all stages. Your big data may include not only proprietary non-public information, but also controlled data that must adhere to regulations such as HIPAA or ITAR. Securing this data while maintaining access for authorized data analytics and reporting workloads can pose significant challenges. In this talk, you’ll learn about strategies leveraging tools such as AWS Identity and Access Management (IAM), AWS Key Management Service (KMS) , Amazon S3, and Amazon EMR to secure your big data workloads in the cloud.
Level: 200
Speaker: Hannah Marlowe - Consultant, Federal, WWPS Professional Services
by Ben Willett, Solutions Architect, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Databases & Analytics AWS re:invent 2019 RecapSungmin Kim
Amazon provides fully managed database services for relational and non-relational databases. It offers services like Amazon RDS for relational databases, DynamoDB for non-relational databases, Amazon ElastiCache for caching, and Amazon Redshift for data warehousing and analytics. These services take care of provisioning, patching, backups and provide high availability and security. Amazon also provides analytics services like Athena, EMR and Elasticsearch to analyze large amounts of data.
BigDL Deep Learning in Apache Spark - AWS re:invent 2017Dave Nielsen
In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent.
Lean Enterprise, Microservices and Big DataStylight
This document discusses enabling the lean enterprise through technologies like microservices, continuous integration/deployment, and cloud computing. It begins by defining the lean enterprise and the OODA loop concept. It then explains how technologies like AWS, big data, and microservices can help organizations continuously observe, orient, decide, and act. Specific AWS services like EC2, EMR, Kinesis, Redshift, S3, and DynamoDB are reviewed. The benefits of breaking up monolithic systems into microservices and implementing devops practices like CI/CD are also summarized.
Distributed-ness: Distributed computing & the cloudsRobert Coup
Discussion on distributed apps and the cloud resources available to support them. Some discussion on the XMPP/Jabber based messaging system we use at Koordinates. Part of the seminar series for the Wellington Summer of Code programme.
This session is recommended for anyone interested in building real-time streaming applications using AWS. In this session, you will get a deep understanding of how data can be ingested by Amazon Kinesis and made available for real-time analysis and processing. We’ll also show how you can leverage the Kinesis client to make your applications highly available and fault tolerant. We’ll explore various design considerations in implementing real-time solutions and explain key concepts against the backdrop of an actual use case. Finally, we’ll situate stream processing in the broader context of your big data applications.
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
Python Awareness for Exploration and Production Students and ProfessionalsYohanes Nuwara
This was presented in a series of webinar organized by SPE Asia Pacific University and SPE Northern Emirates virtually in Malaysia. In this webinar, I presented a motivation presentation for students and young professionals on the education of programming for petroleum engineering and geoscience domains. I showcased some of my open-source works written in Python.
Are you running a database in the cloud? Worried that you're doing it wrong?
Engine Yard supports a broad set of databases with flexibility for customers to modify and configure. However, freedom to adapt and extend standard functionality comes with unexpected negative consequences: modifications can seriously affect durability and performance. I've observed common problems, patterns and best practices with big (and not so big) data. I'll highlight the most common pitfalls and discuss how to avoid them.
Video for this talk is available here: http://vimeo.com/83755776
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
• Reference architectures for popular use cases, including: connected devices (IoT), log streaming, real-time intelligence, and analytics.
• The AWS big data portfolio of services, including Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR) and Redshift.
• The latest relational database engine, Amazon Aurora - a MySQL-compatible, highly-available relational database engine which provides up to five times better performance than MySQL at a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Business intelligence is often described as a set of methodologies and technologies that transform raw data into meaningful and useful information for business purposes. But this simple description hides many technical challenges IT teams struggle with. This session will show how to build business intelligence applications leveraging AWS, from the raw data import, consumption and storage down to the information production. We will also cover best practices for services such as Amazon Redshift or Amazon RDS, and how to use applications such as SAP Hana, Jaspersoft and others.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
How Hadoop Revolutionized Data Warehousing at Yahoo and FacebookAmr Awadallah
Hadoop was developed to solve problems with data warehousing systems at Yahoo and Facebook that were limited in processing large amounts of raw data in real-time. Hadoop uses HDFS for scalable storage and MapReduce for distributed processing. It allows for agile access to raw data at scale for ad-hoc queries, data mining and analytics without being constrained by traditional database schemas. Hadoop has been widely adopted for large-scale data processing and analytics across many companies.
2 years ago if someone had claimed they could stand up a petabyte scale data warehouse in under an hour and then have a non-technical business user querying it live 30 minutes later without knowing any SQL or coding language, they would have been laughed out of the room. These days, that’s called taking advantage of disruptive technology. Amazon Web Services and Tableau Software have shifted the entire paradigm by which organizations not only store and access their data, but ultimately how they innovate with it. The fast, scalable, and inexpensive services that AWS provides for housing data combined with Tableau’s unbelievably flexible and user friendly visual analytic solution means that within hours an organization can securely put the power of their massive data assets into the hands of their domain experts without expensive overhead or lengthy ramp-up time. Attend this webinar to learn how Amazon Web Services and Tableau Software are leveraged together everyday to: • Empower visual ad-hoc data discovery against big data • Revolutionize corporate reporting and dashboards • Promote data driven decision making at every level The presentation will include: • A live demonstration of AWS and Tableau working together • A real customer case study focused on fraud detection and online video metrics • Live Q&A and an opportunity to trial both solutions
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
Amazon Kinesis Data Analytics gives us to tools to run SQL queries against active data streams. We'll look at how we can performance real-time log analytics and q build entire streaming applications using SQL, so that you can gain actionable insights promptly.
Fishing Graphs in a Hadoop Data Lake by Jörg Schad and Max Neunhoeffer at Big...Big Data Spain
The document discusses using a graph database to store and query graph data stored in a Hadoop data lake more efficiently. It describes the limitations of the typical approach of using Spark/GraphFrames on HDFS for graph queries. A graph database allows for faster ad hoc graph queries by leveraging graph traversals. The document proposes using a multi-model database that combines a document store, graph database, and key-value store with a common query language. It suggests this approach could run on a DC/OS cluster for easy deployment and management of resources. Examples show importing data into ArangoDB and running graph queries.
This document provides an overview of 4 solutions for processing big data using Hadoop and compares them. Solution 1 involves using core Hadoop processing without data staging or movement. Solution 2 uses BI tools to analyze Hadoop data after a single CSV transformation. Solution 3 creates a data warehouse in Hadoop after a single transformation. Solution 4 implements a traditional data warehouse. The solutions are then compared based on benefits like cloud readiness, parallel processing, and investment required. The document also includes steps for installing a Hadoop cluster and running sample MapReduce jobs and Excel processing.
For all organizations looking to glean insights from their data, it is essential to deploy the right environment to successfully support analytics workloads. Learn about the different block storage options from AWS and discuss with our experts on how to select the best option for your big data analytics workloads. We will demonstrate how to setup, select, and modify volume types to right size your environment needs.
The document discusses Amazon's use of AWS analytics technologies. It describes Amazon's enterprise data warehouse, which stores over 5 petabytes of integrated data from multiple sources. It faces challenges from rapid data growth and limited IT budgets. Amazon is addressing this by building a data lake called "Andes" that stores data in S3 and enables analytics using services like Redshift, EMR, and Athena. This provides scalability and choices for SQL, machine learning, and other analytic approaches.
The document discusses business intelligence for big data using Hadoop. It describes how 90% of companies are using or plan to use Hadoop to transform structured or semi-structured data for analysis and reporting. While Hadoop provides scalability through distributed processing and storage, its MapReduce programming model makes data transformation difficult for developers accustomed to graphical tools. The document traces how Google and Yahoo developed MapReduce for specific use cases of indexing the internet at massive scales, and how it has since been generalized beyond those specific needs.
Collecting, maintaining, and analyzing data is key to keeping pace within any industry today. In addition to being a critical competitive asset, maintaining corporate data requires careful foundational planning to ensure that the data is secure at all stages. Your big data may include not only proprietary non-public information, but also controlled data that must adhere to regulations such as HIPAA or ITAR. Securing this data while maintaining access for authorized data analytics and reporting workloads can pose significant challenges. In this talk, you’ll learn about strategies leveraging tools such as AWS Identity and Access Management (IAM), AWS Key Management Service (KMS) , Amazon S3, and Amazon EMR to secure your big data workloads in the cloud.
Level: 200
Speaker: Hannah Marlowe - Consultant, Federal, WWPS Professional Services
by Ben Willett, Solutions Architect, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Databases & Analytics AWS re:invent 2019 RecapSungmin Kim
Amazon provides fully managed database services for relational and non-relational databases. It offers services like Amazon RDS for relational databases, DynamoDB for non-relational databases, Amazon ElastiCache for caching, and Amazon Redshift for data warehousing and analytics. These services take care of provisioning, patching, backups and provide high availability and security. Amazon also provides analytics services like Athena, EMR and Elasticsearch to analyze large amounts of data.
BigDL Deep Learning in Apache Spark - AWS re:invent 2017Dave Nielsen
In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent.
Lean Enterprise, Microservices and Big DataStylight
This document discusses enabling the lean enterprise through technologies like microservices, continuous integration/deployment, and cloud computing. It begins by defining the lean enterprise and the OODA loop concept. It then explains how technologies like AWS, big data, and microservices can help organizations continuously observe, orient, decide, and act. Specific AWS services like EC2, EMR, Kinesis, Redshift, S3, and DynamoDB are reviewed. The benefits of breaking up monolithic systems into microservices and implementing devops practices like CI/CD are also summarized.
Distributed-ness: Distributed computing & the cloudsRobert Coup
Discussion on distributed apps and the cloud resources available to support them. Some discussion on the XMPP/Jabber based messaging system we use at Koordinates. Part of the seminar series for the Wellington Summer of Code programme.
Black Friday and Cyber Monday- Best Practices for Your E-Commerce DatabaseTim Vaillancourt
This document provides best practices for scaling e-commerce databases for Black Friday and Cyber Monday. It discusses scaling both synchronous and asynchronous applications, efficiently using data at scale through techniques like caching, queues, and counters. It also covers scaling out through techniques like sharding, pre-sharding, and kill switches. Testing performance and capacity, as well as asking the right questions at development time are also recommended.
Jet Reports es la herramienta para construir el mejor BI y de forma mas rapida CLARA CAMPROVIN
Análisis empresariales cuando los necesite, en cualquier lugar
Jet Enterprise es una solución de inteligencia empresarial y generación de informes desarrollada específicamente para satisfacer las necesidades propias de los usuarios de Microsoft Dynamics. Ahora puede juntar toda su información en un mismo lugar y permitir que quien usted quiera de la organización realice fácilmente sofisticados análisis empresariales desde cualquier sitio. Capacite a los usuarios para tomar mejores decisiones, más rápido, prácticamente con cualquier dispositivo.
Con Jet Enterprise dispone de:
Una solución completa de inteligencia empresarial y generación de informes, lista para usar en solo 2 horas
Más de 80 paneles y plantillas de informes
7 cubos pregenerados personalizables
Un almacén de datos
Integración directa con sus datos de Microsoft Dynamics y posibilidad de conectarse a otros sistemas empresariales pertinentes
Posibilidad de crear paneles en cuestión de minutos, sin necesidad de conocer la estructura de datos subyacente
Jet Mobile opcional, para acceder a sus datos desde cualquier sitio a través de un navegador web o un dispositivo móvil
Una plataforma robusta de automatización y personalización del almacenamiento de datos
«Comenzamos con datos de Sage Pro, datos de NAV 2009 y, además, datos incorporados de la nueva empresa que habíamos adquirido, por lo que ahora estamos usando tres sistemas de datos. Las ventajas de combinar los tres sistemas en Jet Enterprise han sido enormes».
– Davis & Shirtliff
Éxito inmediato = rápido ROI y bajo coste de propiedad
Muchas soluciones de inteligencia empresarial conllevan costes ocultos, como implementaciones prolongadas y difíciles, personalizaciones caras y precio elevado de las licencias cuando se amplían a un gran número de usuarios. Jet Enterprise se suele instalar en unas dos horas, requiere un nivel mínimo de formación de los usuarios y ofrece licencias para un número ilimitado de usuarios. Los usuarios habitualmente experimentan un incremento de los ingresos brutos en los primeros 12 meses de uso.
AWS has different pricing models to match your needs. One example is the different instance types available such as On-Demand, Reserved and Spot Instances. Customers can develop cost-saving strategies based upon their usage patterns, models and growth expectations. In some cases, a set of larger instances can be cheaper than multiple small instances. Learn how to size your AWS applications to maximize your use and minimize your spend. Companies such as Pinterest take very active roles to constantly reduce their spend; learn how they do it and develop your own cost-saving approaches.
This document discusses how to reduce spending on AWS through various techniques:
1. Paying for cloud resources only when they are used through the pay-as-you-go model avoids upfront costs and allows turning off unused capacity.
2. Using reserved instances when capacity needs are predictable provides significant discounts compared to on-demand pricing.
3. Architecting applications in a "cost aware" manner, such as leveraging caching, auto-scaling, managed services, and right-sizing instances can optimize costs.
4. Taking advantage of AWS's economies of scale through consolidated billing and free services helps lower overall spend. Planning workload usage of spot instances can achieve up to 85% savings.
Take Action: The New Reality of Data-Driven BusinessInside Analysis
The Briefing Room with Dr. Robin Bloor and WebAction
Live Webcast on July 23, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=360d371d3a49ad256942f55350aa0a8b
The waiting used to be the hardest part, but not anymore. Today’s cutting-edge enterprises can seize opportunities faster than ever, thanks to an array of technologies that enable real-time responsiveness across the spectrum of business processes. Early adopters are solving critical business challenges by enabling the rapid-fire design, development and production of very specific applications. Functionality can range from improved customer engagement to dynamic machine-to-machine interactions.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, who will tout a new era in data-driven organizations, and why a data flow architecture will soon be critical for industry leaders. He’ll be briefed by Sami Akbay of WebAction, who will showcase his company’s real-time data management platform, which combines all the component parts needed to access, process and leverage data big and small. He’ll explain how this new approach can provide game-changing power to organizations of all types and sizes.
Visit InsideAnlaysis.com for more information.
Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use work load management, tune your queries, and use Amazon Redshift's interleaved sorting features. Finally, learn how to use these best practices to give their entire organization access to analytic insights at scale.
Presented by: Alex Sinner, Solutions Architecture PMO, Amazon Web Services
Customer Guest: Luuk Linssen, Product Manager, Bannerconnect
Horses for Courses: Database RoundtableEric Kavanagh
The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to info@insideanalysis.com, or tweet with #DBSurvival.
Introduction to Microsoft Azure. Covers the change to a cloud development paradigm. Motivations for the change, Pricing structures, and an exercise in IT portfolio evaluation.
"Building Data Warehouse with Google Cloud Platform", Artem NikulchenkoFwdays
In this talk, we would explore available options for building Data Warehouse for data-oriented business using Google Cloud Platform. We will start by discussing why Data Warehouse can be needed, move to the differences between "traditional" and Cloud Data Warehouses, and finally discuss steps and options for building your own Data Warehouse.
The document discusses using MapReduce for a sequential web access-based recommendation system. It explains how web server logs could be mapped to create a pattern tree showing frequent sequences of accessed web pages. When making recommendations for a user, their access pattern would be compared to patterns in the tree to find matching branches to suggest. MapReduce is well-suited for this because it can efficiently process and modify the large, dynamic tree structure across many machines in a fault-tolerant way.
Part 2 of a 2 part presentation that I did in 2009, this presentation covers more about unstructured data, and operational data vault components. YES, even then I was commenting on how this market will evolve. IF you want to use these slides, please let me know, and add: "(C) Dan Linstedt, all rights reserved, http://LearnDataVault.com" in a VISIBLE fashion on your slides.
This document provides an overview of Microsoft Azure and the benefits of cloud computing. It discusses:
- Microsoft's commitment to the cloud with 70% of employees now working on cloud-related projects, rising to 90% in a year.
- The two main reasons for using the cloud are to improve business strategy and the bottom line by gaining efficiencies and flexibility compared to traditional IT infrastructure.
- The key components of Azure including web and worker roles, storage options, and SQL Azure. It also discusses DevFabric for local development and testing.
- Options for getting started with Azure including installing the SDK and training kit on your own machine or using a pre-configured virtual machine.
Google Cloud Platform is a cloud computing platform by Google that offers hosting on the same supporting infrastructure that Google uses internally for end-user products like Google Search and YouTube. Cloud Platform provides developer products to build a range of programs from simple websites to complex applications.
Google Cloud Platform is a part of a suite of enterprise solutions from Google for Work and provides a set of modular cloud-based services with a host of development tools. For example, hosting and computing, cloud storage, data storage, translations APIs and prediction APIs.
Topic Covered
Why Google Cloud Platform ?
Google Cloud Platform Services: First Insight !!!
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for 1/10th the traditional cost. This session will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs. We’ll also cover the recently announced Redshift Spectrum, which allows you to query unstructured data directly from Amazon S3.
Convince your boss to go Serverless at AWS User Group Tirupathi and Serverles...Vadym Kazulkin
TCO of Serverless application. How Serverless helps us to be productive, write less code and implement evolutionary architectures. How to measure productivity to see you're on track with Serverless
The Presentation Talks about how Cloud Computing is Big Data's Best Friend and How AWS Cloud Components Fit in to complete your Big Data Life Cycle.
Agenda:
- How Big is Big Data Actually growing?
- How Cloud has the potential to become Big Data's Best Friend
- A tour on The Big Data Life Cycle
- How AWS Cloud Components Fit in to this Life Cycle
- A Case Study of Our Log Analytics Tool Cloudlytics, using Big Data Implementation
on AWS Cloud.
This document summarizes a presentation given on AWS vs Azure. It begins with an overview of AWS, describing its many services like S3, EC2, VPC, IAM, ELB, RDS, DynamoDB and more. It notes AWS is an "alphabet soup" but has good documentation. The presentation then covers the goods of AWS like elasticity, programmability, variety of services and large user base. The bads include potential higher costs, complexity and service outages. The uglies include vendor lock-in risks and need to rewrite existing systems. It concludes by providing recommendations for different roles on best getting started with AWS.
Similar to Ch-ch-ch-ch-changes....Stitch Triggers - Andrew Morgan (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.
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
DevOps Consulting Company | Hire DevOps Servicesseospiralmantra
Spiral Mantra excels in providing comprehensive DevOps services, including Azure and AWS DevOps solutions. As a top DevOps consulting company, we offer controlled services, cloud DevOps, and expert consulting nationwide, including Houston and New York. Our skilled DevOps engineers ensure seamless integration and optimized operations for your business. Choose Spiral Mantra for superior DevOps services.
https://www.spiralmantra.com/devops/
Liberarsi dai framework con i Web Component.pptxMassimo Artizzu
In Italian
Presentazione sulle feature e l'utilizzo dei Web Component nell sviluppo di pagine e applicazioni web. Racconto delle ragioni storiche dell'avvento dei Web Component. Evidenziazione dei vantaggi e delle sfide poste, indicazione delle best practices, con particolare accento sulla possibilità di usare web component per facilitare la migrazione delle proprie applicazioni verso nuovi stack tecnologici.
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
Nashik's top web development company, Upturn India Technologies, crafts innovative digital solutions for your success. Partner with us and achieve your goals
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
React.js, a JavaScript library developed by Facebook, has gained immense popularity for building user interfaces, especially for single-page applications. Over the years, React has evolved and expanded its capabilities, becoming a preferred choice for mobile app development. This article will explore why React.js is an excellent choice for the Best Mobile App development company in Noida.
Visit Us For Information: https://www.linkedin.com/pulse/what-makes-reactjs-stand-out-mobile-app-development-rajesh-rai-pihvf/
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
🏎️Tech Transformation: DevOps Insights from the Experts 👩💻campbellclarkson
Connect with fellow Trailblazers, learn from industry experts Glenda Thomson (Salesforce, Principal Technical Architect) and Will Dinn (Judo Bank, Salesforce Development Lead), and discover how to harness DevOps tools with Salesforce.
A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...kalichargn70th171
In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.
Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
These brief, focused sessions ensure that safety is a continual conversation rather than a one-time event, which helps keep safety protocols fresh in employees' minds. Studies have shown that shorter, more frequent training sessions are more effective for retention and behavior change compared to longer, infrequent sessions.
Engaging workers regularly, toolbox talks promote a culture of safety, empower employees to voice concerns, and ultimately reduce the likelihood of accidents and injuries on site.
The traditional method of conducting safety talks with paper documents and lengthy meetings is not only time-consuming but also less effective. Manual tracking of attendance and compliance is prone to errors and inconsistencies, leading to gaps in safety communication and potential non-compliance with OSHA regulations. Switching to a digital solution like Safelyio offers significant advantages.
Safelyio automates the delivery and documentation of safety talks, ensuring consistency and accessibility. The microlearning approach breaks down complex safety protocols into manageable, bite-sized pieces, making it easier for employees to absorb and retain information.
This method minimizes disruptions to work schedules, eliminates the hassle of paperwork, and ensures that all safety communications are tracked and recorded accurately. Ultimately, using a digital platform like Safelyio enhances engagement, compliance, and overall safety performance on site. https://safelyio.com/
DECODING JAVA THREAD DUMPS: MASTER THE ART OF ANALYSISTier1 app
Are you ready to unlock the secrets hidden within Java thread dumps? Join us for a hands-on session where we'll delve into effective troubleshooting patterns to swiftly identify the root causes of production problems. Discover the right tools, techniques, and best practices while exploring *real-world case studies of major outages* in Fortune 500 enterprises. Engage in interactive lab exercises where you'll have the opportunity to troubleshoot thread dumps and uncover performance issues firsthand. Join us and become a master of Java thread dump analysis!
WWDC 2024 Keynote Review: For CocoaCoders AustinPatrick Weigel
Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
Superpower Your Apache Kafka Applications Development with Complementary Open...Paul Brebner
Kafka Summit talk (Bangalore, India, May 2, 2024, https://events.bizzabo.com/573863/agenda/session/1300469 )
Many Apache Kafka use cases take advantage of Kafka’s ability to integrate multiple heterogeneous systems for stream processing and real-time machine learning scenarios. But Kafka also exists in a rich ecosystem of related but complementary stream processing technologies and tools, particularly from the open-source community. In this talk, we’ll take you on a tour of a selection of complementary tools that can make Kafka even more powerful. We’ll focus on tools for stream processing and querying, streaming machine learning, stream visibility and observation, stream meta-data, stream visualisation, stream development including testing and the use of Generative AI and LLMs, and stream performance and scalability. By the end you will have a good idea of the types of Kafka “superhero” tools that exist, which are my favourites (and what superpowers they have), and how they combine to save your Kafka applications development universe from swamploads of data stagnation monsters!
3. Agenda
1. Database Triggers
2. RDBMS vs. Stitch Triggers
3. Evolution of computing and App architectures
4. MongoDB Stitch – Serverless Platform
5. Oplog -> Change Streams -> Stitch Triggers
6. MongoDB Swag store demo
4. Why Triggers
• Propagate changes in real
time to multiple
apps/channels
• Apps act on the changes as
they see fit
• No code changes to original
application
• Simpler development, faster
time to market, lower risk
Legacy
App
Mobile App
Web App
Microservic
e
Cloud
Service
5.
6. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular table or view in
a database. The trigger is mostly used for maintaining
the integrity of the information on the database. For example, when
a new record (representing a new worker) is added to the employees
table, new records should also be created in the tables of the taxes,
vacations and salaries. Triggers can also be used to log historical
data, for example to keep track of employees' previous salaries.
https://en.wikipedia.org/wiki/Database_trigger
7. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular table or view in
a database.
https://en.wikipedia.org/wiki/Database_trigger
8. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular table or view in
a database.
https://en.wikipedia.org/wiki/Database_trigger
9. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
https://en.wikipedia.org/wiki/Database_trigger
10. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is mostly used for maintaining the integrity of the
information on the database.
11. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is mostly used for maintaining the integrity of the
information on the database.
12. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is partly used for maintaining the integrity of the
information on the database but also for exciting other stuff.
13. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is partly used for maintaining the integrity of the
information on the database but also for exciting other stuff.
14. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is partly used for maintaining the integrity of the
information on the database but also for exciting other stuff. For
example, when a new record (representing a new worker) is added to
the employees table, new records should also be created in the tables
of the taxes, vacations and salaries.
15. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is partly used for maintaining the integrity of the
information on the database but also for exciting other stuff. For
example, when a new record (representing a new worker) is added to
the employees table, new records should also be created in the tables
of the taxes, vacations and salaries.
16. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is partly used for maintaining the integrity of the
information on the database but also for exciting other stuff. For
example, when you need a database update from a legacy application
to trigger actions in a new App, or you want to add exciting new
features to an existing app.
17. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is partly used for maintaining the integrity of the
information on the database but also for exciting other stuff. For
example, when you need a database update from a legacy application
to trigger actions in a new App, or you want to add exciting new
features to an existing app. Triggers can also be used to log historical
data, for example to keep track of employees' previous salaries.
18. What Are Database Triggers
A database trigger is procedural code that is automatically executed
in response to certain events on a particular collection in a database.
The trigger is partly used for maintaining the integrity of the
information on the database but also for exciting other stuff. For
example, when you need a database update from a legacy application
to trigger actions in a new App, or you want to add exciting new
features to an existing app. Triggers can also be used to log historical
data, for example to keep track of employees' previous salaries.
19. RDBMS vs. Stitch Triggers
RDBMS Triggers Stitch Triggers
Runs within/competes for
resources with the database
Runs in our Stitch infrastructure
20. RDBMS vs. Stitch Triggers
RDBMS Triggers Stitch Triggers
Runs within/competes for
resources with the database
Runs in our Stitch infrastructure
Used to impose referential Notifications, side effects,
integrating apps
21. RDBMS vs. Stitch Triggers
RDBMS Triggers Stitch Triggers
Runs within/competes for
resources with the database
Runs in our Stitch infrastructure
Used to impose referential Notifications, side effects,
integrating apps
Written in proprietary languages JavaScript (ES6)
22. RDBMS vs. Stitch Triggers
RDBMS Triggers Stitch Triggers
Runs within/competes for
resources with the database
Runs in our Stitch infrastructure
Used to impose referential Notifications, side effects,
integrating apps
Written in proprietary languages JavaScript (ES6)
Invoked before | after DB change Triggers invoked after DB
use Stitch Functions for pre-
change requirements
23. Large Data Center Virtual Machines VMs in the Cloud (EC2) Containers Serverless
Manage H/W
Complex ops
Manage less H/W
Complex ops
Size & provision VMs
Simpler ops
Size & provision containers
Simpler ops
Just send in requests
Negligible ops
Wasted resources Better utilization Rent, not buy Rent with less waste Pay as you go
Evolution of Computing Models
$$$$ $$$ $$ $
24. Large Data Center Virtual Machines VMs in the Cloud (EC2) Containers Serverless
Manage H/W
Complex ops
Manage less H/W
Complex ops
Size & provision VMs
Simpler ops
Size & provision containers
Simpler ops
Just send in requests
Negligible ops
Wasted resources Better utilization Rent, not buy Rent with less waste Pay as you go
Evolution of Computing Models
$$$$$ $$$$ $$$ $$ $
25. Large Data Center Virtual Machines VMs in the Cloud (EC2) Containers Serverless
Manage H/W
Complex ops
Manage less H/W
Complex ops
Size & provision VMs
Simpler ops
Size & provision containers
Simpler ops
Just send in requests
Negligible ops
Wasted resources Better utilization Rent, not buy Rent with less waste Pay as you go
Evolution of Computing Models
$$$$$ $$$$ $$$ $$ $
26. Large Data Center Virtual Machines VMs in the Cloud (EC2) Containers Serverless
Manage H/W
Complex ops
Manage less H/W
Complex ops
Size & provision VMs
Simpler ops
Size & provision containers
Simpler ops
Just send in requests
Negligible ops
Wasted resources Better utilization Rent, not buy Rent with less waste Pay as you go
Evolution of Computing Models
$$$$$ $$$$ $$$ $$ $
27. Large Data Center Virtual Machines VMs in the Cloud (EC2) Containers Serverless
Manage H/W
Complex ops
Manage less H/W
Complex ops
Size & provision VMs
Simpler ops
Size & provision containers
Simpler ops
Just send in requests
Negligible ops
Wasted resources Better utilization Rent, not buy Rent with less waste Pay as you go
Evolution of Computing Models
$$$$$ $$$$ $$$ $$ $
28. Large Data Center Virtual Machines VMs in the Cloud (EC2) Containers Serverless
Manage H/W
Complex ops
Manage less H/W
Complex ops
Size & provision VMs
Simpler ops
Size & provision containers
Simpler ops
Just send in requests
Negligible ops
Wasted resources Better utilization Rent, not buy Rent with less waste Pay as you go
Evolution of Computing Models
$$$$$ $$$$ $$$ $$ $
29. Large Data Center Virtual Machines VMs in the Cloud (EC2) Containers Serverless
Manage H/W
Complex ops
Manage less H/W
Complex ops
Size & provision VMs
Simpler ops
Size & provision containers
Simpler ops
Just send in requests
Negligible ops
Wasted resources Better utilization Rent, not buy Rent with less waste Pay as you go
$$$$$ $$$$ $$$ $$ $
Evolution of Computing Models
Evolution to more streamlined, managed
infrastructure– Cheaper to build
– Cheaper to run
– Faster time to market
31. Frontend vs. Backend (Historical)
• Frontend
• What the user interacts with
• Backend (App Server)
• Does the heavy lifting
• More secure than frontend
• Authentication
• Stores data in MongoDB
32. Migration of
functionality
Frontend vs. Backend (Historical)
• Frontend
• What the user interacts with
• Backend (App Server)
• Does the heavy lifting
• More secure than frontend
• Authentication
• Stores data in MongoDB
But this has changed over
last 10 years with Mobile
First + powerful browsers!
33. Migration to Cloud Services &
Microservices
• Backend increasingly delegates
essential but generic tasks
• Processing payments
• Authenticating users
• Posting to social media
• Nothing that makes your app unique
• Developers spend less time on
plumbing – better apps, delivered
quicker
34. Cloud Service & Microservice Integration
• Mundane, repetitive code to
integrate with cloud services &
microservices
• 41% of development time wasted here
• Data access control code
35. Developers spend even less time
on plumbing code.
Developers focus more on high-
value code that differentiates the
experience delivered by their app.
36. Streamlines app development with simple, secure access to data and services from the client with thousands of lines less
code to write and no infrastructure to manage – getting your apps to market faster while reducing operational costs.
MongoDB Stitch Serverless Platform
Services
Stitch Triggers
Real-time notifications that
launch functions in response to
changes in the database
Make further database
changes, push data to other
places, or interact with users
37. Streamlines app development with simple, secure access to data and services from the client with thousands of lines less
code to write and no infrastructure to manage – getting your apps to market faster while reducing operational costs.
Stitch QueryAnywhere Stitch Functions Stitch Mobile Sync (Beta)
MongoDB Stitch Serverless Platform
Services
Stitch Triggers
Real-time notifications that
launch functions in response
to changes in the database
Make further database
changes, push data to other
places, or interact with users
53. Oplog Format
Field Description
ts
The value for this field is a timestamp, which is a data type supported by BSON that is
ever used for replication.
h The hash field gives each entry a unique identity.
v Version of the oplog format.
op The type of operation. This will be either d (delete), u (update) or i (insert).
ns
The value of the namespace field is the name of the database followed by the name of
collection.
o2
For updates, the value for this field will be the _id of the document being updated. This
won’t exist for insertions or deletions.
o
For updates, this field contains the operation that was performed, whilst for inserts and
deletions, it will be the _id of the document that was operated on.
54. Responding to Database Changes in the Past
Before Change Streams: Oplog
}
"ts" : Timestamp(1395663575, 1),
"h" : NumberLong("-5862498903080440015"),
"v" : 2,
"op" : "i",
"ns" : ”music.songs",
"o" : {
"_id" : ObjectId("533024470d7e2c31d4443d22"),
"author" : ”David Bowie",
"title" : ”Changes"
}
}
59. What is the Swagstore
Standard Retail Store:
• Browse Items
• Add items to cart
• Checkout
• Request Notification for Restock
Additional Features:
• Update/text on item re-stock
• Shipping text/e-mail notification
Stitch Features
• Functions
• Triggers
• 3rd Party Services