Cosmos db
•Download as PPTX, PDF•
0 likes•28 views
Cosmos DB is a globally distributed, multi-model database as a service. It supports popular database models like document, graph, and key-value and provides high availability, elastic scalability, and tunable consistency. Cosmos DB partitions data across logical replicas for availability and scales throughput globally. It ensures low latency access to data within 10ms and maintains business continuity during regional outages through multiple read regions.
Report
Share
Report
Share
Recommended
Indexing with solr search server and hadoop framework
Hadoop and Solr are used together for indexing large datasets across distributed systems. Hadoop provides a distributed file system and Solr provides search capabilities. Solr indexes data from Hadoop and allows for fast, scalable search across large datasets even when data and computing resources are spread across multiple machines and locations. The combination of Hadoop and Solr provides a fault-tolerant solution for storing, processing, and searching very large datasets in a distributed environment.
RedisConf17 - Home Depot - Turbo charging existing applications with Redis
The Home Depot is transforming its architecture to use microservices and polyglot persistence to handle increasing online order volumes of 250,000 lines per hour. Redis is being used to turbo charge existing monolithic applications by offloading pieces to new processes using patterns like caching, concurrency management, and powering algorithms. This improves performance by reducing database degradation and wait times by over 95%. Next steps include setting up Redis clusters on-premises and off-premises to further reduce database CPU usage and onboard more patterns.
Best Practices for NoSQL Workloads on Amazon EC2 and Amazon EBS - February 20...
Learn how to optimize your NoSQL database on AWS for cost, efficiency, and scale. NoSQL databases are great for modern datasets that require simplicity in design, handle structured and unstructured data, scale horizontally, and offer finer control over availability. With AWS, you have options for running NoSQL on Amazon EC2 with Amazon EBS or on Amazon DynamoDB. This webinar will dive deep into best practices and architectural considerations for designing and managing NoSQL databases like Cassandra, MongoDB, CouchDB, and Aerospike on EC2 and EBS. We will share best practices around instance and volume selection, provide performance tuning hints, and describe cost optimization techniques.
Learning Objectives:
• Learn about common NoSQL database options and use cases for Cassandra, MongoDB, CouchDB, and Aerospike
• Review best practices around architecting on AWS for different NoSQL databases
• Understand the cost vs. performance of different Amazon EC2 instances and Amazon EBS volumes
Apache spark on Hadoop Yarn Resource Manager
How we can configure the spark on apache hadoop environment, and why we need that compared to standalone cluster manager.
Slide also includes docker based demo to play with the hadoop and spark on your laptop itself. See more on the demo codes and other documentation here - https://github.com/haridas/hadoop-env
Bigdata and Hadoop with Docker
This document provides an introduction to big data and Hadoop. It discusses how distributed systems can scale to handle large data volumes and discusses Hadoop's architecture. It also provides instructions on setting up a Hadoop cluster on a laptop and summarizes Hadoop's MapReduce programming model and YARN framework. Finally, it announces an upcoming workshop on Spark and Pyspark.
Kafka website activity architecture
Using Kafka in a website activity use-case. for disaster recovery, multi DC clusters, and multi cloud clusters.
Shift: Real World Migration from MongoDB to Cassandra
Presentation on SHIFT's migration from MongoDB to Cassandra. Topics will include reasons behind choosing to move to Cassandra, zero downtime migration strategy, data modeling patterns, and the benefits of using CQL3.
NoSQL Session II
This document provides an overview of Apache Cassandra concepts including its architecture, data model, data distribution techniques, and configuration options. Key points covered include:
- Cassandra uses a column-oriented data model and provides high availability, scalability, and predictable performance.
- It uses a decentralized architecture with nodes organized into racks and data centers. Data is replicated across multiple nodes for fault tolerance.
- Cassandra Query Language (CQL) is used to define keyspaces, tables, columns, primary keys, and replication strategies.
- Consistency levels can be set for read and write operations to balance availability and consistency. Seed nodes and coordinator nodes have different roles in the cluster.
Recommended
Indexing with solr search server and hadoop framework
Hadoop and Solr are used together for indexing large datasets across distributed systems. Hadoop provides a distributed file system and Solr provides search capabilities. Solr indexes data from Hadoop and allows for fast, scalable search across large datasets even when data and computing resources are spread across multiple machines and locations. The combination of Hadoop and Solr provides a fault-tolerant solution for storing, processing, and searching very large datasets in a distributed environment.
RedisConf17 - Home Depot - Turbo charging existing applications with Redis
The Home Depot is transforming its architecture to use microservices and polyglot persistence to handle increasing online order volumes of 250,000 lines per hour. Redis is being used to turbo charge existing monolithic applications by offloading pieces to new processes using patterns like caching, concurrency management, and powering algorithms. This improves performance by reducing database degradation and wait times by over 95%. Next steps include setting up Redis clusters on-premises and off-premises to further reduce database CPU usage and onboard more patterns.
Best Practices for NoSQL Workloads on Amazon EC2 and Amazon EBS - February 20...
Learn how to optimize your NoSQL database on AWS for cost, efficiency, and scale. NoSQL databases are great for modern datasets that require simplicity in design, handle structured and unstructured data, scale horizontally, and offer finer control over availability. With AWS, you have options for running NoSQL on Amazon EC2 with Amazon EBS or on Amazon DynamoDB. This webinar will dive deep into best practices and architectural considerations for designing and managing NoSQL databases like Cassandra, MongoDB, CouchDB, and Aerospike on EC2 and EBS. We will share best practices around instance and volume selection, provide performance tuning hints, and describe cost optimization techniques.
Learning Objectives:
• Learn about common NoSQL database options and use cases for Cassandra, MongoDB, CouchDB, and Aerospike
• Review best practices around architecting on AWS for different NoSQL databases
• Understand the cost vs. performance of different Amazon EC2 instances and Amazon EBS volumes
Apache spark on Hadoop Yarn Resource Manager
How we can configure the spark on apache hadoop environment, and why we need that compared to standalone cluster manager.
Slide also includes docker based demo to play with the hadoop and spark on your laptop itself. See more on the demo codes and other documentation here - https://github.com/haridas/hadoop-env
Bigdata and Hadoop with Docker
This document provides an introduction to big data and Hadoop. It discusses how distributed systems can scale to handle large data volumes and discusses Hadoop's architecture. It also provides instructions on setting up a Hadoop cluster on a laptop and summarizes Hadoop's MapReduce programming model and YARN framework. Finally, it announces an upcoming workshop on Spark and Pyspark.
Kafka website activity architecture
Using Kafka in a website activity use-case. for disaster recovery, multi DC clusters, and multi cloud clusters.
Shift: Real World Migration from MongoDB to Cassandra
Presentation on SHIFT's migration from MongoDB to Cassandra. Topics will include reasons behind choosing to move to Cassandra, zero downtime migration strategy, data modeling patterns, and the benefits of using CQL3.
NoSQL Session II
This document provides an overview of Apache Cassandra concepts including its architecture, data model, data distribution techniques, and configuration options. Key points covered include:
- Cassandra uses a column-oriented data model and provides high availability, scalability, and predictable performance.
- It uses a decentralized architecture with nodes organized into racks and data centers. Data is replicated across multiple nodes for fault tolerance.
- Cassandra Query Language (CQL) is used to define keyspaces, tables, columns, primary keys, and replication strategies.
- Consistency levels can be set for read and write operations to balance availability and consistency. Seed nodes and coordinator nodes have different roles in the cluster.
Running MySQL in AWS
This document discusses options for running MySQL in AWS. It describes using Amazon RDS, where AWS manages the infrastructure and MySQL version, but has limitations like lack of root access. It also describes using EC2, where one provisions and manages their own instances, storage, and MySQL binaries, allowing more flexibility but also more management overhead. Key tradeoffs discussed are ease of use vs customization options and control in RDS vs EC2.
Ops Jumpstart: MongoDB Administration 101
New to MongoDB? We'll provide an overview of installation, high availability through replication, scale out through sharding, and options for monitoring and backup. No prior knowledge of MongoDB is assumed. This session will jumpstart your knowledge of MongoDB operations, providing you with context for the rest of the day's content.
The Meta of Hadoop - COMAD 2012
What do you talk about to a hall full of database gurus? Instead of science - my talk focused on the art. What made Hadoop successful? What can we learn from it? What principles work well in building software for large scale services? What are some interesting unsolved problems in a world overrun by open-source (and VC investments :-))
Scylla Summit 2022: New AWS Instances Perfect for ScyllaDB
In this talk AWS’ Ken Krupa, Head of Specialized Solutions Architecture, will describe the architecture and capabilities of two new AWS EC2 instance types perfect for data-intensive storage and IO-heavy workloads like ScyllaDB: the Intel-based I4i and the Graviton2-based I4g series.
The Intel Xeon Ice Lake-based I4i series provides unparalleled raw horsepower for your most demanding workloads. Meanwhile, the Graviton2-powered I4g instances provide lower cost per storage on a power-efficient platform to deploy your cloud-native applications.
Ken will also describe the AWS Nitro SSD, a new form of high-speed NVMe storage with a Flash Translation Layer built with Nitro controllers, which powers both of these instance families.
ScyllaDB VP of Product Tzach Livyatan will then share benchmarking results showing how ScyllaDB behaves under load on these two instance types, providing maximum system utility and efficiency.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
Introduce to spark
Spark is a big data processing tool built with Scala that runs on the Java Virtual Machine (JVM). It is up to 100 times faster than Hadoop for iterative jobs because it keeps intermediate data in memory rather than writing to disk. Spark uses Resilient Distributed Datasets (RDDs) that allow data to be kept in memory across transformations and actions. RDDs also maintain lineage graphs to allow recovery from failures.
Cassandra
Cassandra is a highly scalable, open-source distributed database designed to handle large amounts of structured data across many servers. It provides high availability with no single point of failure and was created by Facebook to power search on their messaging platform. Cassandra uses a decentralized peer-to-peer architecture and replicates data across multiple data centers for fault tolerance. It emphasizes performance and scalability over more complex query options and does not support features like joins typically found in relational databases. Companies like Netflix and Hulu use Cassandra for its availability, scalability, and ability to span large clusters with minimal maintenance.
HBaseConAsia2018 Track3-2: HBase at China Telecom
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.
Database Shrading and cassandra architecture
Database sharding involves horizontally partitioning a database table across multiple servers to improve scalability and performance. Cassandra is a distributed database that supports automatic sharding of data and replication across nodes using consistent hashing. Consistent hashing maps keys and nodes to an abstract hash ring and assigns keys to nodes clockwise on the ring to minimize data movement when nodes are added or removed. This allows Cassandra databases to seamlessly scale without performance impacts.
Cassandra Community Webinar: CMB - An Open Message Bus for the Cloud
At Comcast Silicon Valley we have developed a general purpose message bus for the cloud. The service is API compatible with Amazon’s SQS/SNS and is built on Cassandra and Redis with the goal of linear horizontal scalability. In this Webinar we will explore the architecture of the system and how we employ Cassandra as a central component to meet key requirements. We will also take a look at the latest performance numbers.
Cosmos DB Service
- Cosmos DB is Microsoft's globally distributed database service that can scale worldwide to manage large amounts of data. It supports multiple APIs and data models including document, graph, table, and MongoDB APIs.
- Key advantages include global distribution of data across regions, automatic scaling of storage and throughput, low latency and high availability, flexible schemas, and low cost of ownership.
- Cosmos DB supports document, table, graph and MongoDB APIs. MongoDB and DocumentDB are both document oriented and store data as JSON, while table API uses a key-value model and graph API models relationships.
- The document compares Cosmos DB to other database options like MySQL and MongoDB, covering their data models, usage, and how to set
Cassandra vs Databases
A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques.
Managing MySQL Scale Through Consolidation
Managing MySQL at scale requires the ability to confidently plan for the future while remaining flexible and responsive to the dynamic needs of the present. Quickly responding to requirements to increase performance, deploy additional read-slaves, refresh Dev/Test, QA, and business copies of databases and improve backup and restore times are critical capabilities in the fast paced world of DevOps. In this session, you will learn how to avoid over-provisioning storage to improve performance, reduce replication slave creation times from hours to seconds, significantly shrink backup windows, and slash restore times, all while maintaining the ability to scale storage resources without downtime or performance impact.
Ceph Day Santa Clara: Ceph at DreamHost
Dallas Kashuba, Co-Founder of DreamHost walks through why they went with Ceph at the Santa Clara Ceph Day.
Databases on aws part 2
Amazon Redshift is a fully managed cloud data warehouse service that allows users to launch a data warehouse cluster with a few clicks and handles all administrative tasks. Redshift Spectrum enables querying of data stored in Amazon S3 without loading it into Redshift. Amazon ElastiCache is a service that makes it easy to set up, operate, and scale a cache environment using Memcached or Redis with high performance and cost effectiveness.
Oracle and SQL Server on the Cloud - Bill Baldwin
The document discusses options for running Oracle and SQL Server databases on AWS. It describes the differences between using Amazon RDS, which provides a managed service, versus running the databases on EC2 instances, which provides more control. Key features of RDS like licensing models, versions supported, high availability and security options are summarized. Storage and networking best practices for optimizing database performance when using EC2 instances are also covered.
Cassandra Distributions and Variants
10 different Cassandra distributions and variants ranging from Cassandra / Cassandra Compliant Databases on JVM, Cassandra Compliant Databases on C++, Cassandra as a Service / Managed Cassandra Based on Open Source Cassandra, and Cassandra as a Service / Managed Cassandra Based on Proprietary Technology.
Cloud Optimized Big Data
What makes a big-data platform 'cloud-optimized'. Here's our (Qubole's) shot at it. @Cloud-Asia 2014.
Scylla Summit 2019 Keynote - Avi Kivity
ScyllaDB CTO Avi Kivity looks at the present state of Scylla's capabilities, and offers a glimpse of what's to come. From incremental compaction strategy to take advantage of newer, denser nodes, to data transformations with User Defined Functions (UDFs) and User Defined Aggregates (UDAs), ScyllaDB continues to expand its horizons for capabilities, use cases and APIs.
In-Memory Data Grids - Ampool (1)
The document discusses in-memory data grids and Ampool. It describes that in-memory data grids like Ampool are sophisticated in-memory data stores that provide low latency reads and writes through data partitioning and replication across a scalable cluster. The document then provides details on Ampool's architecture based on Apache Geode, how it compares favorably to other in-memory solutions in providing both low-latency and analytics capabilities, and demonstrates its performance through examples.
DAT304_Amazon Aurora Performance Optimization with MySQL
Amazon Aurora services are MySQL and PostgreSQL -compatible relational database engines with the speed, reliability, and availability of high-end commercial databases at one-tenth the cost. This session introduces you to Amazon Aurora, explores the capabilities and features of Aurora, explains common use cases, and helps you get started with Aurora.
Introduction to Amazon Relational Database Service
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories.
Introduction to Amazon Relational Database Service
This document provides an overview of Amazon Relational Database Service (Amazon RDS). It discusses the multi-engine support, automated provisioning and scaling, high availability features, security capabilities, monitoring options, and compliance certifications of Amazon RDS. It also highlights key customers like Airbnb that use Amazon RDS to simplify database management and improve performance and availability.
More Related Content
What's hot
Running MySQL in AWS
This document discusses options for running MySQL in AWS. It describes using Amazon RDS, where AWS manages the infrastructure and MySQL version, but has limitations like lack of root access. It also describes using EC2, where one provisions and manages their own instances, storage, and MySQL binaries, allowing more flexibility but also more management overhead. Key tradeoffs discussed are ease of use vs customization options and control in RDS vs EC2.
Ops Jumpstart: MongoDB Administration 101
New to MongoDB? We'll provide an overview of installation, high availability through replication, scale out through sharding, and options for monitoring and backup. No prior knowledge of MongoDB is assumed. This session will jumpstart your knowledge of MongoDB operations, providing you with context for the rest of the day's content.
The Meta of Hadoop - COMAD 2012
What do you talk about to a hall full of database gurus? Instead of science - my talk focused on the art. What made Hadoop successful? What can we learn from it? What principles work well in building software for large scale services? What are some interesting unsolved problems in a world overrun by open-source (and VC investments :-))
Scylla Summit 2022: New AWS Instances Perfect for ScyllaDB
In this talk AWS’ Ken Krupa, Head of Specialized Solutions Architecture, will describe the architecture and capabilities of two new AWS EC2 instance types perfect for data-intensive storage and IO-heavy workloads like ScyllaDB: the Intel-based I4i and the Graviton2-based I4g series.
The Intel Xeon Ice Lake-based I4i series provides unparalleled raw horsepower for your most demanding workloads. Meanwhile, the Graviton2-powered I4g instances provide lower cost per storage on a power-efficient platform to deploy your cloud-native applications.
Ken will also describe the AWS Nitro SSD, a new form of high-speed NVMe storage with a Flash Translation Layer built with Nitro controllers, which powers both of these instance families.
ScyllaDB VP of Product Tzach Livyatan will then share benchmarking results showing how ScyllaDB behaves under load on these two instance types, providing maximum system utility and efficiency.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
Introduce to spark
Spark is a big data processing tool built with Scala that runs on the Java Virtual Machine (JVM). It is up to 100 times faster than Hadoop for iterative jobs because it keeps intermediate data in memory rather than writing to disk. Spark uses Resilient Distributed Datasets (RDDs) that allow data to be kept in memory across transformations and actions. RDDs also maintain lineage graphs to allow recovery from failures.
Cassandra
Cassandra is a highly scalable, open-source distributed database designed to handle large amounts of structured data across many servers. It provides high availability with no single point of failure and was created by Facebook to power search on their messaging platform. Cassandra uses a decentralized peer-to-peer architecture and replicates data across multiple data centers for fault tolerance. It emphasizes performance and scalability over more complex query options and does not support features like joins typically found in relational databases. Companies like Netflix and Hulu use Cassandra for its availability, scalability, and ability to span large clusters with minimal maintenance.
HBaseConAsia2018 Track3-2: HBase at China Telecom
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.
Database Shrading and cassandra architecture
Database sharding involves horizontally partitioning a database table across multiple servers to improve scalability and performance. Cassandra is a distributed database that supports automatic sharding of data and replication across nodes using consistent hashing. Consistent hashing maps keys and nodes to an abstract hash ring and assigns keys to nodes clockwise on the ring to minimize data movement when nodes are added or removed. This allows Cassandra databases to seamlessly scale without performance impacts.
Cassandra Community Webinar: CMB - An Open Message Bus for the Cloud
At Comcast Silicon Valley we have developed a general purpose message bus for the cloud. The service is API compatible with Amazon’s SQS/SNS and is built on Cassandra and Redis with the goal of linear horizontal scalability. In this Webinar we will explore the architecture of the system and how we employ Cassandra as a central component to meet key requirements. We will also take a look at the latest performance numbers.
Cosmos DB Service
- Cosmos DB is Microsoft's globally distributed database service that can scale worldwide to manage large amounts of data. It supports multiple APIs and data models including document, graph, table, and MongoDB APIs.
- Key advantages include global distribution of data across regions, automatic scaling of storage and throughput, low latency and high availability, flexible schemas, and low cost of ownership.
- Cosmos DB supports document, table, graph and MongoDB APIs. MongoDB and DocumentDB are both document oriented and store data as JSON, while table API uses a key-value model and graph API models relationships.
- The document compares Cosmos DB to other database options like MySQL and MongoDB, covering their data models, usage, and how to set
Cassandra vs Databases
A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques.
Managing MySQL Scale Through Consolidation
Managing MySQL at scale requires the ability to confidently plan for the future while remaining flexible and responsive to the dynamic needs of the present. Quickly responding to requirements to increase performance, deploy additional read-slaves, refresh Dev/Test, QA, and business copies of databases and improve backup and restore times are critical capabilities in the fast paced world of DevOps. In this session, you will learn how to avoid over-provisioning storage to improve performance, reduce replication slave creation times from hours to seconds, significantly shrink backup windows, and slash restore times, all while maintaining the ability to scale storage resources without downtime or performance impact.
Ceph Day Santa Clara: Ceph at DreamHost
Dallas Kashuba, Co-Founder of DreamHost walks through why they went with Ceph at the Santa Clara Ceph Day.
Databases on aws part 2
Amazon Redshift is a fully managed cloud data warehouse service that allows users to launch a data warehouse cluster with a few clicks and handles all administrative tasks. Redshift Spectrum enables querying of data stored in Amazon S3 without loading it into Redshift. Amazon ElastiCache is a service that makes it easy to set up, operate, and scale a cache environment using Memcached or Redis with high performance and cost effectiveness.
Oracle and SQL Server on the Cloud - Bill Baldwin
The document discusses options for running Oracle and SQL Server databases on AWS. It describes the differences between using Amazon RDS, which provides a managed service, versus running the databases on EC2 instances, which provides more control. Key features of RDS like licensing models, versions supported, high availability and security options are summarized. Storage and networking best practices for optimizing database performance when using EC2 instances are also covered.
Cassandra Distributions and Variants
10 different Cassandra distributions and variants ranging from Cassandra / Cassandra Compliant Databases on JVM, Cassandra Compliant Databases on C++, Cassandra as a Service / Managed Cassandra Based on Open Source Cassandra, and Cassandra as a Service / Managed Cassandra Based on Proprietary Technology.
Cloud Optimized Big Data
What makes a big-data platform 'cloud-optimized'. Here's our (Qubole's) shot at it. @Cloud-Asia 2014.
Scylla Summit 2019 Keynote - Avi Kivity
ScyllaDB CTO Avi Kivity looks at the present state of Scylla's capabilities, and offers a glimpse of what's to come. From incremental compaction strategy to take advantage of newer, denser nodes, to data transformations with User Defined Functions (UDFs) and User Defined Aggregates (UDAs), ScyllaDB continues to expand its horizons for capabilities, use cases and APIs.
In-Memory Data Grids - Ampool (1)
The document discusses in-memory data grids and Ampool. It describes that in-memory data grids like Ampool are sophisticated in-memory data stores that provide low latency reads and writes through data partitioning and replication across a scalable cluster. The document then provides details on Ampool's architecture based on Apache Geode, how it compares favorably to other in-memory solutions in providing both low-latency and analytics capabilities, and demonstrates its performance through examples.
DAT304_Amazon Aurora Performance Optimization with MySQL
Amazon Aurora services are MySQL and PostgreSQL -compatible relational database engines with the speed, reliability, and availability of high-end commercial databases at one-tenth the cost. This session introduces you to Amazon Aurora, explores the capabilities and features of Aurora, explains common use cases, and helps you get started with Aurora.
What's hot (20)
Scylla Summit 2022: New AWS Instances Perfect for ScyllaDB
Scylla Summit 2022: New AWS Instances Perfect for ScyllaDB
Cassandra Community Webinar: CMB - An Open Message Bus for the Cloud
Cassandra Community Webinar: CMB - An Open Message Bus for the Cloud
DAT304_Amazon Aurora Performance Optimization with MySQL
DAT304_Amazon Aurora Performance Optimization with MySQL
Similar to Cosmos db
Introduction to Amazon Relational Database Service
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories.
Introduction to Amazon Relational Database Service
This document provides an overview of Amazon Relational Database Service (Amazon RDS). It discusses the multi-engine support, automated provisioning and scaling, high availability features, security capabilities, monitoring options, and compliance certifications of Amazon RDS. It also highlights key customers like Airbnb that use Amazon RDS to simplify database management and improve performance and availability.
Aws for Startups Building Cloud Enabled Apps
The document discusses how to build cloud-enabled apps that can scale on AWS. It covers scaling vertically by increasing instance sizes, scaling horizontally by adding more instances, using auto-scaling to dynamically scale based on demand, distributing load with an ELB, scaling databases using read replicas and sharding, and taking advantage of managed database services like RDS and DynamoDB for easier administration. It also discusses decomposing applications into small, stateless components and using infrastructure as code for continuous deployment and agility.
Cassandra for mission critical data
1) Apache Cassandra in term of CAP Theorem
2) What makes Apache Cassandra "Available"?
3) How Apache Cassandra ensures data consistency?
4) Cassandra advantages and disadvantages
5) Frameworks/libraries to access Apache Cassandra + performance comparison
Bringing Structure, Scalability, and Services to Cloud-Scale Storage
Deploying storage with a forklift is so 1990s, right? Today’s applications and infrastructure demand systems and services that scale. Customers require performance and capacity to fit the use case and workloads, not the other way around. Architects need multi-temperature, multi-location, highly available, and compliance friendly platforms that grow with the generational shift in data growth and utility.
ENT305 Migrating Your Databases to AWS: Deep Dive on Amazon Relational Databa...
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity, automates time-consuming database administration tasks, and provides you with six familiar database engines to choose from: Amazon Aurora, Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB. In this session, we will take a close look at the capabilities of Amazon RDS and explain how it works. We’ll also discuss the AWS Database Migration Service and AWS Schema Conversion Tool, which help you migrate databases and data warehouses with minimal downtime from on-premises and cloud environments to Amazon RDS and other Amazon services. Gain your freedom from expensive, proprietary databases while providing your applications with the fast performance, scalability, high availability, and compatibility they need.
AWS Innovation at Scale
Technical 101: AWS Innovation at Scale
This session, gives an insider view of some the innovations that help make the AWS Cloud unique. He will show examples of AWS networking innovations from the interregional network backbone, through custom routers and networking rotocol stack, all the way down to individual servers. He will show examples from AWS server hardware, storage, and power distribution and then, up the stack, in high scale streaming data processing. Rodney will also dive into fundamental database work AWS is delivering to open up scaling and performance limits, reduce costs, and eliminate much of the administrative burden of managing databases. Join this session and walk away with a deeper understanding of the underlying innovations powering the cloud.
Speaker: Rodeny Haywood, Manager Solutions Architecture, Amazon Web Services
PayPal Big Data and MySQL Cluster
PayPal's presentation from MySQL Connect conference, including their analysis of big data solutions and selection of MySQL Cluter
AWS Innovation at Scale – Rodney Haywood
This session, gives an insider view of some the innovations that help make the AWS Cloud unique. He will show examples of AWS networking innovations from the interregional network backbone, through custom routers and networking rotocol stack, all the way down to individual servers. He will show examples from AWS server hardware, storage, and power distribution and then, up the stack, in high scale streaming data processing.
M6d cassandrapresentation
Cassandra is used for real-time bidding in online advertising. It processes billions of bid requests per day with low latency requirements. Segment data, which assigns product or service affinity to user groups, is stored in Cassandra to reduce calculations and allow users to be bid on sooner. Tuning the cache size and understanding the active dataset helps optimize performance.
Yes sql08 inmemorydb
This document summarizes a presentation about using MySQL and the NDB storage engine to build a globally distributed in-memory database system on AWS. It proposes using MySQL/NDB clusters tiled across AWS availability zones to provide high availability and performance at a large scale. Key challenges discussed include managing data consistency across wide geographical distances and dealing with limitations of AWS like network performance and lack of global load balancing. Lessons learned are that NDB can successfully compete with NoSQL for most use cases by providing ACID compliance without sacrificing availability or performance.
(DAT202) Managed Database Options on AWS
In addition to running databases in Amazon EC2, AWS customers can choose among a variety of managed database services. These services save effort, save time, and unlock new capabilities and economies. In this session, we make it easy to understand how they differ, what they have in common, and how to choose one or more. We explain the fundamentals of Amazon DynamoDB, a fully managed NoSQL database service; Amazon RDS, a relational database service in the cloud; Amazon ElastiCache, a fast, in-memory caching service in the cloud; and Amazon Redshift, a fully managed, petabyte-scale data-warehouse solution that can be surprisingly economical. We will cover how each service might help support your application, how much each service costs, and how to get started. We will also have with us Jeongsang Baek, the VP of Engineering from IGAWorks, Korea’s No.1 mobile business platform, who will walk us through their architecture and share with us the key insights that they gained from using the various AWS database technologies to deliver a reliable, efficient and cost-effective experience.
Introduction to NoSQL
The document summarizes a meetup about NoSQL databases hosted by AWS in Sydney in 2012. It includes an agenda with presentations on Introduction to NoSQL and using EMR and DynamoDB. NoSQL is introduced as a class of databases that don't use SQL as the primary query language and are focused on scalability, availability and handling large volumes of data in real-time. Common NoSQL databases mentioned include DynamoDB, BigTable and document databases.
Building Your Data Warehouse with Amazon Redshift
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Create cloud service on AWS
For people who start to create a cloud service, it’s really important to know how to create a scalable cloud service to fit the growth of the future workloads. In this session, we will introduce how to design a scalable cloud service including AWS services introduction and best practices.
BigData, NoSQL & ElasticSearch
These slides explain briefly about the big data concept, NoSQL database systems and the ElasticSearch technology which are quite modern concepts
Amazon Relational Database Service (Amazon RDS)
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business.
Highlights of AWS ReInvent 2023 (Announcements and Best Practices)
Highlights of AWS ReInvent 2023 in Las Vegas. Contains new announcements, deep dive into existing services and best practices, recommended design patterns.
A Survey of Advanced Non-relational Database Systems: Approaches and Applicat...
This document summarizes a survey of advanced non-relational database systems, their approaches, applications, and comparison to relational database management systems (RDBMS). It outlines the problem of scaling to meet new web-scale demands, describes how non-relational databases provide a solution by sacrificing consistency for availability and partition tolerance. Examples of non-relational databases are provided, including their data models, APIs, optimizations, and benefits compared to RDBMS such as improved scalability and fault tolerance.
Aws webcast - Scaling on AWS 13 08-20
Learn about the patterns and techniques a business should be using in building their infrastructure on Amazon Web Services to be able to handle rapid growth and success in the early days. From leveraging highly scalable AWS services, to architecting best patterns, there are a number of smart choices you can make early on to help you overcome some typical infrastructure issues.
Presenter: Chris Munns,Solutions Architect, Amazon Web Services
Similar to Cosmos db (20)
Introduction to Amazon Relational Database Service
Introduction to Amazon Relational Database Service
Introduction to Amazon Relational Database Service
Introduction to Amazon Relational Database Service
Bringing Structure, Scalability, and Services to Cloud-Scale Storage
Bringing Structure, Scalability, and Services to Cloud-Scale Storage
ENT305 Migrating Your Databases to AWS: Deep Dive on Amazon Relational Databa...
ENT305 Migrating Your Databases to AWS: Deep Dive on Amazon Relational Databa...
Highlights of AWS ReInvent 2023 (Announcements and Best Practices)
Highlights of AWS ReInvent 2023 (Announcements and Best Practices)
A Survey of Advanced Non-relational Database Systems: Approaches and Applicat...
A Survey of Advanced Non-relational Database Systems: Approaches and Applicat...
Recently uploaded
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
Recently uploaded (20)
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
Cosmos db
- 1. Cosmos DB Overview by Akshat Thakar
- 2. Databases - Background 1. SQL – Normalized Schema, Referential Integrity, Constraints, Indexed data 2. OLTP vs OLAP 3. Teradata, EDW - OLAP 4. NoSQL – Not only SQL use cases i. Document Database – MongoDB Website Visitor profile Case Document of Patient Wheel traceability ii. Column Family Database – Cassandra Gmail with mail Id and mails iii. Graph Database – Gremlin most influential person(s) in a network
- 3. Cloud Concepts High Availability in Cloud Geography Region Region AZ AZ AZ AZ • Failures can occur that affect the availability of resources that are in the same Region and AZ. • AZ may host multiple Data Centers • 54 regions – highest for Cloud provider, public and private regions
- 4. Cosmos DB - Background • IaaS – Compute, Storage and Network • PaaS – Brand of Products – OS, Databases, Middleware as managed service Management and Sizing is planned by Provider • SaaS – Readily available Product – SalesForce, Gmail, PeopleSoft, SAP SuccessFactors • DBaaS [database-as-a-service] 1. SQL Database – Structured Database as service 2. Azure Document DB – NoSQL Database service with support for Document model 3. Cosmos DB – evolution of Document DB with multi model support Supported Models – MongoDB, Gremlin, Cassandra, Table /SQL API ServiceModels
- 5. Globally Distributed • Scale read and write throughput globally. • Maintain business continuity during regional outages. • Globally Distributed – multiple read regions on demand • Low Latency – 10 ms at 99th percentile • Elastic Scalability - thousands to hundreds of millions of requests/sec • High Availability - 99.999% for multi region reads • Tunable consistency – Strong, Bounded Stateless, Session, Consistent Prefix, Eventual. Each model has a tradeoff between consistency and performance.
- 6. High Availability Partitions Logical to Physical Mapping Group of nodes = replication set Container = collection or table London NYC Before MysqLDB1 MysqLDB2 2TB 2TB Total Data = 3 TB
- 7. Pricing Cosmos DB was expensive, now affordable to get started. Before Now Minimum 10000 RU/S with scaling increments of 1,000 RU/S 400 RU/S with scaling increments of 100 RU/S. 1 region - $584 per month Multi Region - $1,168 per month 1 region - $23.36 per month Multi Region - $46.72 per month RU/S – Request Unit per second
- 8. Best Practices • Cloud keep a close eye on your usage. • Scale on Demand – Is complete size estimate needed before deployment? • Test production scale data in Test environment • Technology Stack evolution with multiple model support • Supports Manual Failover of region. Simulate failure and plan. • Leverage automatic secondary indexing • If using MongoDB API, get started using emulator.
Editor's Notes
- AZ has one or more DC
- Why multi model – maintenance of different databases
- Why partitions