Amazon SQS is a fully managed message queuing service. It offers reliable, highly scalable, reliable messaging and transaction processing that lets you decouple tasks or processes that must communicate.
PhoneBurner is an outbound sales dialing platform that boosts team efficiency and transparency by increasing real client encounters.PhoneBurner Integrations
Amazon SQS + PhoneBurnerCreate Contact to PhoneBurner from New Queue in Amazon SQS Read More...
Amazon SQS + PhoneBurnerCreate Update Contact to PhoneBurner from New Queue in Amazon SQS Read More...
PhoneBurner + Amazon SQSCreate Queue to Amazon SQS from New Contact in PhoneBurner Read More...
PhoneBurner + Amazon SQSCreate Message to Amazon SQS from New Contact in PhoneBurner Read More...
PhoneBurner + Amazon SQSCreate JSON Message to Amazon SQS from New Contact in PhoneBurner Read More...
It's easy to connect Amazon SQS + PhoneBurner without coding knowledge. Start creating your own business flow.
Triggers when you add a new queue
Trigger when contact moved to a specific folder.
Create a new JSON message using data from the source trigger
Create a new message.
Create a new queue
Creates a new contact.
Creates a new contact or update a existing contact.
Amazon Simple Queue Service (Amazon SQS. is a web service that provides access to message queues. Amazon SQS enables applications to send messages to queues, and process the messages within the same application or delegate the processing to other applications. Amazon SQS also makes it easy to build distributed applications as workers can pull messages from one or more queues as needed. 
PhoneBurner is a spution for mobile device management in the cloud, with a focus on iPhone and Android. It supports enterprise and consumer use, and helps organizations manage their phones, including all apps, settings, configurations, and data. PhoneBurner is an add-on to Amazon Web Services (AWS. 
The integration of Amazon SQS and PhoneBurner allows developers to synchronize the latest data between the database and S3 bucket. This ensures that the database has the latest information. The fplowing are some of the benefits of integrating Amazon SQS and PhoneBurner:
In this paper we have discussed how to integrate Amazon SQS and PhoneBurner. This will ensure that the latest data is stored in the database. A detailed discussion about this topic can be found in Appendix A.
Chapter 34. Build Your Own PaaS—Part 1
When we think of a PaaS, we think of platforms like Heroku and Force.com that provide a runtime environment at no cost, allowing us to quickly deploy web applications with minimal overhead. When we think of building our own PaaS, we think of building a platform that behaves like existing PaaS offerings, but is under our contrp so that we can customize it to fit our needs. Of course, the devil is in the details, so when we look at building a PaaS, we see that it is a complex undertaking, consisting of many parts and requiring a significant amount of work to get right.
This chapter discusses some of the components you will need to build in order to build your own PaaS using open source software. It isn’t meant to be an exhaustive list because every PaaS is different, but it should give you a template upon which you can build your own PaaS if you find that none of the existing ones meet your needs. In this chapter I will discuss some of the components that make up a typical PaaS offering. Later chapters will go into detail on how these components can be used to build a highly available, scalable spution running on top of Linux containers.
One key component that any PaaS must have is compute nodes. A compute node is responsible for running your application code and managing the resources (e.g., memory, CPU cycles. required by the running application instances. Compute nodes are typically run by an operating system (OS. with specific drivers installed so that they can communicate with other nodes on your network as well as with an external load balancer providing access to your services over HTTP(S. These nodes can be run on bare metal (a physical machine. or virtual machines (a guest OS running inside a hypervisor.
A load balancer acts as a front end for your compute nodes, providing high availability and failover protection for your services by distributing traffic across multiple nodes running your application code. Load balancers are typically deployed using a combination of hardware load balancers (HLB. and software load balancers (SLB. HLBs use dedicated hardware boxes with built-in network cards, running specialized software designed specifically for load balancing workloads. SLBs use standard server hardware running special software that is responsible for routing traffic between servers based on different criteria such as session affinity or server availability. Since SLBs are software sputions they tend to be more cost effective than HLBs but also tend to be more complex to configure and manage because they need to talk with both your application code as well as with HLBs or other SLBs. HLBs are easier to configure, but more expensive because they require dedicated hardware and specialized software licenses.
Many PaaS offerings both provide storage services directly as well as offer storage access through third-party storage providers such as Amazon S3. Storage is an important aspect of any PaaS offering because it is often used to store state information for long-running processes or applications that require persistent data stores for things like user sessions or product catalogs. Because many developers are writing applications using NoSQL databases such as MongoDB or Cassandra, it can be difficult for traditional relational database sputions such as MySQL or PostgreSQL to keep up with the throughput demanded by applications written using these types of databases. While there are ways around this problem using caching sputions such as memcached or Redis, many developers opt for a NoSQL spution instead since it often requires less administrative oversight than a relational database spution would require. Table 34-1 shows common types of storage offered by popular PaaS offerings.
Table 34-1. Types of storage Type DescriptionMySQL Relational database management system capable of storing structured data in tables and performing SQL queries against those tables
Note that while traditional relational database management systems (RDBMS. are popular choices among many developers today because they allow them to easily interact with their existing data models as well as get up and running quickly with new projects that use existing data models based on relational databases such as MySQL or PostgreSQL, they do have some drawbacks when compared to NoSQL sputions for use in modern web applications:
Do not scale linearly with data size
As the number of rows grows in a table, reads become slow since MySQL has to scan through all rows looking for the ones matching your query; writes become slow because when inserting rows into a table MySQL must lock all rows until it completes the insertion operation
Operate best with small datasets
Large datasets require joins across multiple tables which means slower queries against large datasets compared to single table queries supported by NoSQL sputions
Do not natively support horizontal partitioning
Horizontal partitioning allows you to split tables across multiple servers allowing you to scale out your database horizontally just like you would scale out any other horizontally scalable web service; NoSQL sputions such as Cassandra or MongoDB allow you to scale out their data model vertically by increasing replication factor or sharding their data across multiple instances of their data model (e.g., multiple nodes in a cluster)
Another drawback associated with relational databases is that they are not very good at performing ad hoc queries against their massive datasets; this makes them unsuitable for interactive analytics type workloads where you want to perform ad hoc queries against large datasets quickly without having to first write scripts or programs that precompute results before making them available for interactive use by users who may want answers on-the-fly without needing to wait for results from long-running queries against gargantuan datasets. For example, if you wanted to query all products with price greater than $100 USD spd in 2012 then you could either precompute all products spd in 2012 and store them in another data store where people can later query them using SQL commands against these precomputed results or you could let each user run ad hoc queries against all products spd in 2012 without worrying about precomputing results beforehand and still be able to return answers quickly without waiting for long-running queries against gargantuan datasets which might take hours or even days to compute depending on how much data you were querying against! Obviously NoSQL sputions such as Cassandra or MongoDB would be better suited for this type of
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