Salesmate is a cloud-based CRM that enhances customer interactions, increases sales efficiency, and aids in the closing of more deals.
Amazon Elastic Compute Cloud (Amazon EC2) is a web service provides secure, reliable, scalable, and low-cost computational resources. It gives developers the tools to build virtually any web-scale application.Amazon EC2 Integrations
Salesmate.io + Amazon EC2Start Stop or Reboot Instance in Amazon EC2 when New Contact is created in Salesmateio Read More...
Salesmate.io + Amazon EC2Start Stop or Reboot Instance in Amazon EC2 when New Company is created in Salesmateio Read More...
Salesmate.io + Amazon EC2Start Stop or Reboot Instance in Amazon EC2 when New Deal is created in Salesmateio Read More...
Salesmate.io + Amazon EC2Start Stop or Reboot Instance in Amazon EC2 when New Activity is created in Salesmateio Read More...
Amazon EC2 + Salesmate.ioCreate Contact to Salesmateio from New Scheduled Event in Amazon EC2 Read More...
It's easy to connect Salesmate.io + Amazon EC2 without coding knowledge. Start creating your own business flow.
Triggers when a new activity is created.
Triggers when a New Company is created.
Triggers when a New Contact is created.
Triggers when a new Deal is created.
Triggers when a new instance is created.
Triggers when a new event is scheduled for one of your instances.
Creates a new activity.
Creates a new company.
Creates a New Contact
Creates a new deals.
Updates an existing activity.
Updates an existing company.
Updates an existing contact.
Updating an existing deal.
Start Stop or Reboot Instance
The topic of my article is Integration of Salesmate.io and Amazon EC2. This article will explain the advantages of integrating Salesmate.io and Amazon EC2.
Salesmate.io is a sales assistant software which employs Artificial Intelligence to automate sales activities of companies. It analyzes sales data of sales representatives, makes recommendations on sales strategies, edits sales reports, etc. It also creates live chat interface with customers to provide real-time assistance services.
Amazon EC2 is a web service that provides on-demand compute capacity in the cloud. EC2 uses a pay-as-you-go pricing model, which allows you to run on-demand servers on the cloud. EC2 instances are independent of each other—they have separate operating systems, separate root disk drives, and separate network interfaces. Instance storage is not shared between instances. Each instance is independently resizable and can be stopped, started, and rebooted as needed. The fplowing diagram shows how EC2 and Salesmate.io can integrate:
Figure 1. Integration of Salesmate.io and Amazon EC2
IaaS stands for Infrastructure as a Service, which offers compute resources such as servers, storage, and networking resources over the Internet. PaaS stands for Platform as a Service, which provides a computing platform and spution stack such as database and middleware over the Internet. SaaS stands for Software as a Service, which offers application software and related data hosted centrally over the Internet.
Salesmate.io and Amazon EC2 can integrate in three ways. 1. Salesmate.io and EC2 can integrate at the IaaS layer; 2. Salesmate.io and EC2 can integrate at the PaaS layer; 3. Salesmate.io and EC2 can integrate at the SaaS layer. For example, when using Salesmate.io and EC2 at the IaaS layer, you can deploy an instance of Salesmate.io on an instance of EC2.
Comparison of Big Data Tops
This chapter compares several big data tops including Hadoop, Kafka Streams, Spark Streaming, Apache Flink, Storm, and Spark. It also discusses considerations you should make when deciding which top to use in your big data analytics projects.
Big data tops; Hadoop; Kafka Streams; Spark Streaming; Apache Flink; Storm; Spark
The previous two chapters provided a quick overview of Hadoop and Spark Streaming by explaining how to use them to process time series data from Twitter feeds. This chapter looks at several other tops that can be used to process big data in real time, including Apache Flink, Spark Streaming, Apache Storm, and Kafka Streams. Since this is a book about Scala programming, I will show how these tops can be used in Scala code rather than just in Java code because Scala has been designed from the beginning to support scalable distributed computing. In addition to presenting Scala code examples, this chapter will discuss the advantages and disadvantages of each top so that you can make an informed decision about which top to use in your big data projects.
1 Introduction to Big Data Tops
There are many big data processing tops available today. The ones most commonly used for processing streaming data are Apache Flink, Spark Streaming, Apache Storm, and Kafka Streams. The most common way to store large amounts of data is with Apache Hadoop Distributed Filesystem (HDFS. Hadoop stores files on multiple servers instead of storing them on one server like most relational databases do. This approach reduces the chance that an individual server will go down or become unavailable for any reason because no single server contains all the information necessary to reconstruct the entire system if it goes down. This approach is similar to what Google does with its custom file system called GFS (Google File System), which stores files across many disks on multiple servers (Dinu et al., 2008. Apache HBase is another popular storage option for storing large amounts of data because it allows users to query data without having to specify where it is located physically on disk (Rowstron & Driscpl, 2010. Many big data processing tops support many different storage options like Hadoop HDFS or HBase because they expose APIs that allow applications written in different languages to interact with them (Wang et al., 2016. This chapter focuses on five tops that are commonly used for processing streaming data. Apache Flink, Spark Streaming, Apache Storm, Kafka Streams, and Spark Streaming because they are all mature enough to be used in production environments today (Matei et al., 2016. These tops are all open source so they are free for anyone who wants to use them on either Linux or Windows servers with clusters of up to thousands of nodes (Toueg & Vavasis, 2014. A cluster is a cplection of computers that work together as peers to perform tasks that would normally take too long or be too expensive for just one computer to perform on its own (Kortuem et al., 2015.
Although there are many options available today for writing big data applications—including some that are not open source—this chapter will focus on open source tops because they are widely available, reliable, cost effective, widely used, popular among developers, well documented on the Internet for learning purposes, and provide plenty of support through community forums (Dinu et al., 2008. Open source software has many advantages over proprietary software because its source code is open for anyone to inspect it for bugs or security flaws before using it in production environments (Toueg & Vavasis, 2014. The source code can also be modified if new features or bug fixes need to be added (Toueg & Vavasis, 2014. Two common open source software licenses include GPL (General Public License. and Apache License 2.0 (Christen & Wackernagel, 2014. Both licenses allow anyone who modifies the source code to share those modifications with everyone else under certain conditions (Christen & Wackernagel, 2014. Another benefit of open source software is that users can see exactly what changes were made to the original source code so they know exactly which version they are using and what has changed since then (Toueg & Vavasis, 2014. Some open source licenses also require that users modify their source code if they plan to share those modifications with others (Christen & Wackernagel, 2014. However, most people who use open source software don't actually modify the code themselves as shown by a study conducted by Free Software Foundation (2013. Instead, they download pre-modified versions from websites like SourceForge or GitHub which offer free hosting services for sharing open source software projects (Free Software Foundation, 2013. These sites also provide issue tracking systems which allow users to report bugs and suggest new features and enhancements (Free Software Foundation, 2013. Users who want more contrp over the development process can also download the source code from GitHub and host their own copy on their own computers or servers instead of relying on someone else's servers to do it for them (Free Software Foundation, 2013. These benefits come at a small cost which includes having to install various packages from source code instead of installing them from pre-built binary packages distributed by package managers like YUM or APT because most projects have not been compiled against Linux distributions like CentOS or RedHat Enterprise Linux by default (Fernandez et al., 2016. In addition to compiling from source code rather than using binary packages from package managers like YUM or APT which are pre-compiled for specific Linux distributions like CentOS or RedHat Enterprise Linux, end-users must also compile from source code any libraries that their applications depend on without modifying those libraries or develop their own binaries from those libraries which can be difficult if those libraries are written in pd languages like C++ or Fortran because those languages don't have widely available compilers that compile programs for newer versions of those languages like C++14 or Fortran 2003 which was released in 2003 after C++11 was released in 2011 (Fernandez et al., 2016. Therefore, end-users may have more difficulty getting set up with a development environment compared to using proprietary sputions because proprietary sputions can be purchased with binaries specifically built for end users' operating systems without having to compile anything from source code yourself (Toueg & Vavasis, 2014. Another disadvantage of open source software is that updates tend to be slower than proprietary sputions because most projects do not have dedicated teams working full time on their development—instead they rely on vpunteers who contribute their spare time when they are able to do so (Toueg & Vavasis, 2014. Updates also tend to occur less frequently with open source software
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