Platform as a service (PaaS) or application platform as a service (aPaaS) is a category of cloud computing services that provides a platform allowing customers to develop, run, and manage applications without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. PaaS can be delivered in two ways: as a public cloud service from a provider, where the consumer controls software deployment with minimal configuration options, and the provider provides the networks, servers, storage, operating system (OS), 'middleware' (e.g. Java runtime, .NET runtime, integration, etc.), database and other services to host the consumer's application; or as a private service (software or appliance) inside the firewall, or as software deployed on a public infrastructure as a service.

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Development and uses

Fotango, a London-based (Old Street) company owned by Canon Europe launched the world's first public platform as a service known as 'Zimki'. It was developed in 2005 with a beta launch in March 2006 and a public launch at EuroOSCON in 2006. Zimki was an end-to-end JavaScript web application development and utility computing platform that removed all the repetitive tasks encountered when creating web applications and web services. All aspects of infrastructure and operations from provisioning and setting up virtual servers, scaling, configuration, security and backups were done automatically by Zimki. Zimki introduced the tagline 'Pre-Shaved Yaks' to describe the removal of all these repetitive tasks.

Zimki was a pure 'pay as you go' code execution platform which enabled developers to build and deploy applications or web services without incurring any start-up costs on a true utility based computing platform. Charging was done on storage used, network traffic and JSOPs (Javascript Operations). It provided a multi-tenant platform where developers could create entire applications (front and back end through SSJS) by using a single language - Javascript, with all development, billing, monitoring and application control exposed through APIs and a range of component services from a No-SQL object store to Message Queue services. Furthermore, all functions within Zimki could be exposed as web services and Zimki provided billing analysis down to individual functions.

Whilst the Zimki platform was rapidly growing and Fotango was profitable, the parent company decided this area was not core and the service was closed in Dec 2007. At the time of its closure, Zimki had several thousand developer accounts and had demonstrated the technical viability of Platform as a Service but also provided the first example of the perils of being dependent upon a single provider. This risk had been highlighted in July 2007, when the CEO gave a presentation on Zimki at OSCON 2007 which announced that Zimki would no longer be open sourced and discussed the future of what was then called Framework as a Service (later renamed to Platform as a Service) covering the importance of a market of providers based upon an open source reference model. 

In April 2008, Google launched App Engine, with a free trial version limited to 10,000 developers. This was said to have "turned the Internet cloud computing space into a fully-fledged industry virtually overnight."

The original intent of PaaS was to simplify the code-writing process for developers, with the infrastructure and operations handled by the PaaS provider. Originally, all PaaSes were in the public cloud. Because many companies did not want to have everything in the public cloud, private and hybrid PaaS options (managed by internal IT departments) were created.

PaaS provides an environment for developers and companies to create, host and deploy applications, saving developers from the complexities of the infrastructure side (setting up, configuring and managing elements such as servers and databases). PaaS can improve the speed of developing an app, and allow the consumer to focus on the application itself. With PaaS, the consumer manages applications and data, while the provider (in public PaaS) or IT department (in private PaaS) manages runtime, middleware, operating system, virtualization, servers, storage and networking. Development tools provided by the vendor are customized according to the needs of the user. The user can choose to maintain the software, or have the vendor maintain it

PaaS offerings may also include facilities for application design, application development, testing and deployment, as well as services such as team collaboration, web service integration, and marshalling, database integration, security, scalability, storage, persistence, state management, application versioning, application instrumentation, and developer community facilitation. Besides the service engineering aspects, PaaS offerings include mechanisms for service management, such as monitoring, workflow management, discovery and reservation.

Advantages and disadvantages

The advantages of PaaS are primarily that it allows for higher-level programming with dramatically reduced complexity; the overall development of the application can be more effective, as it has built-in infrastructure; and maintenance and enhancement of the application is easier. It can also be useful in situations where multiple developers are working on a single project involving parties who are not located nearby.

One disadvantage of PaaS offerings is that developers may not be able to use a full range of conventional tools (e.g. relational databases, with unrestricted joins). Another possible disadvantage is being locked in to a certain platform. However, most PaaSes are relatively lock-in free.

Types
Public, private and hybrid
There are several types of PaaS, including public, private and hybrid. PaaS was originally intended for applications on public cloud services, before expanding to include private and hybrid options.

Public PaaS is derived from software as a service (SaaS), and is situated in cloud computing between SaaS and infrastructure as a service (IaaS). SaaS is software that is hosted in the cloud, so that it doesn't take up hard drive from the computer of the user or the servers of a company. IaaS provides virtual hardware from a provider with adjustable scalability. With IaaS, the user still has to manage the server, whereas with PaaS the server management is done by the provider. IBM Bluemix (also private and hybrid), Amazon AWS and Heroku are some of the commercial public cloud PaaS providers.

A private PaaS can typically be downloaded and installed either in a company's on-premises data center, or in a public cloud. Once the software is installed on one or more machines, the private PaaS arranges the application and database components into a single hosting platform. Private PaaS vendors include Apprenda, which started out on the Microsoft .NET platform before rolling out a Java PaaS; Red Hat's OpenShift and Pivotal Cloud Foundry. Apprenda and Microsoft once considered to be two of the only PaaSes that provide superior .NET support. Now joined by the publicly announced  Microsoft and IBM Partnership programme.

Hybrid PaaS is typically a deployment consisting of a mix of public and private deployments. An example here is IBM Bluemix which is delivered as a single, integrated cloud platform across public, dedicated, and on-premises deployment models.

Mobile PaaS
Initiated in 2012, mobile PaaS (mPaaS) provides development capabilities for mobile app designers and developers. The Yankee Group identified mPaaS as one of its themes for 2014, naming a number of providers including Kinvey, CloudMine, AnyPresence, FeedHenry, FatFractal and Point.io.

Open PaaS
Open PaaS does not include hosting, but rather it provides open source software allowing a PaaS provider to run applications in an open source environment. For example, AppScale allows a user to deploy some applications written for Google App Engine to their own servers, providing datastore access from a standard SQL or NoSQL database. Some open platforms let the developer use any programming language, database, operating system or server to deploy their applications.

PaaS for Rapid Development
In 2014, Forrester Research defined enterprise public cloud platforms for rapid developers as an emerging trend, naming a number of providers including Mendix, Salesforce.com, OutSystems and Acquia.

System types
PaaS is found on the following types of systems:

Add-on development facilities
These facilities allow customization of existing SaaS applications, often requiring PaaS developers and their users to purchase subscriptions to the add-on SaaS application.
Stand alone environments
Stand-alone PaaS environments do not include technical, licensing or financial dependencies on specific SaaS applications or web services, and are intended to provide a generalized development environment.
Application delivery-only environments
Delivery-only PaaS offerings generally focus on hosting services, such as security and on-demand scalability. The service does not include development, debugging and test capabilities, though they may be supplied offline (via an Eclipse plugin, for example).

Providers
There are various types of PaaS providers. All offer application hosting and a deployment environment, along with various integrated services. Services offer varying levels of scalability and maintenance. Developers can write an application and upload it to a PaaS that supports their software language of choice, and the application runs on that PaaS.

What is Node.js?

Node.js is a platform built on Chrome’s JavaScript runtime for easily building fast and scalable network applications. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

Node.js. It’s the latest in a long line of “Are you cool enough to use me?” programming languages, APIs, and toolkits. In that sense, it lands squarely in the tradition of Rails, and Ajax, and Hadoop, and even to some degree iPhone programming and HTML5. Go to a big technical conference, and you’ll almost certainly find a few talks on Node.js, although most will fly far over the head of the common mortal programmer.

Node.js training

Dig a little deeper, and you’ll hear that Node.js (or, as it’s more briefly called by many, simply “Node”) is a server-side solution for JavaScript, and in particular, for receiving and responding to HTTP requests. If that doesn’t completely boggle your mind, by the time the conversation heats up with discussion of ports, sockets, and threads, you’ll tend to glaze over. Is this really JavaScript? In fact, why in the world would anyone want to run JavaScript outside of a browser, let alone the server?

It is an open source, cross-platform runtime environment for developing server-side and networking applications. Node.js applications are written in JavaScript, and can be run within the Node.js runtime on OS X, Microsoft Windows, and Linux.

Node.js also provides a rich library of various JavaScript modules which simplifies the development of web applications using Node.js to a great extent.

Node.js = Runtime Environment + JavaScript Library

The good news is that you’re hearing (and thinking) about the right things. Node really is concerned with network programming and server-side request/response processing. The bad news is that like Rails, Ajax, and Hadoop before it, there’s precious little clear information available. There will be, in time — as there now is for these other “cool” frameworks that have matured — but why wait for a book or a tutorial when you might be able to use Node today, and dramatically improve the maintainability of your code and even the ease with which you bring on programmers?

A warning to the Node experts out there

Node is like most technologies, that are new to the masses, but old hat to the experienced few: it’s opaque and weird to most but completely usable for a small group. The result is that if you’ve never worked with Node, you’re going to need to start with some pretty basic server-side scripts. Take your time making sure you know what’s going on, because while this is JavaScript, it’s not operating like the client-side JavaScript you’re used to. In fact, you’re going to have to twist your JavaScript brain around event loops and waiting, and even a bit of network theory.

Unfortunately, this means that if you’ve been working and playing with Node for a year or two, much of this article is going to seem pedestrian and overly simplistic. You’ll look for things like using Node on the client, or heavy theory discussions on evented I/O and reactor patterns, and npm. The reality is that while that’s all interesting — and advances Node to some pretty epic status — it’s incomprehensible to someone who is just getting started out. Given that, maybe you should pass this piece onto your co-workers who don’t know Node, and then when they’re buying into Node’s usefulness, start to bring them along with the more advanced Node use cases.

Performance Counters

There are a few key performance counters to watch when you’re trying to monitor the performance of your SSIS package. These counters greatly help you troubleshoot issues, if you have memory contention or you need to tweak your SSIS settings. Inside the System Monitor (also known to old-school administrators as perfmon) is a performance object called SQLServer: SSIS Pipeline. (There are quite a few other objects as well, but they are not useful enough to describe here.)

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If you’re trying to benchmark how many rows are being read and written, you can use the Rows Read and Rows Written counters. These counters show you the number of rows since you starting monitoring the packages. It sums all rows in total across all packages and does not allow you to narrow the results down to a single package.

The most important counters are the buffer counters. The Buffer Memory counter shows you the amount of memory, in total, being used by all the packages. The Buffers In Use counter indicates how many buffers are actually in use. The critical counter here, though, is Buffers Spooled. This shows you how many buffers have been written from memory to disk. This is critical for the performance of your system. If you have buffers being spooled, you have a potential memory contention, and you may want to consider increasing the memory or changing your buffer settings in your package. This is covered in more detail in Understanding and Tuning the Data Flow Engine Topic, but you should never see this number creep above a 5, if not 0.

Dimensional modeling Concept Introduction

Dimensional modeling (DM) names a set of techniques and concepts used in data warehouse design. It is considered to be different from entity-relationship modeling (ER). Dimensional modeling does not necessarily involve a relational database. The same modeling approach, at the logical level, can be used for any physical form, such as multidimensional database or even flat files. According to data warehousing consultant Ralph Kimball, DM is a design technique for databases intended to support end-user queries in a data warehouse. It is oriented around understandability and performance. According to him, although transaction-oriented ER is very useful for the transaction capture, it should be avoided for end-user delivery.

Dimensional modeling always uses the concepts of facts (measures), and dimensions (context). Facts are typically (but not always) numeric values that can be aggregated, and dimensions are groups of hierarchies and descriptors that define the facts. For example, sales amount is a fact; timestamp, product, register#, store#, etc. are elements of dimensions. Dimensional models are built by business process area, e.g. store sales, inventory, claims, etc. Because the different business process areas share some but not all dimensions, efficiency in design, operation, and consistency, is achieved using conformed dimensions, i.e. using one copy of the shared dimension across subject areas. The term "conformed dimensions" was originated by Ralph Kimball.


Dimensional modeling process

The dimensional model is built on a star-like schema, with dimensions surrounding the fact table. To build the schema, the following design model is used:

Choose the business process
Declare the grain
Identify the dimensions
Identify the fact
Choose the business process
The process of dimensional modeling builds on a 4-step design method that helps to ensure the usability of the dimensional model and the use of the data warehouse. The basics in the design build on the actual business process which the data warehouse should cover. Therefore, the first step in the model is to describe the business process which the model builds on. This could for instance be a sales situation in a retail store. To describe the business process, one can choose to do this in plain text or use basic Business Process Modeling Notation (BPMN) or other design guides like the Unified Modeling Language (UML).

Declare the grain
After describing the business process, the next step in the design is to declare the grain of the model. The grain of the model is the exact description of what the dimensional model should be focusing on. This could for instance be “An individual line item on a customer slip from a retail store”. To clarify what the grain means, you should pick the central process and describe it with one sentence. Furthermore, the grain (sentence) is what you are going to build your dimensions and fact table from. You might find it necessary to go back to this step to alter the grain due to new information gained on what your model is supposed to be able to deliver.

Identify the dimensions

The third step in the design process is to define the dimensions of the model. The dimensions must be defined within the grain from the second step of the 4-step process. Dimensions are the foundation of the fact table, and is where the data for the fact table is collected. Typically dimensions are nouns like date, store, inventory etc. These dimensions are where all the data is stored. For example, the date dimension could contain data such as year, month and weekday.

Identify the facts

After defining the dimensions, the next step in the process is to make keys for the fact table. This step is to identify the numeric facts that will populate each fact table row. This step is closely related to the business users of the system, since this is where they get access to data stored in the data warehouse. Therefore, most of the fact table rows are numerical, additive figures such as quantity or cost per unit, etc.

Dimension Normalization

Dimensional normalization or snowflaking removes redundant attributes, which are known in the normal flatten de-normalized dimensions. Dimensions are strictly joined together in sub dimensions.

Snowflaking has an influence on the data structure that differs from many philosophies of data warehouses. Single data (fact) table surrounded by multiple descriptive (dimension) tables

Developers often don't normalize dimensions due to several reasons:

Normalization makes the data structure more complex
Performance can be slower, due to the many joins between tables
The space savings are minimal
Bitmap indexes can't be used
Query performance. 3NF databases suffer from performance problems when aggregating or retrieving many dimensional values that may require analysis. If you are only going to do operational reports then you may be able to get by with 3NF because your operational user will be looking for very fine grain data.
There are some arguments on why normalization can be useful. It can be an advantage when part of hierarchy is common to more than one dimension. For example, a geographic dimension may be reusable because both the customer and supplier dimensions use it.


Benefits of dimensional modeling
Benefits of the dimensional model are the following:

Understandability. Compared to the normalized model, the dimensional model is easier to understand and more intuitive. In dimensional models, information is grouped into coherent business categories or dimensions, making it easier to read and interpret. Simplicity also allows software to navigate databases efficiently. In normalized models, data is divided into many discrete entities and even a simple business process might result in dozens of tables joined together in a complex way.
Query performance. Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating. The predictable framework of a dimensional model allows the database to make strong assumptions about the data which may have a positive impact on performance. Each dimension is an equivalent entry point into the fact table, and this symmetrical structure allows effective handling of complex queries. Query optimization for star-joined databases is simple, predictable, and controllable.
Extensibility. Dimensional models are scalable and easily accommodate unexpected new data. Existing tables can be changed in place either by simply adding new data rows into the table or executing SQL alter table commands. No queries or applications that sit on top of the data warehouse need to be reprogrammed to accommodate changes. Old queries and applications continue to run without yielding different results. But in normalized models each modification should be considered carefully, because of the complex dependencies between database tables.


Dimensional Models, Hadoop, and Big Data

We still get the benefits of dimensional models on Hadoop and similar big data frameworks. However, some features of Hadoop require us to slightly adopt the standard approach to dimensional modelling.

The Hadoop File System is immutable. We can only add but not update data. As a result we can only append records to dimension tables. Slowly Changing Dimensions on Hadoop become the default behaviour. In order to get the latest and most up to date record in a dimension table we have three options. First, we can create a View that retrieves the latest record using windowing functions. Second, we can have a compaction service running in the background that recreates the latest state. Third, we can store our dimension tables in mutable storage, e.g. HBase and federate queries across the two types of storage.
The way how data is distributed across HDFS makes it expensive to join data. In a distributed relational database (MPP) we can co-locate records with the same primary and foreign keys on the same node in a cluster. This makes it relatively cheap to join very large tables. No data needs to travel across the network to perform the join. This is very different on Hadoop and HDFS. On HDFS tables are split into big chunks and distributed across the nodes on our cluster. We don’t have any control on how individual records and their keys are spread across the cluster. As a result joins on Hadoop for two very large tables are quite expensive as data has to travel across the network. We should avoid joins where possible. For a large fact and dimension table we can de-normalise the dimension table directly into the fact table. For two very large transaction tables we can nest the records of the child table inside the parent table and flatten out the data at run time.

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Advanced planning and scheduling Concept introduction

Advanced planning and scheduling (APS, also known as advanced manufacturing) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand.[1] APS is especially well-suited to environments where simpler planning methods cannot adequately address complex trade-offs between competing priorities. Production scheduling is intrinsically very difficult due to the (approximately) factorial dependence of the size of the solution space on the number of items/products to be manufactured.


Difficulty of production planning

Traditional production planning and scheduling systems (such as manufacturing resource planning) use a stepwise procedure to allocate material and production capacity. This approach is simple but cumbersome, and does not readily adapt to changes in demand, resource capacity or material availability. Materials and capacity are planned separately, and many systems do not consider material or capacity constraints, leading to infeasible plans. However, attempts to change to the new system have not always been successful, which has called for the combination of management philosophy with manufacturing.

Unlike previous systems, APS simultaneously plans and schedules production based on available materials, labor and plant capacity.

APS has commonly been applied where one or more of the following conditions are present:

make to order (as distinct from make to stock) manufacturing
capital-intensive production processes, where plant capacity is constrained
products 'competing' for plant capacity: where many different products are produced in each facility
products that require a large number of components or manufacturing tasks
production necessitates frequent schedule changes which cannot be predicted before the event
Advanced planning & scheduling software enables manufacturing scheduling and advanced scheduling optimization within these environments.

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