DEV Community: Natasah Mary

Introduction to Netsuite

Natasah Mary — Mon, 14 Aug 2017 05:54:42 +0000

NetSuite Inc. is a United States software company based in San Mateo, CA. The company markets cloud-based software-as-a-service integrated business management software. This includes software for ERP, accounting, order management, inventory, CRM, professional services automation (PSA), and E-commerce applications. NetSuite was originally named NetLedger by its founders, Larry Ellison and one of his early employees, Evan Goldberg

Products:

NetSuite OneWorldNetSuite OneWorld is software for global businesses. It encompasses modules for accounting/Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), ecommerce and professional services automation. NetSuite launched OneWorld in 2008 to target midsized companies that are multinational or have multiple subsidiaries. It allows companies to run multiple companies or subsidiaries off of one system. The software runs in the cloud via a Software as a Service (SaaS) model, meaning NetSuite runs and maintains the computing infrastructure and delivers it over the Internet. Users access the software via a Web browser and pay a monthly subscription price. OneWorld supports more than 170 currencies, 15 languages, and country-specific accounting standards in North America, Europe and Asia. A foreign currency revaluation engine lets businesses monitor exchange rate fluctuations continuously. In 2012, NetSuite offered OneWorld as a Two Tier ERP option for companies running Oracle software. NetSuite argues that the two-tier approach can cut deployment time and costs instead of installing on-premise ERP in divisions.

NetSuite SuiteCommerceSuiteCommerce is NetSuite’s software package for retailers, integrating ecommerce websites with in-store sales and back office accounting tools. SuiteCommerce is a central system that manages all transactions and customer interactions via the Web, mobile, social and brick-and-mortar. NetSuite acquired Retail Anywhere, one of its partners, in January 2013 to provide a Point of Sale (POS) system to complement SuiteCommerce. Integrating Retail Anywhere with NetSuite's cloud computing platform gives businesses a full view of each customer across all retail channels, while providing inventory and financial information.

SuiteCloud Developer Network NetSuite’s SuiteCloud Developer Network is a Java-based development platform for NetSuite partners and customers to customize the software and build new applications to complement NetSuite. It includes the SuiteApp solution directory and a number of developer tools.

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What Is Integration Services and Why Do I Need It?

Natasah Mary — Thu, 10 Aug 2017 12:45:09 +0000

What Is SQL Server Integration Services?

Depending on whom you ask, you might get different answers to that question ranging from descriptions such as a data import/export wizard, to an ETL tool, to a control flow engine, to an application platform, or to a high-performance data transformation pipeline. All are correct because Integration Services is a set of utilities, applications, designers, components, and services all wrapped up into one powerful software application suite. SQL Server Integration Services SSIS is many things to many people.

Data Import/Export Wizard
One of the most popular features of Integration Services is the Import/Export Wizard, which makes it easy to move data from a source location such as a flat file or database table to a flat file, table, or other destination. The Import/Export Wizard was the first utility developed back in the SQL Server 7.0 time frame and continues today as an important utility in the database administrator’s (DBA) toolbox.
ETL Tool
ETL is an acronym for Extract, Transform, and Load and describes the processes that take place in data warehousing environments for extracting data from source transaction systems; transforming, cleaning, deduplicating, and conforming the data; and finally loading it into cubes or other analysis destinations. Although Data Transformation Services (DTS), Integration Services’ predecessor application, was considered a valuable tool for doing ETL, Integration Services is where true Enterprise ETL became available in SQL Server.
Control Flow Engine
The processes involved in moving data from location to location and transforming it along the way are not restricted to only processing data. Integration Services provides a control flow for performing work that is tangentially related to the actual processing that happens in data flow, including downloading and renaming files, dropping and creating tables, rebuilding indexes, performing backups, and any other number of tasks. Integration Services provides a full-featured control flow to support such activities.
Application Platform
Developers can create applications that use Integration Services as a platform, embedding the engines within their application using the provided object models. As a developer, you can embed the Integration Services engines and components within your applications using the object models.
High Performance Data Transformation Data Pipeline
That’s a mouthful and really incorporates two ideas: high performance and data pipelining. The Data Flow Task is a high-performance tool because you can use it to perform complex data transformations on very large datasets for incredibly performant processing. The pipeline concept means that you can process data from multiple heterogeneous data sources, through multiple parallel sequential transformations, into multiple heterogeneous data destinations, making it possible to process data found in differing formats and on differing media in one common “sandbox location.
A Short Integration Services History
Integration Services is the successor to Data Transformation Services (DTS). DTS had humble beginnings. It was started on a shoestring budget with very few resources. Its first incarnation was a proof-of-concept transformation, which later became known as the data pump. The proof of concept caught the attention of some folks around Microsoft, and it was given some funding.

The first release of DTS shipped with SQL Server 7.0 to receptive users. The alternatives at the time were either difficult to work with, expensive, or both. Many DBAs were forced to write custom transformation software, which was inflexible and difficult to maintain. Some tools had limitations, such as the need for source and destination schemas to match exactly, direct dependence on a particular database product, and/or no transformation capabilities. Many wrote custom parsing and transformation applications. For example, many companies are only now converting from hand-coded flat file parsers, SQL scripts, and transformation code to a standard platform such as Integration Services.

The first release of DTS addressed several of these issues and simplified life for a lot of people. By using OLEDB for its data access layer, DTS could access various data sources with little or no custom coding. DTS was also affordable because it shipped “in the box with SQL Server. Users had access to all the power of more expensive products, yet incurred no additional cost for their ETL tools. This was obviously a benefit to IT shops trying to stretch their budgets. DTS was a flexible product that was easy to use. There were also a number of standard tasks in the box, including the Transform Data, Execute Process, Active X Script, Execute SQL, and Bulk Insert Tasks.

SQL Server 8.0 added even more functionality by adding more tasks. The Execute Package, FTP, and MSMQ Tasks added incremental improvements across the product. However, users experienced some frustration with DTS when attempting to work with large datasets and some of the other limitations inherent in a script-based tool. The time was ripe to create a truly enterprise-ready integration tool.

In 2000, SQL Server decided to make a substantial investment in the ETL and Integration space and brought together some talented folks who formulated the ideas behind the Data Flow Task and the next version of Integration Services. Over a period of five years, the development time frame for SQL Server 2005, the DTS team completely redesigned and rewrote DTS to become Integration Services 2005.

Integration Services 2008 is the next incarnation of that release. While not as revolutionary a release as in 2005, Integration Services 2008 brings some new capabilities and incremental improvements across the product.
Summary
SQL Server Integration Services is a complex, flexible, and powerful product with many uses. Many people use Integration Services for enterprise extract, transform, and load purposes. Others use it for an IT management tool for doing things like backing up databases. Others use it as an integration tool for synchronizing multiple systems. However you use it, Integration Services can be frustrating and difficult to learn. Hopefully, this book will enable you to take advantage of all that power and flexibility in as little time as possible.
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Advanced planning and scheduling Concept introduction

Natasah Mary — Fri, 28 Jul 2017 10:20:17 +0000

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 planningTraditional 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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tabular model

Natasah Mary — Tue, 13 Jun 2017 05:58:26 +0000

Introducing the tabular model
This post introduces SQL Server Analysis Services (SSAS) 2016, provides a brief overview of what the tabular model is, and explores its relationship to the multidimensional model, to SSAS 2016 as a whole, and to the wider Microsoft business intelligence (BI) stack. This chapter will help you make what is probably the most important decision in your project’s life cycle: whether you should use a tabular model or a multidimensional model. Finally, it includes a short description of the main differences in tabular models between SSAS 2016 and previous versions.

EMANTIC MODELS IN ANALYSIS SERVICES
In the Microsoft ecosystem, BI is not a single product, but a set of features distributed across several products. The following sections explain the role of SSAS in this ecosystem. The tabular model is one of the two types of semantic models you can create in SSAS. (The other is the multidimensional model.)

What is Analysis Services and why should I use it?
SSAS is an online analytical data engine–a type of service that is highly optimized for the kinds of queries and calculations that are common in a business intelligence environment. It does many of the same things that a relational database can do, but it differs in many respects. In most cases, it will be easier to develop your BI solution by using SSAS in combination with a relational database, such as Microsoft SQL Server, than by using SQL Server alone. SSAS does not replace the need for a relational database or a properly designed data warehouse, however.

One way of thinking about SSAS is as an extra layer of metadata, or a semantic model, that sits on top of a data warehouse in a relational database. This extra layer contains information about how fact tables and dimension tables should be joined, how measures should aggregate up, how users should be able to explore the data through hierarchies, the definitions of common calculations, and so on. This layer also includes one or more models containing the business logic of your data warehouse. End usersquery these models rather than the underlying relational database. With all this information stored in a central place and shared by all users, the queries that users need to write become much simpler. In most cases, all a query needs to do is describe which columns and rows are required, and the model applies the appropriate business logic to ensure that the numbers that are returned make sense. Most important, it becomes impossible to write a query that returns “incorrect” results due to a mistake by end users. This, in turn, means that end-user reporting and analysis tools must do much less work and can provide a clearer visual interface for end users to build queries. It also means that different tools can connect to the same model and return consistent results.

Another way of thinking about SSAS is as a kind of cache that you can use to speed up reporting. In most scenarios in which SSAS is used, it is loaded with a copy of the data in the data warehouse. Subsequently, all reporting and analytic queries are run against SSAS instead of the relational database. Even though modern relational databases are highly optimized and contain many features specifically aimed at BI reporting, SSAS specifically designed for this type of workload and can, in most cases, achieve much better query performance. For end users, optimized query performance is extremely important because it allows them to browse through data without waiting a long time for reports to run and without any breaks in their chain of thought.

For the IT department, the biggest benefit of all this is that it becomes possible to transfer the burden of authoring reports to end users. A common problem with BI projects that do not use Online Analytical Processing (OLAP) is that the IT department must build not only a data warehouse but also a set of reports to go with it. This increases the amount of time and effort involved, and can be a cause of frustration for the business when it finds that IT is unable to understand its reporting requirements or to respond to them as quickly as is desirable. When an OLAP database such as SSAS is used, the IT department can expose the models it contains to end users and enable them to build reports themselves, using whatever tool with which they feel comfortable. By far the most popular client tool is Microsoft Excel. Ever since Office 2000, Excel PivotTables have been able to connect directly to SSAS multidimensional models (also known as cubes), and Excel 2016 has some extremely powerful capabilities as a client for SSAS.

All in all, Analysis Services not only reduces the IT department’s workload but also increases end-user satisfaction. Users now find they can build the reports they want and explore the data at their own pace, without having to go through an intermediary.

A short history of Analysis Services
SQL Server Analysis Services–or OLAP Services, as it was originally called when it was released in 1998 with SQL Server 7.0–was the first foray by Microsoft into the BI market. When it was released, many people saw it as an indicator that BI software was ready to break out of its niche and reach a mass market. Over the past 16 years, the success of Analysis Services and the rest of the Microsoft BI stack has proved them correct.

SQL Server Analysis Services 2000 was the first version of Analysis Services to gain significant traction in the marketplace. Analysis Services 2005 became the biggest-selling OLAP tool not long after its release. As Analysis Services 2008 and 2008 R2 improved scalability and performance still further, more and more companies started to adopt it as a cornerstone of their BI strategy. By 2010, terabyte-sized cubes were not uncommon. The famous example of the 24-terabyte (TB) cube Yahoo! built shows just what can be achieved.

Microsoft Analysis Services 2012 leveraged an existing infrastructure to introduce a new engine and a new type of data model, essentially becoming two products in one. It still contains Analysis Services from the SQL Server 2008 R2 release and before, but that has become known as the multidimensional model. Although Analysis Services has seen a few improvements since the 2008 R2 release, related to performance, scalability, and manageability, no new major functionality has been introduced since then. Meanwhile, Analysis Services 2012 has a new data-modeling experience and a new engine that closely resembles the Power Pivot and Power BI data modeling experience. This is called the tabular model.

The following version of SQL Server did not introduce new BI features, so there are no differences between Analysis Services 2012 and 2014, provided you run the latest service packs and cumulative updates. However, Analysis Services 2016 introduces many new features and improvements to the tabular model, to the point that we considered it necessary to write a new book about it.

The tabular model in Analysis Services 2016 is the subject of this book. We will cover migration issues from tabular models created in former versions of Analysis Services, but if you are not planning to upgrade to 2016, then we recommend you read our previous book, Microsoft SQL Server 2012 Analysis Services: The BISM Tabular Model.

UNDERSTANDING TABULAR AND MULTIDIMENSIONAL
This section explains a little about the architecture of Analysis Services, which since SQL Server 2012 has been split into two modes: Tabular and Multidimensional.

When installing Analysis Services, you must choose between installing an instance that runs in Tabular mode and one that runs in Multidimensional mode. (For more details on the installation process, see Chapter 2, “Getting started with the tabular model.”) A Tabular mode instance can support only databases containing tabular models, and a Multidimensional mode instance can support only databases containing multidimensional models. Although these two parts of Analysis Services share much of the same code underneath, in most respects they can be treated as separate products. The concepts involved in designing the two types of models are very different. You cannot convert a tabular database into a multidimensional database, or vice versa, without rebuilding everything from scratch. That said, it is important to emphasize the fact that, from an end user’s point of view, the two models do almost exactly the same things and appear almost identical when used through client tools such as Excel and Power BI.

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