telephone:InetSoft's Data Mashup Engine as a Data Warehousing Software Alternative
Are you considering whether to purchase data warehousing software or not? InetSoft's business intelligence software can access data warehouses and can also mashup operational data stores directly, saving the cost of acquiring data warehouse software. Read articles below about InetSoft's software for dashboards, reporting, and data integration:
JavaScript Object-Oriented Concepts - JavaScript is an object-oriented programming (OOP) language, and provides various objects and methods, as well as the ability to create user-defined methods. To use JavaScript effectively, it is important to understand the following concepts. Properties are predefined data-storage locations associated with an object. You can 'get' or 'set' these properties to observe or alter the corresponding attributes of the object. For example, every report element has a visibility property: Table1.visible = false; Methods are predefined functions associated with an object. (In general, these functions operate on the object itself.) For example, the CALC object provides a method for obtaining today's date: Text1.text = CALC.today()...
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Joining Multiple Data Sources - To embed static data manually into an element (Table, Section or Chart), follow these steps. 1. Right-click the element and select ‘Bind Data’. This opens the ‘Data Binding’ dialog box. 2. Under the Data tab in the ‘Data Binding’ dialog box, expand the ‘Report’ node, and select ‘Embedded Data’. 3. Click the ‘Edit’ button on the right. This opens the ‘Embedded Data Editing’ dialog box. 4. In the ‘Embedded Data Editing’ dialog box, enter values in the ‘Rows’ and ‘Columns’ fields to specify the table size. You can also specify Header and Trailer rows and columns. 5. Click on the table cells to enter values. Specify the data type for each cell from the pop-up menu within the cell...
List Comparison for Queries Using Hierarchical Meta Data - If you want to get a list of all employees with C++ programming skill, you can add a condition to the employee node to select only people with C++ on their skill list. However, since each employee may have multiple skills linked in the employee record, a simple string comparison does not give the correct result. This condition would work if all employees only had one skill listed. If the C++ programming were listed as the second skill of an employee, the comparison would return false because a list of skills is converted to a scalar value by using the value of the first child. You need an expression to compare the skill list with a value, and the comparison should be true if any value on the list matches the skill. The following list comparison expression returns the correct result...
Making Information the Most Valuable Commodity - There is some thinking out that at this point the most valuable commodity and manufacturing is not energy. It’s not the goods. It’s the information that you use, that information about customers, about your products, about what customers are actually looking at. How do you build an entire solution for them? Information is the most valuable commodity. Information about your supply chain, your processes, everything is valuable. The issue that you see with this right now, a lot of times is that, that information is not treated in any way, shape or form as if it is the most valuable commodity. A lot of data in a lot of firms is still scattered in lots and lots of places. It resides in a variety of different databases. It’s not uncommon to see even a small or midsized company with 20, 30, 40 different databases or more. You see a lot of data that is sitting in spreadsheets, especially for firms or plants that are using improvement methodology where they are going outside an established enterprise resource planning system. You actually see a lot of company’s data that still resides in hard copies, pieces of paper. Even when it is digitally located in databases, the issue is it’s not in a centralized database. It’s in spreadsheets. It’s in product drawings. It’s sitting on in some computers somewhere, somebody’s laptop, and that’s a problem...
Manipulating Data Blocks - Worksheets provide a full complement of sophisticated data manipulation tools, including joins, filters, and concatenations. You will now use some grouping and aggregation tools to manipulate the Data Block that you created earlier in Constructing Data Blocks. 1. Right-click on the table’s top row and select ‘Group and Aggregate’ from the drop-down menu. This will open the ‘Aggregate’ dialog box. 2. In the ‘Aggregate’ dialog box, make the following selections: a. For the ‘Order.Date’ field, select the ‘Group’ checkbox and select ‘Month’ from the drop-down list. b. For the ‘Product.Total’ field, select the ‘Aggregate’ checkbox and choose ‘Sum’ from the drop-down list. 3. Click ‘OK’ to exit the ‘Aggregate’ dialog box...
MapReduce and Hadoop - Key Technologies for Big Data - So we’re approaching big data in a couple of ways. MapReduce and Hadoop are key technologies associated with big data. There are a lot of questions out there surrounding these technologies. For instance, is it better to use Map Reduce or a data warehouse for big data? That’s a good question. A lot of people are struggling with that, and there’s a lot of religious fervor on both sides of that camp. Should we use commodity hardware with a parallel Map Reduce for our analytics, or should we use a traditional data warehouse with relational tools and relational capabilities. And the answer is simple. If you had a screwdriver you could pound in a nail with your screwdriver, but you might choose a different tool which would be more effective. So MapReduce and Hadoop which are two sides of the same coin. They provide process oriented parallelism, and they use a lot of process languages. In contrast, relational databases have parallelism built into the data, and they handle the SQL language. So you use the correct tool for the job. And ultimately these are very complementary...
Mashups Empowering End Users - There should be a general movement in this whole industry of business intelligence or decision support or whatever you want to call it, analysis, of empowering the end-user, and so one of the values that I see in the mashup concept is that ideally what you need to be doing is enabling your end users to mix and match data sets very quickly and very easily. that’s where the collaboration perspective comes in. And again for the mashups to achieve their goal, any type of business agility, it’s the collaboration angle that I am always looking for...
Materialized OLAP Overlays - Materializing an OLAP overlay involves the implementation of a physical data store such as a star schema in place of the OLAP overlay’s logical structure. An OLAP overlay is a low overhead OLAP option, but other data warehouse options may be more suitable under certain conditions. such as the following: * Data Mismatch: when data is not in a 1-n relationship from dimension to fact, the remedy is to create data that does satisfy these conditions. * Increase performance: large databases could produce long running queries. A dedicated Data Mart or Data Warehouse can produce the best performance, by physically implementing the OLAP functions...
MDM as an Enterprise Initiative - While MDM is an enterprise initiative, I think in the end it should be a horizontal kind of platform solution for the organization. I think it’s best, and most of the success will come by looking at the problem a little bit more vertically and trying to solve actual business problems. When should vendor technology evaluations and selections come into the process? Are there any tips for evaluating and sorting out MDM technology options? Well, there are multiple definitions of MDM out there. I think that’s one issue that I know everybody is dealing with because we often still get questions what is MDM? What exactly do you mean by that? So I think that there are a lot of rival definitions. I think there is also a general distinguishing feature between analytical and operational MDM, and that is one that we see as well where analytical is more about supporting BI and reporting and analysis activities, including things like reporting hierarchies, whereas operational MDM is more about establishing a gold copy or reference data that can be used by multiple transaction-oriented applications. So that’s one distinction that people should look at when they are looking at tools and looking at technology to combat the problem...
More JavaScript Syntax Basics - JavaScript does not have an integer type and a float type. All numbers are treated as float by default. var total = 2 + 3; text = total; // this will convert the number to 5.0 To force a number to be treated as an integer, use the toFixed() method of the number object and give a decimal point of zero. A number constant is in decimal format by default, hexadecimal format if it starts with '0x', and octal format if it starts with '0'. Numerical computations are performed with the usual operators, +, *, /, -. The increment and decrement operators (++, --–) are also available...
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