Active Data Excel

[Gabelo Camphire]

Iknow from experience that many users seem unaware that dates in Excel are actually numbers. It drives me crazy, as it can easily bugger up formulas later on down the line, but fortunately we can deal with this.

So Cherisse, the answer is... Of course! One thing I would advise though... if you can, prevent the user from entering an invalid date to begin with using Data Validation first, then you don't have to worry about the garbage ever getting in to your spreadsheet. But, because you asked, I'll walk through the conditional format route first...

Wherever possible, you should try pro-actively validate your data before the user can even commit it. The Data Validation tool is great for this purpose. You will find, however, that there will be times where you just can't pro-actively evaluate everything... and that is where conditional formatting can be really handy as a Re-Active Data Validation tool. I often use the two in combination to generate a really robust interface.

We all recognize budget is a constant concern for auditors and investigators. Oftentimes our technology budgets are just too tight. We find ourselves looking for powerful tools to add to our toolbox that do not break the bank. One of these tools is Active Data for Excel. Active Data is an add-in for Excel that gives the user some of the powers of a data mining software without busting the budget. This is a great tool for performing data analytics.

Using the split column menu, select the column you would like to separate (Date) and then chose one of the options available. In this instance I have selected to split the year, month and day for date columns.

If you wanted to see the Sales Person Name in the May 2014 Sales Report, you could use the merge function and quickly have all information from Sales Person Master visible in the May 2014 Sales Report.

For this example I choose to merge the two worksheets on the Sales Person ID field and to include all columns for May 2014 Sales Report and all columns from Sales Person Master. I also chose to include all rows from May 2014 Sales Report and only those rows from Sales Person Master that do not match rows in May 2014 Sales Report. With this selection it will help me identify if there are any sales people who did not have sales in May 2014.

The items discussed above are just a few of the key features of Active Data that provide the Excel user with more power in an audit or investigation without busting the technology budget. A quick visit to www.informationactive.com provides a more detailed view of the functions available in this tool.

It would retrieve the data generated and transfer them to excel, but I have a tiny problem with this vi. I really need the vi to place the each value that was retrive into appropriate cells (like a delimiter). So far its retrieving the data and putting all of them into cell A1. can some one help me please??

Your receive string example is not delimited. I am assuming there are delimiters in the string. Do you have the Report Generation Toolkit. It's not as flexible, but it is easier to use than Active X; especially if your starting out and it will do what you need.

I used the excel Easy Table once, but the reason that I am using activeX is that the toolkit is too generic, has too much parameters, and I can modified a VI to as detail as I want using activeX. Correct me if i am wrong, but does the report generation toolkit allow users to open an existing excel file, choose a specific worksheet, and choose a specific cell to writte on? if it does, could you show me?

Yes, it does. They are located under the Excel General pallete for pointing to a specific sheet or workbook. You can use the Excel Easy text to write to a specific cell. I should note that the RGT uses Active X controls internally to do the same thing you are trying to do. Open them up and have a look.

So I have made this VI according to your suggestion and so far its working well except for the delimiter. I am glad that the "end of line" constant works well cause thats one less problem that I have to worry about. but do you have any suggestion in how to apply delimiter to this VI?

In the digital age, data is the lifeblood of any organization. The way you store and analyze your data can significantly impact your success. This is where data warehouses come into the picture. Data warehouses are essential for businesses of all sizes, as they provide a central repository for data from a variety of sources, which can then be used for analysis and reporting. This data can be used to make better business decisions, improve operational efficiency, and identify new opportunities.

But with the myriad of data management options available, how do you choose the right one for your needs? The choice between an active data warehouse and a traditional data warehouse can significantly impact your business intelligence outcomes. This article aims to provide a comprehensive understanding of these two types of data warehouses: active data warehouses vs. traditional data warehouses.

A traditional data warehouse, whether it's an on-premises data warehouse or an enterprise data warehouse, is a repository of historical data that is used for analysis and reporting. The data in a traditional data warehouse is typically loaded in batches, which means that it is updated on a regular basis but not in real-time.

Integrating data into a traditional data warehouse involves a process known as ETL, which stands for Extract, Transform, and Load. This process is crucial for converting raw data from various sources into a format that can be analyzed and used for decision-making.

The first step, extraction, involves pulling data from various sources. These sources can be anything from databases, cloud data storage, data lakes, to big data platforms. SQL (Structured Query Language) is often used in this step to query and retrieve data from these sources, including disparate sources like Amazon Redshift and Google BigQuery.

Once the data is extracted, it undergoes the transformation process. This step involves cleaning, validating, and converting the data into a consistent format that can be used in the data warehouse. This might involve tasks such as removing duplicates, validating data for consistency and accuracy, and converting data types to match the data warehouse schema.

The final step is loading the data into the data warehouse. This involves writing the transformed data into the data warehouse's storage system. Depending on the requirements, this could be a full load, where all the data is written into the warehouse, or an incremental load, where only new or updated data is written.

This process has evolved with the advent of cloud data warehouses and big data, leading to new techniques and tools for data integration. For instance, the ingestion of data into platforms like Amazon Redshift and Google BigQuery has become more streamlined and efficient.

Traditional data warehouse architecture is typically organized into three main tiers: the bottom tier, the middle tier, and the top tier. Each tier has a specific role in the data warehousing process, and together, they form a comprehensive system for storing, analyzing, and accessing data.

A popular approach in designing data warehouse architecture involves storing data in a central warehouse and logically partitioning it into multiple data marts. This method allows for different workloads to be isolated from each other, enhancing performance and simplifying data management. Various data warehouse platforms, such as Snowflake, BigQuery, or Redshift, can be effectively utilized within this architecture.

In a traditional data warehouse architecture, the focus is on structuring data in a way that allows for efficient and effective data analysis. By organizing data into data marts and using a Snowflake architecture, businesses can derive valuable insights from their data, driving better decision-making and strategic planning.

A typical scenario where a traditional data warehouse is utilized is when businesses need to analyze historical data to identify trends and make strategic decisions. This is a common use case that showcases the practicality and effectiveness of traditional data warehouses. They provide a consolidated view of data from various sources, making it easier for businesses to gain insights and make informed decisions.

In summary, while traditional data warehouses can be more cost-effective in certain scenarios, they can also potentially have a higher total cost of ownership due to factors like maintenance and licensing costs. The choice between a traditional and active data warehouse should be guided by a thorough assessment of your business requirements, the volume of data you need to process, and the resources at your disposal. An analysis of these factors can help you determine the most cost effective data warehouse solution.

An active data warehouse, often cloud-based, takes the concept of data warehousing to the next level by supporting real-time data processing. This means that data can be updated in near real-time, providing businesses with the most current information for decision-making. Platforms like Amazon Redshift and Google BigQuery are often used in this context due to their powerful data analytics capabilities and ability to handle data from disparate sources.

Integrating data into an active data warehouse is a dynamic process that involves real-time or near-real-time data processing. Unlike traditional data warehouses, which typically use batch processing, active data warehouses are designed to handle continuous data updates. This allows businesses to make decisions based on the most current data, making active data warehouses ideal for dynamic business environments where conditions change rapidly.

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