Monat: März 2023 (Page 1 of 2)

How Heat Maps Can Help You Find the Big Fish in Your Data Pond

A heat map is a graphical representation of data in which different values are represented by different colors. In a BI dashboard, a heat map can be used to visualize patterns and trends in large datasets, making it easier for users to quickly identify areas of interest and focus their attention where it’s needed most.

Some use-cases for heat maps in BI dashboards include:

  1. Sales Analysis: A heat map can be used to show the distribution of sales across different regions or territories. This can help sales teams identify areas where they need to focus their efforts in order to improve performance.
  2. Customer Behavior Analysis: A heat map can be used to show the areas of a website or application where users are spending the most time. This can help designers and product managers identify areas of the user experience that need improvement.
  3. Risk Management: A heat map can be used to visualize potential risks and their likelihood of occurrence. This can help organizations identify and prioritize risks in order to develop effective risk management strategies.

Some good practices for using heat maps in BI dashboards include:

  1. Keep it Simple: Heat maps can quickly become overwhelming if there are too many data points or if the colors are too bright or contrasting. Stick to a simple color scheme and limit the number of data points in order to keep the visualization clear and easy to read.
  2. Use Color Effectively: Choose colors that are easy to differentiate and that have meaning. For example, green might represent positive values, while red might represent negative values. Make sure that the color scheme is intuitive and easy to understand.
  3. Provide Context: Heat maps are most useful when they are accompanied by other data or visualizations that provide context. For example, a heat map showing sales by region might be more useful if it’s also accompanied by a chart showing the overall sales trends over time.

Introduction to Heat Maps (

Exploring the Power of Bubble Charts with Hans Rosling


If you’re looking to explore the power of bubble charts, Hans Rosling is the man to turn to. A Swedish statistician and public health professor, Rosling is known for his groundbreaking work in data visualization. He was the first to use bubble charts to illustrate the relationship between health and wealth in the world. Through his work, Rosling has been able to make complex data more accessible and understandable. With his help, you can learn how to create your own bubble charts and use them to explore the power of data visualization.

Hans Rosling was a Swedish physician, statistician, and public speaker who was known for his creative use of data visualization to explain complex global issues. He was a pioneer in the use of bubble charts to illustrate data in a more visually appealing way. Bubble charts are a type of chart that uses circles to represent data points, with the size of the circle representing the magnitude of the data point.

Rosling used bubble charts to show the progress of countries over time, such as the change in life expectancy and the GDP of countries. He used this type of chart to demonstrate how the world has changed and to show the differences between countries. His work has been credited with helping to bring data visualization to the mainstream and to make it more accessible to the public. Rosling’s work has been an inspiration to many, and his use of bubble charts has been adopted by many other data visualizers. His work has helped to make data more accessible and easier to understand, and has been a great contribution to the field of data visualization. His legacy will continue to inspire and inform data visualizers for years to come.


I recently watched Hans Rosling’s TED Talk on the power of bubble charts. It was amazing to see how he used data to tell a story and how he used visuals to make it easier to understand. I’m inspired to use bubble charts to explore data in my own work.

ALERTs on Oracle 19.17 at RHEL 8

After a new installation of an Oracle 19 CDB+PDB database we always look at the alert.log.

And there were 2 anomalies:

1: ORA-00800:

ORA-00800: soft external error, arguments: [Set Priority Failed], [VKTM], [Check traces and OS configuration], [Check Oracle document and MOS notes], [] Error attempting to elevate VKTM’s priority: no further priority changes will be attempted for this process


So the customer had to open a ticket with RHEL:

The latter already knew the problem:

  • Removing any software which enabled CPUQuota and/or CPUaccounting on systemd level (usually Insights), or remove the CPUQuota and accounting directives from systemd files.
    This solves ORA-00800 error without need of changing cpu.rt_runtime_us.
  • Why does Insights prevent Oracle grid services from starting
  • Oracle Database Enterprise 19c fails to start with error: ORA-00800: soft external error, arguments: [Set Priority Failed], [VKTM]

2: Prallel FPTR failed:

PDB(3):Undo initialization recovery: Parallel FPTR failed: start:28373 end:28380 diff:7 ms (0.0 seconds)



The default value for _min_undosegs_for_parallel_fptr is 100, which you can change to 0. This problem appears to be documented in the note below and fix included in oracle 20.0 version.

Bug 30159581 – A DB open hangs after switchover due to a detected deadlock ( Doc ID 30159581.8 )

Please implement work around solution as discussed in the ( Doc ID 30159581.8 )

Oracle DBA Life


The end of the Oracle DB – Links?

Database links in Autonomous Database Shared are the past – Cloud links are the future


Database links have been a popular way to let remote databases access specific tables or views in a database, but they require a lot of back and forth communication to establish the connection. However, with Oracle Autonomous Database Shared’s new feature, Cloud Links, data owners can easily register a table or view for remote access by a certain audience, without needing to set up a connection every time someone needs to access the data. This means that anyone with remote access can discover and use the data that has been made available to them without any extra hassle.

select .. from <namespace>.<name>@cloud$link;—cloud-links-are-the-future

We like this Solution


Voting behavior with Sankey diagrams

Sankey diagrams can be useful in analyzing voter behavior in elections. They can visually represent the flow of voters between different parties or candidates, as well as the factors that influence their decision-making.

For example, a Sankey diagram could show how many voters switched from one party to another between two elections, and what issues or events may have influenced their decision. It could also show how many voters abstained from voting altogether or how many new voters entered the electorate.

By analyzing the flow of voters through a Sankey diagram, political analysts can gain insights into the dynamics of an election and better understand the factors that determine electoral outcomes. They can also use Sankey diagrams to compare different elections or to track changes in voter behavior over time.

Overall, Sankey diagrams can be a valuable tool for anyone interested in understanding the complex and ever-changing landscape of electoral politics.

d3.js example

Create a Sankey Diagram Visualization (

Making Sense of Box Plots: A Beginner’s Guide to Interpreting Data


Are you feeling overwhelmed by box plots? If so, you’re not alone! Interpreting data can be a daunting task, but it doesn’t have to be. With this beginner’s guide to making sense of box plots, you’ll be able to make sense of data in no time. This guide will provide you with the tools you need to understand box plots and interpret data quickly and easily. So don’t worry, you’ll be a box plot pro in no time!

Making sense of box plots can be a daunting task for beginners. But with a little bit of practice, you can learn to interpret data quickly and accurately.

Box plots are a great way to visualize data, and can help you make decisions about how to proceed with your project.

The first step is to understand the different components of a box plot. The box itself represents the middle 50% of the data, while the whiskers represent the rest of the data. The line in the middle of the box is the median, which is the middle value of the data set. The dots outside the box are outliers, which are values that are significantly higher or lower than the rest of the data.  Once you understand the components of a box plot, you can start to interpret the data.

To interpret a box plot, look at the size of the box and the position of the median. If the box is large, it means that the data is spread out, and if the median is close to the center of the box, it means that the data is evenly. If the box small and the median close to one side, it means that the data is clustered around one value.

You can also look at the outliers to see if there are any extreme values that could be affecting the data. With a little practice, you’ll be able to make sense of box plots quickly and accurately.

Introduction to Box Plot Visualizations (

Box plots in JavaScript (

Gantt Charts: A Must-Have Tool for Every Project Manager


As a project manager, I know how important it is to stay organized and on top of things. Gantt charts are a must-have tool for any project manager who wants to keep their projects running smoothly. Gantt charts provide a visual representation of a project timeline, making it easy to see which tasks need to be completed and when. With Gantt charts, you can easily identify potential problems and make adjustments to ensure your project is completed on time and within budget. Plus, Gantt charts are easy to use and don’t require any special software or training. So if you’re looking for a simple yet effective way to manage your projects, Gantt charts are the way to go.

Gantt charts are an essential tool for every project manager. They provide a visual representation of the timeline of a project, allowing project managers to easily track progress and identify any potential issues. With a Gantt chart, project managers can quickly see what tasks need to be completed and when, and can easily adjust the timeline as needed.

Gantt charts are also great for keeping stakeholders informed about the progress of a project. They provide a clear picture of the timeline, which makes it easy for stakeholders to understand where the project is at and what needs to be done. This helps to ensure that everyone is on the same page and that the project is running smoothly.

Overall, Gantt charts are an invaluable tool for project managers. They provide a clear and concise timeline of the project, making it easy to track progress and identify any potential issues. They also help to keep stakeholders informed, which is essential for successful project management. If you’re a project manager, Gantt charts are a must-have tool for your toolbox.


Gantt charts are a must-have tool for every project manager. They help you plan and track the progress of your project, and make sure that you stay on track. They’re easy to use and understand, and they can save you a lot of time and hassle. So if you’re a project manager, make sure you get yourself a Gantt chart!

How Line Charts Helped Me Visualize Data Better


As someone who loves working with data, I’ve always been a fan of visualizing it in a way that makes it easier to understand. Recently, I started using line charts to do just that and it has made a huge difference in how I understand the data I’m working with. Line charts are a great way to visualize data because they provide a clear and concise picture of how a certain set of data points are related. They also allow me to easily identify trends and changes over time. By using line charts, I’m able to better understand the data I’m working with and make more informed decisions.

The Power of Line Charts for Data Analysis

When it comes to data analysis, line charts are a powerful tool. They can be used to track trends over time, compare different sets of data, and identify patterns. Line charts are easy to read and interpret, making them a great choice for visualizing data.

Line charts are a type of graph that plots data points along a line. Each data point is connected by a line, making it easy to see the overall trend. The x-axis of the graph is usually the independent variable, while the y-axis is the dependent variable. This means that the x-axis is usually a timeline, while the y-axis is the value of the data being tracked.

Line charts are a great way to visualize data because they make it easy to identify patterns and trends. For example, if you’re tracking sales over time, a line chart can quickly show you whether sales are increasing or decreasing. It can also show you how sales are changing from month to month.

Line charts can also be used to compare different sets of data. For example, if you’re tracking sales for two different products, you can use a line chart to compare the performance of each product over time. This can help you identify which product is performing better and why.

Line charts are also useful for forecasting. By looking at the trend of the data, you can make predictions about what will happen in the future. This can be especially helpful for businesses that need to plan ahead.

Overall, line charts are a great tool for data analysis. They make it easy to track trends, compare different sets of data, and make predictions. If you’re looking for a way to visualize your data, line charts are a great choice.



I’m a data enthusiast, and I’ve found that line charts are a great way to visualize my data. They help me see trends and patterns more clearly, and make it easier to understand the information I’m looking at. Line charts are my go-to for data visualization, and I’m grateful for how much they’ve helped me better understand my data.

Line Chart (

The Bar Chart

There is an amusing anecdote about a bar chart depicting the pros and cons of coffee versus tea. One day, two colleagues met in the coffee kitchen and noticed the chart on the wall. One colleague was an inveterate coffee drinker, while the other preferred tea.

When the coffee drinker looked at the chart and saw that coffee had more benefits than tea, he began to smile triumphantly and said, „See, I told you coffee was better than tea!“ The tea drinker, however, not a fan of statistics and charts, just shook his head and replied, „That may be, but I’ll stick with tea anyway.“

The anecdote illustrates that while bar charts are a useful and informative way to present data, ultimately the decision of what to consume or do depends on personal tastes and preferences. Nevertheless, the bar chart remains one of the most important chart types to the human eye because it provides a clear and quick visualization of data and is easy to understand, even for people without extensive knowledge of statistics.

Learn more about Bar Charts (

Oracle APEX App Multi-Tenant Capable

How do I turn a single-user app into a multi-tenant app?



Tom, I want to use your app, but only with my data!


There are a few ways to achieve this (here only 2):

  1. Clone the app in a workspace and mount it on its own Oracle schema, create all the necessary objects (which you can now define right inside Oracle Apex with the install scripts).

    not all sideeffects:
  • Update of the app means more complex app lifecycle management.
  • Maybe other processes have to be adapted which also write to the new database or schema.
  • There might be common data that should be used by all

2. Don’t clone the app and change the data model and customize the APP

not all side effects:

  • This means you have to filter in the Apex App depending on APP_USER or client of a user.
  • Depending on how the data is connected to Regions, IG, IR etc., you might have to make an adjustment in each page.

to point 2:

Here we can proceed very effectively because we can use the option of Oracle VPD-Virtual Private Database. We do not need to customize a single view or Apex SQL + PL/SQL query and that is really a huge advantage.

What was to be done?

In my case I had to extend the APP_USER in the relevant tables (of course not in attached views), I call the column MANDANT:


Next I had to tell the asynchronous load process to assign the data to the user. That means the DML (Insert , Update, Delete) in this table had to be extended by the column MANDANT. It would also have been possible to use a DML trigger that could assign the data correctly. But this was not an „easier“ option for me so I decided to customize the loading process.

As soon as the data of the other client was in the database, I saw my data aggregated with his in the Apex APP. For example, in this specific case, my car drove 133km instead of 100km that day.

Now the Oracle VPD comes into play:

First, create a function that allows to manage more complex criteria in one place: Here I restrict that the check only takes place if the session is in the context of the Apex application 100.

The APP_USER is my client. Oracle Apex automatically sets the APP_USER to the session context after login. Nice…

create or replace function get_mandant_criteria 
 object_schema IN VARCHAR2, 
 object_name VARCHAR2
return varchar2 DETERMINISTIC as
l_criteria varchar2(4000);
  -- Only apply VPD to specific APEX applications
  if sys_context('APEX$SESSION', 'APP_ID') in (100)
   l_criteria:=q'#(MANDANT=sys_context('APEX$SESSION', 'APP_USER'))#';
  end if;

  return l_criteria;
end get_mandant_criteria;

The function must of course also be used in the VPD therefore.
Using Oracle Virtual Private Database to Control Data Access

    ,object_name=>'CARLOG_DATA' -- für jede Tabelle anlegen wo gebr.

And already APEX App 100 is filtered to APP_USER client from login time.


On the subject of multi-tenant and more complex security concepts such as row-level and column-level security, we recommend our SCURTY!
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