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• Introducing the BigQuery & Data Warehouse Export feature

  30 janvier, par Matomo Core Team

  Matomo is built on a simple truth : your data belongs to you, and you should have complete control over it. That’s why we’re excited to launch our new BigQuery & Data Warehouse Export feature for Matomo Cloud, giving you even more ways to work with your analytics data. 

  Until now, getting raw data from Matomo Cloud required APIs and custom scripts, or waiting for engineering help.  

  Our new BigQuery & Data Warehouse Export feature removes those barriers. You can now access your raw, unaggregated data and schedule regular exports straight to your data warehouse. 

  The feature works with all major data warehouses including (but not limited to) : 

  • Google BigQuery 
  • Amazon Redshift 
  • Snowflake 
  • Azure Synapse Analytics 
  • Apache Hive 
  • Teradata 

  You can schedule exports, combine your Matomo data with other data sources in your data warehouse, and easily query data with SQL-like queries. 

  Direct raw data access for greater data portability 

  Waiting for engineering support can delay your work. Managing API connections and writing scripts can be time-consuming. This keeps you from focusing on what you do best—analysing data. 

  

  With the BigQuery & Data Warehouse Export feature, you get direct access to your raw Matomo data without the technical setup. So, you can spend more time analysing data and finding insights that matter. 

  Bringing your data together 

  Answering business questions often requires data from multiple sources. A single customer interaction might span your CRM, web analytics, sales systems, and more. Piecing this data together manually is time-consuming—what starts as a seemingly simple question from stakeholders can turn into hours of work collecting and comparing data across different tools. 

  This feature lets you combine your Matomo data with data from other business systems in your data warehouse. Instead of switching between tools or manually comparing spreadsheets, you can analyse all your data in one place to better understand how customers interact with your business. 

  Easy, custom analysis with SQL-like queries 

  Standard, pre-built reports often don’t address the specific, detailed questions that analysts need to answer.  

  When you use the BigQuery & Data Warehouse Export feature, you can use SQL-like queries in your data warehouse to do detailed, customised analysis. This flexibility allows you to explore your data in depth and uncover specific insights that aren’t possible with pre-built reports. 

  

  Here is an example of how you might use SQL-like query to compare the behaviours of paying vs. non-paying users : 

  				
                                          <xmp>SELECT  
  
  
  
  custom_dimension_value AS user_type, -- Assuming 'user_type' is stored in a custom dimension 
  
  
  
  COUNT(*) AS total_visits,  
  
  
  
  AVG(visit_total_time) AS avg_duration, 
  
  
  
  SUM(conversion.revenue) AS total_spent  
  
  
  
  FROM  
  
  
  
  `your_project.your_dataset.matomo_log_visit` AS visit 
  
  
  
  LEFT JOIN  
  
  
  
  `your_project.your_dataset.matomo_log_conversion` AS conversion  
  
  
  
  ON  
  
  
  
  visit.idvisit = conversion.idvisit  
  
  
  
  GROUP BY  
  
  
  
  custom_dimension_value; </xmp>
  
                                  
  			

  This query helps you compare metrics such as the number of visits, average session duration, and total amount spent between paying and non-paying users. It provides a full view of behavioural differences between these groups. 

  Advanced data manipulation and visualisation 

  When you need to create detailed reports or dive deep into data analysis, working within the constraints of a fixed user interface (UI) can limit your ability to draw insights. 

  Exporting your Matomo data to a data warehouse like BigQuery provides greater flexibility for in-depth manipulation and advanced visualisations, enabling you to uncover deeper insights and tailor your reports more effectively. 

  Getting started 

  To set up data warehouse exports in your Matomo : 

  1. Go to System Admin (cog icon in the top right corner) 
  2. Select ‘Export’ from the left-hand menu 
  3. Choose ‘BigQuery & Data Warehouse’ 

  You’ll find detailed instructions in our data warehouse exports guide.  

  Please note, enabling this feature will cost an additional 10% of your current subscription. You can view the exact cost by following the steps above. 

  New to Matomo ? Start your 21-day free trial now (no credit card required), or request a demo. 

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• How to reduce fps to reduce filesize using ffmpeg ?

  10 septembre 2024, par Asas

  I recorded the video in waaaay to high of fps and its like over 100 gb now, i dont want any quality degradation just to lower the fps to a reasonable 60 but when i googled i got the result which converted my video but the size is the same for some reason
I tried

  


  ffmpeg -y -i input_video.mp4 -c copy -f h264 output_raw_bitstream.h264
ffmpeg -y -i input_video.mp4 -vn -acodec copy output_audio.aac
ffmpeg -y -r 24 -i output_raw_bitstream.h264 -i output_audio.aac -c copy output.mp4


  


  but replaced numbers and names for what is in my case but it outputs the same 100 plus filesize

  


• Different filesizes for images generated using octave and python

  22 février 2017, par Lakshay Garg

  I am using python (scikit-image) and octave to generate 200 images as follows

  Python3

  import numpy as np
  
  from skimage.io import imsave
  
  
  
  images = [255*np.ones((100,100), dtype=np.uint8),  # white
  
               np.zeros((100,100), dtype=np.uint8)]  # black
  
  
  
  for i in range(200): # save alternating black and white images
  
      imsave('%04d.png'%(i+1), images[i%2])

  Octave

  pkg load image;
  
  
  
  im1 = 255*ones(100,100); # white
  
  im2 = zeros(100,100);    # black
  
  for i=1:200
  
      name = sprintf('%04d.png', i);
  
      if mod(i,2) == 0
  
          imwrite(im1, name);
  
      else
  
          imwrite(im2, name);
  
      end
  
  end

  Next, I use ffmpeg to generate two videos (alternating white and black frames) from these two sets of images using the following command

  ffmpeg -r 10 -loglevel quiet \
  
         -i ./%04d.png -c:v libx264 \
  
         -preset ultrafast -crf 0 ./out.mkv

  1. Sizes of image files generated by both these codes are different.

     • Octave white : 192 bytes, black : 98 bytes
     • Python white : 120 bytes, black : 90 bytes

  2. Sizes of video files generated from these octave and python images are significantly different from each other.

     • Octave filesize : 60 kilobytes
     • Python filesize : 116 kilobytes

  Why do we have this apparently very strange behavior ?

  EDIT

  Since it was suggested that the behavior might be dues to octave and python using different bit-depths to store the images, I changes the octave code to use 8 bit numbers

  im1 = uint8(255*ones(100,100)); # white
  
  im2 = uint8(zeros(100,100));    # black

  and now the image file sizes are nearly the same

  • Octave white : 118 bytes, black : 90 bytes
  • Python white : 120 bytes, black : 90 bytes

  but the problem is still the same for video files, octave : 60K, python : 116K