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The world of medium-sized companies is being stirred at present by one topic, in particular: Industry 4.0. Pressure here is growing enormously because the digitization bus must not be missed if successful interaction on the market is to remain possible in the long term. What most companies do not know, however: Not all internal procedures must be cast overboard in order to remain abreast of the issues of big data and Industry 4.0. It is much more important make use of one's own employees' curiosity and convey the data culture to all departments. This blog post shows how to master the change to digitization step by step.

The claim that "Python is not a typed language" now raises my pulse rate just like "Python is simply a scripting language" which was common many years ago.

One just needs to open a Python console and input 1+"1". The result is not 2 as in PHP, for example, but a TypeError. Python is by all means strongly typed, and also differentiates between mutable and immutable types. Because code in Python is not compiled until run time, the above-mentioned addition error makes itself noticed not during programming, but only during execution.

When a BW system uses an SAP HANA database, it is possible to use what are called „Mixed Scenarios“. In these scenarios you can use SAP HANA tools to combine data modeled in both the BW system and in SAP HANA.

In Longview, after activating the cell input control function, a reference object can be used to block or allow manual inputs into cells as well as the copy & paste function.

With the migration to SAP BW/4 HANA or activation of the BW/4 HANA mode described here, it is no longer possible to use legacy objects. This includes the popular MultiProvider, used to combine different InfoProviders and to create an abstraction layer for a BW query. It can link InfoProvider objects, even if these are only available as navigation attributes in one of the providers and not directly in the InfoProvider fact table. In this example, the country is allocated once directly and once as a customer navigation attribute.

SAP BW/4 HANA ist nun seit genau einem Jahr für Kunden verfügbar. Grund, einmal zurückzublicken und einen Vergleich mit SAP BW on HANA 7.5 anzustreben. Zudem werde ich von vielen Kunden gefragt, was genau der Unterschied zwischen SAP BW/4 HANA und SAP BW on HANA ist. Deshalb möchte ich dies einmal darstellen.

After years of experience of developing ETL applications, I can say that they are generally tested less rigorously than transaction systems.

I’d like to once again delve into the issue of memory usage. Other blogs like “SAP HANA – no more memory? Implement early unload!” have shown the importance of properly configuring SAP BW objects for the intended usage. Briefly:  anything not queried regularly and immediately should be unloaded early to storage. For this you set the “early unload priority” attribute in the HANA database table. Since this setting is not a part of the ABAP transport system (CTS), the developer or operation must always ensure that the configuration is correct. Otherwise, only data needed for staging from the data acquisition layer perform well in RAM, which burdens the system and is unnecessarily expensive.

The Devil in the Detail - What Details Decide on the Effectiveness of Partition Pruning?

In the previous article of this series, a practical and effective approach to using partition pruning was explained in detail. This easy-to-implement method can significantly optimize query times. However, as is often the case, some details need to be taken into account to ensure the efficient and effective use of the presented method. In this regard, we echo Theodor Fontane, who observed as far back as the 19th century that the magic always lies in the details.

In these times of digitalisation is it particularly important to be able to draw on reliable databases in order to eliminate errors at the source and facilitate a focused and precise way of working. The staging area is a solution for this type of challenge in today’s world.

Designers and architects often underestimate the need for a staging area for the database environment as they consider it a waste of space, effort and development time. Developing staging certainly requires space and effort, but this pays off over the whole life cycle of the database.

After outlining the conventional methods for storing historical data in the first post of this blog series, I would like to introduce a more effective approach to partitioning a historical table in this second part.

R is one of the most popular open source programming languages for predictive analytics. One of its upsides is the abundance of modeling choices provided by more than 10000 user-created packages on the Comprehensive R Archive Network (CRAN). On the downside, package-specific syntax choices (which are a much bigger problem in R than in e.g. in Python) impede the employment of new models. The caret package attempts to streamline the process of creating predictive models by providing a uniform interface to various training and prediction functions. Caret’s data preparation- , feature selection- and model tuning functionalities facilitate the process of building and evaluating predictive models. This blog post focuses on model tuning and selection and shows how to tackle common model building challenges with caret.