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This end-to-end and step-by-step quick demo shows you how to connect an existing on-premises data source to a modern cloud data warehouse solution such as Snowflake:
Before HANA, exception aggregations in SAP BW more frequently posed a challenge at runtime, this also applying to the distinct count operation. This operation is used, for example, to obtain a customer count from orders.
Earlier, distinct count operations were often implemented as follows: Â A calculated key figure with a value of 1 was established, and additions then performed via exception aggregation. However, what worked well in environments without HANA currently causes the pushdown to stop functioning. This can prevent calculations from being optimally performed, depending on settings.
A solution is therefore required for HANA environments: Accordingly, let us concentrate next on optimal implementation of distinct count with business warehouse on HANA or BW/4HANA.
In the following article you will get an overview of the different categories of Customer Data Platforms - based on the classification of David Raab from the Customer Data Platform Institute and further developed by the b.telligent experts on the subject of CDP.
German companies must venture a big step toward digital transformation and improvement of customer experience - this insight is evolving increasingly among decision-makers. Â The reasons for this are manifold:
CDP systems, existent in the USA since 2013, appear to be right on cue here. Whereas campaign management systems tending to be based on workflow often automate CRM-based campaign processes in relational data models, CDP systems allow comprehensive treatment ranging from customer-centric data management in big-data technologies to e-mail dispatch in real time, in order to optimize the customer interaction across all digital channels such as the web, e-mail, mobile communications, online shops and social networks.
In an analysis of the causes of the financial crisis of 2008, the European Central Bank (ECB) and many other regulatory authorities discovered, inter alia, major deficiencies in institutional IT and data architecture. As a result of these deficiencies, risk assessments could not be performed sufficiently quickly and accurately, and not completely either. In short: Risks could neither be reported nor controlled to an appropriate extent. In the wake of this lesson, the Basel Committee on Banking Supervision (BCBS) published standard 239, widely known through press releases and also referred to as the "principles for effective risk data aggregation and risk reporting" and AT 4.3.4 of MaRisk.
New data sources and areas of application emerging constantly continue to drive the steady expansion of data storage systems such as DWH, Data Lake and Analytics Platform. Data management processes must also keep pace with growing requirements. Not seldom do small BI applications grow into major initiatives in which several development teams participate. The situation is exacerbated in many industries by the need to make adjustments faster than ever before. This demands short reaction times and high flexibility from the teams and, not least of all, from the infrastructure.
In the last blog, we used a recursive function for a solution with less than 10 lines to scan folders and allow file evaluation by modification date and size.
Now I’m going to raise the bar somewhat for this example by showing even better alternatives.
Recently while working on a project, a colleague asked whether one could list the content of drives in Python. Of course, you can. Moreover, since this isn’t at all complicated, I’d like to take this case to illustrate key best practices recommended for working with paths on drives.
Because (storage) volume and execution time are paid for, the shorter times can reduce costs. A detailed online documentation is available at the following URL: https://docs.snowflake.net/manuals/index.html
Incidentally, one does not have to be an AWS customer to be able to use Snowflake. As a cloud-DB service, Snowflake itself offers no proprietary ETL tools, but leaves this to the manufacturers of ETL, reporting or self-service BI tools. These usually provide native drivers and connections to allow use of their tools with Snowflake. If no separate ETL tool is to be used at the company, there are several possibilities of loading data and realizing the ETL routes. One possibility is implementation of logic in SQL, and orchestration via Python.
Recommendation systems are a crucial part of every digital business model. This blog post concisely answers two foundational questions:
In this article I focus on a solid overview.
A lot of what have hitherto been manual programming tasks can be replaced - or at least greatly simplified - by DWA tools. The precise elements of development that can be automated in that respect can vary greatly from tool to tool. For example there are pure code generator concepts, with the aid of which database structures and ETL/ELT processes can be generated automatically ("design time"). On the other hand, extensive integration suites exist that can generate but also manage the entire DWH lifecycle, from provision of the data in the sources right through to the data marts ("run time").
During the development phase there is a series of tasks in which a DWA tool can provide support. The following deals in particular with the fields of reverse engineering and compatibility, analysis, implementation and framework condition.
Increased use of the Internet as well as smartphones is also increasingly changing consumer behavior. With increasing digitization of channels and touchpoints, customers today are informed better than ever and thus harder to acquire. To register, evaluate and understand consumer behavior digitally too, persons in charge of marketing must today form targeted segments and create an accordingly coordinated strategy for potential customer touchpoints, in order to address customers on the right channel at the right time with the right offer, while nonetheless refraining from inundating them with e-mails or push-messages.
