Big data and
data science

Do you know the three main technology-related challenges
in big data solutions?

Big data and
data science

Do you know the three main technology-related challenges
in big data solutions?

Successful structures for your projects 

The media regularly focus on the applications and success prospects of big data and data science. But what about the underlying technology? What are the crucial considerations when it comes to architectures? Success with Big Data requires new and innovative forms of information processing, including unconventional data management technologies, architectures and analytics. Big Data's potential applications are now extensive and highly individual. But regardless of the respective use case, the same three challenges always arise during implementation:

  • Efficient handling of large volumes of data
  • Fast processing of data streams
  • Mastery of complex analyses

That’s why we have developed implementation solutions for big data projects that address these three challenges. This is what they enable you to do:

  • STORE & PROCESS VERY LARGE DATA SETS
  • CAPTURE & PROCESS FAST DATA STREAMS
  • ADVANCED ANALYTICS & DATA LAB
We are experts in both traditional and new big data technologies, as well as complex analytics. That’s a decisive advantage for you.
 

Big Data Canvas

Using the Big Data Canvas architecture approach, we map the entire scope of a Big Data solution with seven roughly granular architecture modules. Each of these building blocks represents a functional area for analytical information processing. This is first broken down by fine-granular Achitecture Building Blocks and finally deposited by corresponding Solution Building Blocks. With this solution approach, highly standardized and integrated big data architectures can be implemented for each of your individual business cases.


These are your advantages with Big Data Canvas:

  • STANDARDISED ARCHITECTURE
  • PROVEN SOLUTION COMPONENTS
  • INVESTMENT SECURITY
 
Store & process very large data sets – enterprise data hub
Our additional technological recommendation for you is: Enterprise Data Hub. This is the central location for all company-relevant data in a comprehensive Operational Data Store (ODS). All data is available in its original state, continuously collected and stored at the highest level of detail. All this is extremely cost-effective and paired with sufficient computing power for analyses. This is done directly on the data hub platform or as a central source for all other IT systems. Consistent backup of all historical data allows detailed data to be fully accessed even years later or error analyses to be carried out with unprecedented reliability.

Comprehensive data histories - big data lambda architecture
The main problem with historical data is not their volume, but rather their heterogeneous nature. Structure and significance are constantly changing. The big data lambda architecture provides a uniform view of historical data – regardless of structure. It also ensures that new structural changes never prevent data from being recorded. Nothing is lost.
What you can achieve with the enterprise data hub:

  • STANDARDISED ARCHITECTURE
  • ACCESS TO HISTORICAL DATA AT ALL TIMES
  • EQUIPPED FOR THE ANALYSIS OPTIONS OF THE FUTURE

 

Fast data streams – real-time analytics
It is becomingly increasingly crucial to be able to analyze data streams in real-time, not only from social media sources, but also, and more importantly, from sensor and telematics systems. However, the parallelism of multiple simultaneous data streams and the frequently changing and uncontrollable data rates represent major challenges in processing such data. Our approach is based on a flexible lambda architecture, which we configure to accommodate your requirements and implement using proven Hadoop ecosystem technology components.

The architecture is crucial
The architecture is a fundamental success factor in your project. The lambda architecture we prefer is fully equipped to cope with the major challenges associated with processing real-time data streams. It can be scaled to process any number of data streams and transmitted data sets. Components can be replicated to boost the system's performance. Processing components for real-time analyses can be incorporated in the data stream.
Your advantages at a glance:

  • STANDARDIZED ARCHITECTURE
  • SCALABLE SOLUTION
  • READY FOR REAL-TIME ANALYTICS
 
Data Lab – advanced analytics in a sandbox
Power users, BI analysts or data scientists need more than just access to the data warehouse and a BI tool. They also need powerful analysis platforms which allow heterogeneous data from different sources to be analyzed in self-service mode. Good news for you! We can design and build the perfect workplace for your exploratory analyses and deliver new insights to your experts without weeks-long lead times.
 
All self-access
Depending on your requirements, we link existing DWH data with other information sources such as social media, log files, sensor data or documents. The right technological approach enables the system as a whole to deliver best-possible performance. Whether different systems are connected virtually or data are physically integrated at a suitable location depends on your performance and data quality requirements. We apply our technical and professional expertise to develop a solution that represents the best value for your investment.
For comprehensive data analysis you have the following options available to you:

  • UNIFIED ANALYSIS
  • ADVANCED ANALYTICS
  • SEMANTIC WEB
 
 

Do you have questions or need help with your project?

We are here to help you.