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Data Engineering

Data engineering forms the basis for sustainable success in digitization – from data analysis to the use of AI.

A solid data infrastructure is the key to future-oriented business development. It facilitates everything from the creation of new value chains and well-founded decision-making using business intelligence to product personalisation and the use of artificial intelligence.

Data Engineering for Success

Opportunities for collecting data have increased rapidly in recent years. Almost any interface with a customer, a machine, or a product is also capable of capturing data. Maintaining an overview in this ocean of information requires concepts and competencies from a wide variety of areas that go beyond traditional software engineering.

To this end, therefore, we provide more than just the data streaming architecture. Organising, provisioning, managing, and integrating huge volumes of data requires intelligent data engineering which also takes into account testing, security, monitoring, and data quality.

To make our data engineering projects even more successful, we employ best practices from software engineering. This results in higher quality, robust data products which form the cornerstones of sustainable success.

Designing and developing data lakes

As with traditional software development, the success of a data lake project rests on the design of its architecture. The right design will ensure that the data platform is both stable and flexible enough to handle new applications in areas such as reporting or machine learning, while ensuring that operating costs remain low.

We provide our customers with cleanly designed architectures which enable them to meet even major challenges – such as those posed by the EU General Data Protection Regulation (GDPR) – and we select technologies based purely on our customers’ needs. In most cases, we support our customers throughout the entire process, from the initial concept right through to implementation and further development.

Cloud migrations

We gained early experience in the field of big data, and we are particularly experienced in setting up, designing, and maintaining on-premises Hadoop distributions. We therefore understand exactly what is involved in a cloud migration and can evaluate the advantages and disadvantages of each case both individually and comprehensively.

During a migration, we ensure that both the infrastructure and the data systems and use cases are implemented cloud natively in order to fully leverage the advantages of the cloud environment. We believe that our customers’ working methods should be altered as little as possible by their cloud migration, despite the addition of new technologies.

Data-protection-compliant processing

The GDPR is an important factor in the development of new solutions. Not only does it usher in new technical requirements, but it can also mean severe penalties for companies which violate them.

This has a tremendous impact on both existing and new data platforms, as it affects everything from regulation-compliant data storage and provision to the applicable authorisation concepts and documentation.

If these requirements are not taken into account during the design phase, the subsequent conversion process is both time-consuming and expensive. When designing our projects, therefore, we determine exactly which GDPR provisions must be applied and decide how to incorporate them into each individual solution.

Developing new, data-driven services

A high-performance data platform is the basis for successfully extracting value from data. It enables teams to develop new products by allowing them to flexibly access information and to expand it as they see fit. Using a common platform also facilitates the development of symbiotic cross-team relationships, which can provide companies with new insights.

We support customer teams by providing continuous quality monitoring, enabling them to set up their platforms so that they can work successfully and load their data reliably.

We also enable companies to flexibly evaluate their data by giving them access to their data lakes through traditional reporting solutions. This enables analysts to use the data to create reports and evaluations.

Traditional reporting, however, is not the only business tool which can benefit from the creation of centralised data platforms. Complex machine-learning products can also be used to mine data lakes containing unstructured data, such as images.

Specialized Topics

Our Data Engineering Focus
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Big data

The collection, analysis, and evaluation of big data help us to understand existing business models better and in greater detail, to digitise them, and to establish new digital products. Highly scalable data management solutions form an indispensable foundation for many of the processes associated with data science, machine learning, and artificial intelligence.

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Business Intelligence

Companies can use performance-oriented BI solutions to analyse, evaluate, and visualize their business data independently and in real time. They can then use this aggregated information to draw solid conclusions and determine specific options for action.

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Cloud infrastructures

A solid cloud infrastructure is an important factor in analysing and processing data in scalable and cost-efficient manner. While traditional data centres require a higher level of manual effort, cloud solutions can be more flexibly adapted to changing needs. Building on this, additional automation steps ensure that we can now implement cloud infrastructures that are precisely tailored to the requirements of the digital transformation.

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Data science

Data science goes hand in hand with data engineering. Once data has been collected and processed, the next step is to leverage its benefits. Data-driven models ensure that the information gathered makes its way into production.

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Case Studies

dm-drogerie markt: Development of a Fully Automated Data Centre for the Online Shop

When a major brand like dm strategically enters the online market, it creates high expectations. For this reason, their IT subsidiary FILIADATA, which has been responsible for all dm’s IT systems since 1988, launched a project in 2013 which was aimed at laying the foundations for reliable, fail-safe IT operations for dm.de. These included creating a large, fully automated Linux infrastructure in the data centre on which sophisticated web services like the online shop can be operated.

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REWE digital: Demand Forecasting for REWE’s Delivery Service

inovex and REWE’s collaboration in the area of supply chain optimisation has focused particularly intensively on REWE’s IT subsidiary, REWE digital.
This particular project involved developing demand forecasting for REWE’s delivery service, leveraging big data technologies to enable easy scalability.

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mobile.de: Use Cases for Online Portal Recommendations Using Data Products

mobile.de is a marketplace for the buying and selling of vehicles. Every month, the web platform draws 13.5 million visitors who can choose from the more than 1.6 million vehicles on offer. Each visit to the platform creates a stream of data which contains information about the demand for particular vehicles, the quality of the vehicles for sale, and user requirements. mobile.de wants to use this data to continuously improve the user experience for both vehicle sellers and purchasers.

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Arvato Bertelsmann: Optimised Fraud Detection in Microsoft Azure

arvato Financial Solutions is collaborating with Microsoft, Cloud and Big Data specialist inovex GmbH, and three pilot e-commerce customers on an innovation project to create a Big Data architecture based on Microsoft Azure. The team will use the project to evaluate how the combination of Cloud Computing, Big Data and advanced analytics can improve fraud prevention and facilitate the development of new financial BPO services.

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Technology Partners

Cloudera

As a certified Cloudera and Hortonworks partner, we support (after the merger of the companies) our customers with the Cloudera Data Platform – a solution capable of acquiring, storing, processing and analysing very large volumes of data. Cloudera is a state-of-the-art platform for Data Management and Analysis, Machine Learning and Artificial Intelligence.

Confluent

Confluent was founded by the team who developed the Apache Kafka™ distributed streaming platform for LinkedIn, scaling it to receive, process and store over 1 trillion messages per day. Kafka boasts a particularly impressive processing speed and provides connectors for data integration, as well as a framework for stream processing.

databricks

The mission of our partner Databricks is to accelerate innovation for all customers by unifying Data Science, Data Engineering and Business Intelligence in one solution.

Amazon Web Services (AWS)

Amazon Web Services (AWS) is a secure platform for cloud services. It is designed to support the growth of your company by providing computing performance, database storage, content delivery and other functions. As an AWS Certified Solutions Achitect Associate, we can draw on a wide range of tools, training courses, and support in order to develop your AWS solutions more efficiently.

Google Cloud

Google Cloud Platform offers a variety of services that enable companies to operate and process their systems and data on a modern, highly scalable and proven infrastructure.

Microsoft

We are a certified Microsoft Partner: Certified Gold Partner for Cloud Platform, Data Platform and Data Analytics, and Certified Silver Partner for Application Development.

Research Projects

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„KOSMoS“ project

The aim of the KOSMoS project, which is funded by the Federal Ministry of Research and Education, is to connect manufacturing companies with one another, thereby creating a secure, digital value network that transcends company boundaries. Within the consortium of nine project partners, inovex is the expert in Data Management and Analytics.

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EM²Q expert system

The innovative technology of imaging mass spectrometry is about to complete its move from its primary application in research to commercial use in the clinical environment.

Get in touch!

Florian Wilhelm

Head of Data Science, Contact for Data Management & Analytics