Frau mit Kopfhörer am Laptop

Big Data

The advancing digitisation of various areas of life and work in our society is making ever-increasing quantities of data available in a wide variety of forms.

Increasingly powerful and more networked devices such as smartphones, sensors, cameras, machines and servers produce measurement data, log data and process data, while commerce and social media platforms generate records of social interactions, as well as transactions involving goods and finances.

The collection, analysis and evaluation of that data aids better and more detailed understanding of existing business models and the establishment of new, ‘digital’ business models and products. Highly scalable data management forms the indispensable basis for many processes in data sciencemachine learning and artificial intelligence.

Big Data overview

‘Big Data’ is the collective term for the technologies, frameworks and tools that have arisen for this purpose in recent years. What they have in common is that they are scaled horizontally as distributed systems and their runtime properties can thus be comparatively easily adjusted to increasing quantities of data through the addition of further resources. Big Data Systems can process a broad range of data types from a wide variety of sources, both in large batches and in a continuous data stream with low latencies. With these properties, big-data technologies provide the foundation for complex analytical evaluations, scalable, reporting-oriented data platforms, and distributed software systems with an event-based processing paradigm.

Since as long ago as 2009, inovex has been one of the first IT service providers in Germany dealing in depth with big data, and has developed and implemented productive corporate solutions in many projects:

  •     Data lakes, data hubs, data platforms
  •     Intelligent, data-powered services and applications (references: mobile.deREWEArvato)
  •     Data analysis and machine-learning platforms (references: EM²QKOSMoS)
  •     Hybrid data warehouses, virtual data integration (references: ProSiebenSat.1, dmTech, C. H. Beck Verlag)

This means we can support you in all areas: from planning and development to operation of Big Data Systems, using on-premises infrastructure and/or in the cloud.

Big Data Tech Stack
  •     Event streaming platform: Kafka, Confluent
  •     Streaming data processing: Spark Streaming, NiFi, Flink, Storm
  •     Scalable data processing and analytics: Spark, Databricks
  •     Hadoop data platform distributions: MapR and Cloudera
  •     SQL mass data queries: Hive, Phoenix or Drill
  •     NoSQL databases: HBase, Cassandra, Elasticsearch, Druid
  •     Public-cloud (Big) Data Services: Microsoft Azure, Amazon AWS and Google Cloud
  •     Container-based set-up of infrastructure and services: Docker, Kubernetes
  •     Job management, orchestration: AirFlow, Argo, Oozie
  •     Data ingestion: NiFi, Flume, Sqoop
  •     Data governance and cluster security: Ranger, Kerberos, Navigator, Atlas

Technology Partners

Big Data

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.

Case Study

Big Data

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.

READ CASE STUDY

dmTech: Creation of a hybrid BI architecture with Big Data components

The IT subsidiary of dm, the chain of cosmetics and healthcare stores with the biggest sales in Germany, has more than 800 employees focused on digitalising the company’s retail operation.
The emphasis is on the development of innovative solutions: for the online shop and customer and employee apps as well as for the IT infrastructure in the dm stores, distribution centres and headquarters. With a number of innovations, a key role is played by the efficient handling and analysis of large amounts of data in order to produce added benefits from the combination of data sources (Big Data).

READ CASE STUDY

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.

READ CASE STUDY

REWE digital: Agile Data Science for Optimization of the Digital Supply Chain

In optimising the group’s supply chain, inovex has worked with REWE’s IT subsidiary, REWE Digital. This arm of the company is responsible for all the REWE Group’s strategic online activities and aims to become the leading provider of online solutions in all REWE’s associated supermarkets and supply warehouses. These include those distribution warehouses that REWE set up specifically for their home delivery service, as well as the group’s in-store delivery and collection points all over Germany.

READ CASE STUDY

Get in touch!

Florian Wilhelm

Head of Data Science

Blog

Big Data