Turn Data
into Business Value

Data Engineering

Benefit from solutions around Realtime Data Analytics, Enterprise Search and Big Data

Big Data originated new technologies and approaches for gathering, managing and processing large amounts of data in different formats, shapes and sizes. Data is the currency of the future is a statement that you hear everywhere these days. It's not easy to know how to take advantage of this currency, but you also don't need to be a scientist or explorer to get there. The goal of data engineering is to turn the data that is generated every second by your customers, employees, partners and systems into knowledge and value for your organization. The outcome of this process can be highly business relevant and have impact on your customer's experience, your company financials, your ideal got-to-market strategy, business model and, as a result, your market position against your competitors.

The practical implementation of use cases for data engineering approaches is at the beginning of the beginning and far away from reaching its full potential. But it is already possible to gain practical value and insights for almost every industry and type of business. Some exemplary uses cases are:

  • Early detection of anomalies for fraud and error prevention in the banking and finance industry
  • Better customer targeting based on behavior and interest for ecommerce, retail companies and all kinds of product and service providers
  • Predictive Maintenance in manufacturing and everywhere else machines are used that require service or need replacement parts
  • Linking of medical related patient information to improve treatments or medication procedures
  • And many more in all kinds of industries and business types

How mimacom Data Engineering Works for you

  1. Big Data analysis of large amounts of structured and unstructured data or data streams. This can be done with a search engine or real-time data analytics. The answers generated in this step are: What data is it and can it be classified?
  2. Is it supposed to be there in the way it is? Anomaly detection algorithms are applied.
  3. Recognize the amount, size and quantity and apply a regression algorithm
  4. Are there any patterns with other data found or looking for? This step is called clustering.
  5. In the last step, decisions can be made: What to do with finding and analysis? Advanced systems are even able to learn automatically based on their findings (machine learning)

mimacom Services for Big Data, Enterprise Search and Realtime Data Analytics

  • We provide consulting, development and implementation, and training on big data and data engineering projects
  • We are experts and first hour users of the ELK-stack
  • We use the leading open source solution Elasticsearch for big data and enterprise search engine functionality
  • For data collection we use Logstash
  • For visualization of data and results, we use Kibana
  • Confluent Kafka is integrated for real-time analytics of data streams and to trigger messages on data based events
  • mimacom is a partner of Pivotal. We use Cloudfoundry for efficient management of large big data volumes.
  • We have big data experts and data engineering consultants with dedicated professional expertise in implementing big data models and data analysis at our 13 global locations near you


mimacom USA Inc.

17505 West Catawba Ave, Suite 290
Cornelius, NC 28031

+1 (704) 997-8468

mimacom asia Pte Ltd.

1 Scotts Road #21-10, Shaw Centre
Singapore, 228208

+65 316 391 77