Data Engineering

Your Questions. Answered.


Businesses depend on data to be productive and effective.

Data Engineering ensures data pipelines are created whereas data is ingested, processed, stored, and moved around efficiently. Having a data pipeline in place a business can operate with optimum speed, agility and insight.

At ZettaScale we have extensive experience across Data Engineering, DevOps, cloud, distributed systems, big data and security. We also work with data processing technologies including data extraction, data preparation, machine learning, spatial temporal queries, graph and artificial intelligence. Whether you need design advice or are planning a move to distributed big data services, we can help.


When it comes to Data Engineering, we’ve worked with clients and projects of all sizes and configurations. This has included building real-time streaming platforms, batch processing systems, automated machine learning environments.

We have helped our clients with data engineering in the following ways through our discovery and delivery services:

  • Data-focused architectural reviews – Apache Spark data pipeline for streaming and batch processes
  • Data-driven proof-of-solutions and full-scale delivery by using Hadoop ecosystem (HDFS, Hive)
  • Design and implement spatial temporal system enabling massive geolocation intelligence: Apache Spark, Kafka, Geomsa, Uber H3
  • Design and development of data streaming pipelines with Kafka and Spark
  • Cloud-native and data-driven integrations – Kafka, event-driven architectures and real-time data streaming
  • Model knowledge graph data and graph querying - Arangodb and Neo4J
  • Integrating machine learning and data analysis models into pipelines
  • Discovering patterns in large data sets
  • Calibrating and integrating machine learning models into data pipelines
  • A large-scale data platform to support the training of machine learning models
  • Providing domain expertise Apache Spark, Kafka, Hadoop