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Data Engineering and Machine Learning Operations are tightly coupled areas of expertise that are an integral part of OpsGuru’s core competencies.

We support companies by helping them to create exceptional products and equip them to make informed business decisions by harnessing the value of their data.

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Real-time Analytics

Stream processing enables organizations to gain real-time insights and respond to real-time events and/or trends efficiently. Typical use cases for stream processing include IoT, stock markets, log collection and analysis, transaction fraud detection and more.


Our Big Data experts have built dozens of robust, scalable, secure and cost-efficient ingestion and real-time stream processing pipelines for the most demanding environments and SLA’s.

Our team has helped web-scale companies efficiently, whilst processing millions of events per second leveraging open source projects such as Spark streaming, Flink and Kafka; as well as vendor solutions such as Amazon Kinesis and Google DataFlow.

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Enterprise Data Lakes

Data lakes have become the de-facto standard for enterprises and corporations who are looking to take advantage of their existing data to drive better business decisions while minimizing costs. Adopting data lakes over traditional data warehouses has many advantages. By decoupling storage and compute you create a powerful store and processing environment for structured, semi-structured and unstructured data. Data lake also minimizes data movement and data duplication in multiple stores by leveraging a shared data catalog.

However, with great power comes great responsibility. Since the data now resides in one location and is potentially accessible by a large number of services, it is necessary to take factors like scalability, security and cost-efficiency into account.

Our Big Data experts can help you architect and deploy a scalable, secure, fully compliant and cost-effective data lake on a variety of public and private clouds.

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Data Stores

Our data engineers help architect, build and operate scalable, performant, compliant and highly available data solutions for the strictest SLA’s.

Our experts specialise in schema design, performance tuning, disaster recovery strategies as well as security and compliance for multiple industries.

Our Data Stores include:

  • Sub-millisecond OLTP systems like HBase, Cassandra or DynamoDB

  • Petabyte scale OLAP data-warehouses such as Redshift Spectrum

  • Relational databases and web-scale NoSQL databases

ML Ops

Machine Learning (ML) algorithms assist companies to make informed business decisions by analysing past data and producing accurate predictions.

In production, ML-based systems have extremely subtle properties differentiating them from traditional software systems, this is due to the fact that their outputs have more complex behaviours than the typical software components.

This complexity makes the management of ML systems trickier than the management of traditional, modularized software systems.

We help our customers focus on business logic rather than operations; handling the necessary data preparation tasks, as well as offering a robust and battle-tested CI/CD process optimised for ML model serving and A/B model testing.