Secure Data Engineering At Scale
We understand that our clients’ data is a core business asset and key to their success. Being able to reliably ingest and store data is critical – as is the ability to mine it for competitive insight. We also know the damage that embarrassing data breaches can cause. OpenCredo has a proud history of delivering secure and reliable data-driven systems at scale. Success has spanned challenges in areas such as recommendation engines, real-time processing of IoT datasets and analysis of complex infrastructure & network interconnected (graph) datasets.
Confused about Big Data, Fast Data, Unstructured Data? Is it better to go with Streaming, Event Sourcing, Message or Event driven approaches? Combined with our Application Architecture and Cloud experience, our consultants deep knowledge of both distributed systems and large scale data processing principles are key to delivering insight through the right combination of technology and approach for your context.
We provide pragmatic advice and hands-on delivery capabilities across a wide variety of N*SQL databases, both open source and cloud vendor offerings. We partner with many of the key innovators in the space including Confluent (Kafka), Neo4j, Hazelcast and Datastax (Cassandra). Our strict policy of not reselling allows us to operate as a trusted advisor and independent partner for our clients. Our teams provide key architectural, data modelling and delivery skills for many of our clients most critical projects.
The requirement to monitor and analyse specific streams of events in real time is becoming critical for many organisations. Doing so in a reliable and predictable manner however, requires not only the right combination of technology, but equally important, architectural and processing approach. This is where our real time stream processing experience can help to understand the nuances, avoid common pitfalls and understand the impact of the trade offs made when going down one path versus another. This is regardless of whether you use an open source offering like Kafka, Spark or a cloud vendors product.
Our data engineering foundation often provides the platform for us to incorporate elements of data science and machine learning into your data driven systems. Whether commodity ML models offered by cloud providers, or models created from your own data science initiatives, we can advise, help discover, as well as ensure models are appropriately integrated and maintained moving forward.
Additionally in conjunction with our Cloud practice, and through the disciplines of automation and programmatic infrastructure, we create secure, reliable cloud environments for Data Scientists to train models and perform ad-hoc analysis within.