Data Engineering focused on structured and actionable statistics

Data engineering is a complex yet vital role in modern software development which requires an understanding of different tools, languages and methodologies for accessing, normalizing, and logically storing data.

Data enters the pipeline from many sources and needs to be normalised and fed intelligently into a data lake designed by experienced engineers in order for organisations to be able to analyse and utilise that data in business applications.

We understand the importance and complexity of data engineering and deliver comprehensive solutions to clients which allow them to control and analyse their data more efficiently.

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A modern approach to data engineering

All of our projects start with a discovery workshop with the senior product designer and senior technical director for the project.

During this workshop we work with client teams to understand the data infrastructure required to deliver meaningful metrics. We establish a roadmap and milestones for the project which include building a data pipeline, data lakes, and data warehouses.

We pinpoint what data is vital to the company and how it should be structured in order to devise logical solutions that allow organisations to derive maximum value from the data being collected and processed.

Well structured and well defined data engineering processes result in more efficient applications, more robust data warehouses and ultimately more valuable data.

By engineering data pipelines according to performance and scalability data accessibility is significantly increased.

Our Data Engineers work side by side with client teams to help them understand the architecture of the data infrastructure so that they can manage, maintain and grow the data warehouse after the product launches.

By harnessing the power of cloud technologies we provide our clients with the data they need to access in real-time empowering organisations to make informed decisions with confidence.

GraphQL

We use the GraphQL query language for APIs to aggregate specific data from multiple sources.
Read more about efficient data architecture with GraphQL

Python

Python is the preferred language for data manipulation and data engineering and we are experts at writing the code that is the backbone of data pipelines.

Postgres

Postgres is a powerful, open-source, object-relational database known for its performance, robustness and reliability that is an excellent solution when properly implemented.

Redis

Redis is a highly performant non-relational database which can significantly increase the efficiency of your applications with the correct implementation.

Solve your data engineering issues with NearForm

Data
Infrastructure

  • Management

  • Architecture

  • Security

  • Design

  • Automation

  • Strategy

Data
Pipeline

  • Integrations

  • Auditing

  • Big Data

  • ETL/ELT Jobs

  • Normalisation

  • Node.js

Data
Consulting

  • Performance

  • Consistency

  • Platforms

  • Compliance

  • Governance

  • Migration

NearForm’s enterprise software development approach

Our Front-end development engineers and Node.js experts can guide you with best practices for building maintainable, scalable, high-performance modern web applications. This includes Node.js Certification from the OpenJS Foundation and ongoing code-reviews and remote webinars.
Many of our engagements begin with a request for help on an existing problematic application. We have assisted some of the world’s biggest brands in rescuing and hugel