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Data Engineering Services

Leverage our data engineering solutions to enhance your analytics and data science capabilities to drive enterprise initiatives

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What is Data Engineering And why is It Important?

As an integral aspect of data science, data engineering is the process of designing and building pipelines to transform and transport data into a format that is easy to use for data scientists or other end users. Data engineering is the enabler of all those technologies that require a lot of data to run their algorithms. It also provides data transmission speed and improves forecasting.

As a well-known analytics firm in the UK, SG Analytics’ primary goal is to enable its clients to make real-time decisions and predict future events accurately by harnessing big data. Leverage SG Analytics’ data engineering services to operate on all types of data, and build brand new pipelines.

Data engineering services

What We Offer

SG Analytics helps its clients in the UK in transforming existing processes into automated pipelines and building fresh pipelines based on business needs. These automated pipelines range from simple file transfer to complex data processing and modelling, leveraging different types of tools and technologies.

  • As a part of our data engineering solutions, we provide code development: Breakdown business processes into simple logical steps
  • Pipeline integration: Develop parameterised codes for every step
  • Workflow management tools: Performant platforms such as Airflow, Terraform, etc. to trigger the codes in sequence and build QC steps as per the requirement

SG Analytics is adept in developing server-less data processes with the use of cloud-based products. These pipelines are custom-made on the basis of different types of business use cases and the web services deployed.

  • Data engineering consultancy and advisory services to help clients select appropriate services and cloud platforms according to their requirement
  • Build functions for every step within the cloud services e.g. AWS-Lambda, Azure Functions, GCP Functions, etc.
  • Logical event-based triggers to integrate every single step
  • Time/Mail/Event triggers to run the whole process as per business and client requirements

Data processes are created in ‘Dockers’ in order to assist customers in deploying the processes easily into the clients’ production environment. This is helpful for the customers in the duplication and deployment of the process on multiple systems with relative ease.

  • Guiding the client in identifying the appropriate environment for the application
  • Construct a docker environment with all the required packages and applications
  • Robust programming to execute the required steps of the processes
  • Create the image on the required system to gain improved flexibility in deploying the container

SG Analytics conducts several purpose-driven sessions with clients to get to the bottom of their business requirements in order to design the production and development servers. SGA also assists in training and deploying new processes.

  • Compile data from multiple sources into a single system by building data pipelines
  • Logical flow process to consolidate different data sources with primary and foreign keys
  • Build single-source table/views to provide clean and ready data for data analytics projects

In order to help our clients, multiple processes with simple input values, web server and on-premises server-based APIs are deployed. These can range from a simple data extraction from a data lake to multiple data transformations or image/voice analysis on the inputs provided.

  • Parameterised codes to collate user inputs and operate on them
  • Run the codes in well-prepared servers and provide endpoints for user usage
  • Custom authentication and authorisation for each endpoint to strengthen data security

SG Analytics’ data science solutions help its customers in mining meaningful information from various sources of text data such as online reviews, emails, tweets, notes from feedback forums, survey results, etc. Leverage our data science solutions to extract valuable insights that help you understand your customers: what they think and feel about your products and services.

  • Pipeline-driven ML engines and custom-built data stewardship interfaces to create mastered data sets
  • Automatic summarisation on legal and business documents by building pipelines and leveraging AWS services such as Comprehend and Textract
  • NLTK-based workflows to automate document tagging for Knowledge Management documents


Creating a strong foundation of data with commercial grade solutions for large scale data processing

Using fast cluster computing technologies to monetise and maximise value data assets

Who we work with


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Our full stack data engineering services and solutions provide organised, standard data flow powering data-driven models and decisions for your business.