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DATA ENGINEERING SERVICES

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

What is Data Engineering and Why is it Important? 

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.  

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Why SGA ?

INDUSTRIES WE SERVE

BFSI

BFSI
TECHNOLOGY

TECHNOLOGY
MEDIA & ENTERTAINMENT

MEDIA & ENTERTAINMENT

Automation of Data Processes

  • Code development: Breakdown business processes into simple logical steps
  • Pipeline integration: Develop parameterized codes for every step
  • Work-flow management tools: Performant platforms like Airflow, Terraform, etc., to trigger the codes in sequence and build QC steps as per requirement

Serverless Data Processes

  • 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

Dockerizing Data Processes

  • Guiding the client in identifying the appropriate environment for the application
  • Construct 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

Hadoop/On-Premises

  • 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

API Application

  • Parameterized 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 authorization for each endpoint to strengthen data security

NLP and Text Analytics

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

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*By sharing the information you have entered, you give your express consent to SG Analytics to use the provided information to contact you with relevant information related to its offerings and services as and when required. SG Analytics secures all your personal information from unauthorized access, use or disclosure. For more information, please visit our privacy policy.

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