Leverage our data engineering solutions to enhance your analytics and data science capabilities to drive enterprise initiatives
Automation of Data Processes
Automation of Data Processes
Serverless Data Processes
Serverless Data Processes
Dockerising Data Processes
Dockerising Data Processes
Hadoop/On-Premises
Hadoop/On-Premises
API Application
API Application
NLP and Text Analytics
NLP and Text Analytics
What IsData EngineeringAnd Why Is ItImportant?
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.
Why SGA
Why SGA
What We Do
Automation of Data Processes
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
Serverless Data Processes
- 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
Dockerising Data Processes
- 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
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
- 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
NLP and Text Analytics
- 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
