Our Solutions
Serverless API
API designs adhere to RESTful principles, and designs are completely independent from the specific implementation. Standard documentation tools, such as SwaggerHub or alternative sources are employed, with a particular focus on GET, POST, PUT, and DELETE methods.
Serverless implementations are accomplished using tools like AWS API Gateway and AWS Lambda to efficiently access data from sources like Redshift and other data repositories.
We offer deployment tools and code generators to accelerate the process of developing and deploying a serverless RESTful API accessing your data.
Serverless Data Store
Data store designs, whether hierarchical, semi-structured, unstructured, or traditional relational, are communicated via standard documentation, such as Entity-Relationship (E/R) diagrams or other diagramming tools. Our implementations are highly flexible and tailored to the specific design and access requirements. This adaptability spans a wide spectrum, accommodating everything from document data stores to more conventional Relational Database Management System (RDBMS) setups. Serverless implementations leverage services such as Dynamo DB or Redshift Serverless.
Enterprise data models are often complex and made up of diverse sub-models, so it’s common for our implementations to include multiple data stores, optimizing them for various functions such as data ingestion versus reporting or addressing distinct usage patterns. Modern Lakehouse architecture is designed to enable a unified reporting and analysis platform accessing diverse underlying data stores.
Serverless ETL
Serverless ETL and/or ELT designs are based on mapping and transformation documentation; they seek to limit processing and passes through source data. Implementations focus on serverless offerings such as AWS Glue, for example. Smaller, faster tasks can be performed by more lightweight components, such as AWS Lambda.
This strategic combination can be used to minimize the total cost of ownership while seamlessly scaling to address the most demanding performance prerequisites. The transition from existing Spark/SparkSQL applications to serverless components such as AWS Glue can be nearly effortless. Data migration is accomplished using existing services, such as AWS Data Migration Service.
Serverless Workflow
Individual workflows are automated via serverless offerings such as AWS Step Functions. Orchestration of multiple workflows is typically accomplished using a solution that automatically scales the number of workers, such AWS MWAA (Managed Workflows for Apache Airflow). Such a choice presents an extensible and highly scalable solution, tailored to the automation of multiple ETL and reporting workloads in the cloud environment.
Apache Airflow, at its core, offers a rich tapestry of features, and this implementation smoothly interfaces with external services. It does so through a range of built-in operators and sensors, while also providing the flexibility to construct custom operators and sensors in Python, ensuring that your workflow orchestration meets your unique requirements.
Serverless AI/ML Integration
Integrate your data with ML algorithms to build new AI models or feed your trusted data to existing third-party models to automate results. Designs focus on feature selection and input data available to produce the desired output.
Serverless implementations are accomplished using tools such as AWS SageMaker or AWS Bedrock for generative AI. For enhanced efficiency, training new models and/or accessing existing models can be seamlessly integrated into your ETL processes, allowing AI-driven results to become part of your data store and standard reporting.
