AWS Data Lake and Analytics

S3 - cloud-based object store

With the Amazon Simple Storage Service (Amazon S3), you use cloud-based AWS object storage to store and manage any amount of unstructured and structured data. Amazon S3 gives you the option of using management functions to optimise access to your data. This allows you to meet your specific business, organisational and compliance requirements. It is suitable for a wide range of data storage applications due to its scalability, availability, security and performance. The service provides the basis for data storage in AWS-specific Woodmark architectures.

Your advantages with Amazon S3

Maximise your end-to-end data knowledge and break down existing silos.
You have all your data at your fingertips and increase the innovative power and potential of your company.
Eliminate server management and store your data in the AWS cloud.
Scale your data virtually indefinitely and store it from any source.

Data-Warehousing with Amazon Redshift

This cost-effective, high-performance data warehouse service enables you to store and quickly analyse large volumes of data. Woodmark likes to use this AWS service because it offers a very high level of data security and scalability. Redshift is able to customise capacity and processing power to your requirements, giving you scaling options.

Redshift uses SQL to optimally analyse data in data warehouses, operational databases and data lakes with hardware developed by AWS and Amazon Machine Learning.

Integrate your data with AWS Glue

AWS Glue is a powerful tool for quickly and effectively extracting and transforming data from various sources and managing it in an Amazon Data Lake. Amazon Glue is characterised by its simplicity and serverless deployment, which leads to fast results and lower operating costs. As an experienced consultancy, we will show you how easily and effectively data transformation can be implemented.

Our services

Connection of all data sources including connection of APIs and streaming data sources as the basis for the cloud data platform
Development of data lakehouses and hybrid data warehouses
Migration of existing ETL processes to the AWS Cloud
Development of metadata catalogues (data catalogue) for integration into other AWS services
Establishment of schedule-controlled or event-triggered data pipelines