AWS Data-as-a-Service Platform

Enable advanced analytics by creating a centralized repository for your data and eliminating data silos.

Successful organizations strive to monetize their data by extracting critical business value from it. Adastra’s AWS data-as-a-service (DaaS) platform enables the provisioning of data from various sources and the managing of access to it. The platform focuses on data accessibility to the data downstream data consumers and reduces management and operations overhead.  

Why Adastra’s AWS Data-as-a-Service Platform?

The following real-time business objectives are supported by our AWS Data-as-a-Service platform:

Smart tools

Power self-service business intelligence, where new tools allow users to quickly produce smart dashboards.

Advanced analytics

Embrace advanced analytics services, where machine learning can be developed quickly and deployed at scale, in batch and in real-time.

Valued data

Shorten time to insights to take full advantage of the value of your data.

What Do We Do

Adastra’s AWS data-as-a-service platform offers the fastest path to value with accelerators designed to prioritize streamlined and insightful analytics. Our approach to building a modern, scalable, and secure data-as-a-service platform is based on the classical data lake and lake house architectures, but also includes centralized security and access control and automates a large part of the tedious work.

Strategy alignment and roadmap

Adastra will identify what your environment, data strategy and cloud maturity level are. Based on the findings, we not only design your platform as per your requirements, but also create a roadmap for future development and more advanced usage of the data across the entire organization.

Implement

Adastra will leverage our platform architecture blueprints and experienced team of professionals to create the platform and all necessary data pipelines for you (blueprints or custom built), establish CI/CD pipelines and make sure all security mechanisms are in place. Tap into Adastra’s expertise and benefit from our established and proven frameworks based on best practices.

Handover and training

We make sure your team is fully capable of managing the implementing the AWS data-as-a-service platform and is comfortable working with it. Optionally, you can benefit from Adastra’s managed services where we run and update the platform for you.

Approach to AWS Data-as-a-Service Platform

Fully take advantage of automated data discovery, metadata registration and data ingestion. AWS Lake Formation allows you to register various cloud data sources and it will automatically crawl them, identify the distinct source tables and register their metadata in the Catalogue. Lake Formation can then use ETL Blueprints to ingest the identified source data, without you having to manually define ETL processes.

Define once, apply everywhere. Once your security policies are defined, they “stick” to the data and are applied to any data analytics AWS services which interact with or are part of the platform. Access controls can be defined on the table, column and even cell level.

  • Leverage machine learning for better data quality and master data management.
  • Use FindMatches ML Transforms to identify duplicate records in your data and link data records (which represent the same entity – customer, product, etc.) across various data sets.
  • Implement data tokenization and custom transformations to meet specific business needs.

  • With multiple sources feeding data into your platform, data can easily be exposed to downstream data consumers, without compromising on security.
  • Execute data mesh queries with Redshift Spectrum to combine data on the fly from your AWS Redshift warehouse and any structured, semi-structured or unstructured data from your data lake.
  • Feed batch or real-time data into AWS SageMaker ML models, create visualization with Amazon QuickSight or any other supported BI tool.

Purpose-Built Analytics

AWS provides a deep portfolio of services which enable a wide variety of analytics use cases. Adastra’s DaaS platform covers the full data lifecycle:

Data ingestion

In real-time or batch using either Adastra’s data ingestion framework and tools, or purpose-built on top of EC2, Kinesis (or MSK) and/or AppFlow.

Data transformation

Using Glue or EMR, or Lambda and Amazon Kinesis Data Analytics for real-time cases.

Data lake

Leverage a secure and governed data lake on top of S3, Lake Formation and Glue Data Catalog, optionally integrated with third-party data governance tools.

Data access

Access and visualize data through a variety of interfaces (Athena, Redshift Spectrum, QuickSight, Grafana, EMR Studio).

Data prediction

Utilize a web-based toolset which covers the entire machine learning workflow, or spin-up your own custom workflow using specialized AWS services. Integrate data insights into on-premises business processes using batch or real-time interfaces.

Data insights

Use Adastra’s prebuilt platform architecture blueprint with executable infrastructure-as-a-service modules to achieve insights in under 4 hours.

Frequently Asked Questions

Data-as-a-service is a data management strategy which uses cloud storage and compute services to allow for centralized storage, processing, and management of data from various sources. It also enables advanced usage in the form of analytics, machine learning, and more.

AWS Lake Formation is a fully managed service that makes it easy for you to ingest, clean, catalog, transform, and secure your data to make it available for analysis and machine learning. Lake Formation provides a central console where you can discover data sources, define transformations, remove duplicates and match records, catalog data for access by analytical tools, configure data access and security policies, and audit and control access from AWS analytics and machine learning services.

Let’s modernize with a tailored AWS Data-as-a-Service Platform