Business winners strive to monetize their data by extracting the critical and game-changing business value from it. Adastra’s AWS Data-as-a-Service platform enables provisioning of data from various sources and managing the access to it. The platform focuses on easy access to the data inside by downstreaming data consumers and reducing the management and operations overhead.
Why AWS Data-as-a-Service Platform?
The following real-time business drivers are supported by our AWS Data-as-a-Service platform:
Power self-service business intelligence, where new tools allow power users to quickly produce smart dashboards.
Embrace advanced analytics services, where machine learning can be developed quickly and deployed at scale, in batch and real-time.
Shorten time-to-insight which results in taking full advantage of the value of data.
AWS provides a deep portfolio of services which enable a wide variety of analytics use cases. Adastra’s data-as-a-service platform covers the full data lifecycle:
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.
using Glue or EMR, or Lambda and Amazon Kinesis Data Analytics for real-time cases.
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.
Access and visualize data through a variety of interfaces (Athena, Redshift Spectrum, QuickSight, Grafana, EMR Studio).
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-premise business processes using batch or real-time interfaces.
Use Adastra’s prebuilt platform architecture blueprint with executable infrastructure-as-a-service modules to achieve time-to-insight of under 4 hours.
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 and 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.
Democratize access to your data
- 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 AWS QuickSights or any other supported BI tool, etc.
What 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 adding centralized security and access control, and automating a large part of the tedious work.
Strategy alignment and roadmap
Identify what are your data strategy, cloud maturity and environment. Based on the findings, we not only design your data lake solution as per your requirements, but also create a roadmap for future development and advanced usage of the data lake across the entire organization.
Leverage our experienced team of seasoned professionals to create the data lake and necessary data pipelines for you, establish CI/CD pipelines, and make sure all security mechanisms are in place. Tap into Adastra’s expertise and benefit from frameworks, based on best practices and implemented in numerous other successful deliveries.
Handover and training
We make sure your team is fully capable of managing the implemented data lake and is comfortable working with it. Optionally, you can benefit from Adastra’s Managed Services where we run and evolve the data lake for you.
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 and enable advanced usage of it – analytics, machine learning, etc.
AWS Lake Formation is a fully managed service that makes it easy for you to ingest, clean, catalog, transform, and secure your data, making it available for analysis and machine learning. Lake Formation provides a central console where you can discover data sources, define transformation on it, remove duplicates and match records, catalog the data for access by analytical tools, configure data access and security policies, and audit and control access from AWS analytics and machine learning services.