Tag: Data

Solution Spotlight – Incident Response Foundations

Posted September 28, 2023

Vertical Relevance's Incident Response Foundation lays the groundwork for an AWS Organization to take advantage of native AWS services for the detection and management of security incidents.  It supplements the available CloudFormation resources with a custom resource that can be enhanced for the enabling of additional Security Hub supported services at the organization level.

Solution Spotlight – Monolith To Microservices Foundation

Posted September 5, 2023

Vertical Relevance’s Monolith to Microservices Foundation provides a proven framework on how to break the monolith and deliver improved agility to increase the pace of innovation and drive value to your customers and your business.  By following the approaches laid out in this Foundation, customers will manage risk and lay out a consistent iterative approach to decompose the monolith into cloud native microservices, following a well-defined process.

Solution Spotlight – Application Observability Foundations

Posted January 12, 2023

With the ever-growing adoption of the cloud and hybrid cloud, businesses are struggling to “connect the dots” when it comes to customer experience – regardless of whether the customer is in-house or external. By implementing instrumentation and distributed tracing as discussed throughout this solution, enterprises will be able to leverage their single pane of glass to improve performance at the margins and quickly identify and remediate application issues as they arise.

Solution Spotlight – Data Mesh Foundations

Posted November 24, 2022

A Data Mesh is an emerging technology and practice used to manage large amounts of data distributed across multiple accounts and platforms. It is a decentralized approach to data management, in which data remains within the business domain (producers), while also making data available to qualified users in different locations (consumers), without moving data from producer accounts. It is a step forward in the adoption of modern data architecture and aims to improve business outcomes. A Data Mesh is a modern architecture made to ingest, transform, access, and manage analytical data at scale.

Module Spotlight – Experiment Broker

Posted November 2, 2022

Vertical Relevance's Experiment Broker provides the infrastructure to implement automated resiliency experiments via code to achieve standardized resiliency testing at scale. The Experiment broker is a resiliency module that orchestrate experiments with the use of state machines, the input is driven by a code pipeline that kicks off the state machine but also can be executed manually. Coupled with a deep review and design of targeted resiliency tests, it can help ensure your AWS cloud application will meet business requirements in all circumstances.

Use Case: Lakehouse and Data Governance

Posted August 16, 2022

In this use case learn how a leading financial services company obtained a data platform that is capable of scaling to accommodate the various steps of the data lifecycle along with tracking of all the steps involved including cost allocation, parameter capturing, and the providing of metadata required for integration of the client’s third party services.

Use Case: Financial Risk Data Analytics Pipeline and Lakehouse

Posted August 10, 2022

In this use case learn how a leading financial services company obtained a carefully planned, scalable, and maintainable testing framework that dramatically reduced testing time for their mission-critical application and enabled them to constantly test the applications releasability.

Solution Spotlight – Data Pipeline Foundations

Posted July 14, 2022

The Data Pipeline Foundations provide guidance on the fundamental components of a data pipeline such as ingestion and data transformations. For data ingestion, we heavily leaned on the concept of data consolidation to structure our ingestion paths. For transforming your data, be sure to utilize our step-by-step approach to optimize architecting your data for end-user consumption. By following the strategies provided, your organization can create a pipeline to meet your data goals.  

Solution Spotlight – Lake House Foundations

Posted January 19, 2022

By implementing a Lakehouse, an organization can avoid creating a traditional data warehouse. Organizations are enabled to perform cross-account data queries directly against a Lake Formation Data Lake through Redshift Spectrum External Tables and/or Athena. Table and Column-Level access granularity achieved through Lake Formation Permissions. Data Lake Governance enabled through Lake Formation Resource Shares. Multi-regional, parameterized, infrastructure-as-code deployments. Full data flow and processing pipeline with Glue Jobs, orchestrated by a single Step Function.
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