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Optimized cloud spend analytics, resulting in a significant reduction in expenses, automated cost-center chargebacks, and granular insights for IT leaders
Data Indicators was engaged to help the client reduce cloud storage cost and optimize cloud spend analytics. The development project’s goal was to understand the costs of GCP and AWS, report accurate cost-center chargebacks, understand GKE namespace usage and costs, and create combined cloud spend views for IT leaders. The team also aimed to create optimization/tagging alerts to help the client manage their costs effectively.
Google BigQuery, Google Cloud SQL for PostgreSQL and Microsoft SQL Server, Google Virtual Machines, Google Cloud Storage, and Apache Airflow. To support data cataloging across the data mesh, we are implementing Secoda. For data governance, we have chosen Immuta. In addition, we used Docker where appropriate and Terraform for infrastructure management.
Standardized tooling and centralized data management supports fast onboarding and regulatory compliance for data teams
A large healthcare client needed a solution to help them make data-centric decisions, and a project was created to deliver this. The solution involved the collection and curation of large amounts of data and providing The Client with critical insights to inform decision-making. With the help of the Data Indicators team, a scalable, secure, and easily accessible solution was developed and deployed using GCP hosting and tools.
Data Indicators developed and delivered a core suite of processes and tooling that allowed The Client’s teams to retain ownership and responsibility over their product data while providing governance and data cataloging capabilities. This enabled The Client to ensure that data usage agreements and regulatory requirements are adhered to. The solution has resulted in a 400% faster time-to-onboarding for data teams and provided a standardized suite of tooling for ETL and data quality, as well as a centralized data catalog and centralized data access governance, including rights management for data usage agreements and support for regulatory compliance and auditability.
Google BigQuery, Google Cloud SQL, Google Cloud Storage, Google App Engine, Java, Spring Boot, Typescript, RESTful, Python and Google Apigee.
Comprehensive architectural guidance on foundational, structural, semantic, and organizational levels of interoperability, covering interconnectivity, data format and models, governance, and best practices.
Client needed a partner to provide architectural guidance on the four levels of interoperability, which include:
Foundational: Interconnectivity requirements between systems.
Structural: Defining the format, syntax, and organization of data.
Semantic: Underlying data models and use of data elements.
Organizational: Governance, legal policies, standards, and best practices.
The client was seeking a reliable partner to help them establish an integrated MarTech infrastructure that could support the creation of a centralized API-driven customer profile store, real-time customer engagement, content personalization, segmentation, and data science. They also required support in enhancing and maintaining their Hadoop-based data lake, implementing event-based data streaming pipelines, and ensuring data governance, privacy, and regulatory compliance. The client was looking for a trusted advisor to guide them through this process and help them achieve their marketing goals.
Data Indicators was tasked with the following deliverables:
Apache Spark ( Python and Scala ), Apache NiFi for real-time orchestration and transformation, Apache Airflow for batch orchestration and transformation, Snowflake Data Warehouse, Snowflake Snowpipe real time ingestion, Snowflake Snowpark , AWS S3, AWS EC2, Docker, Kubernetes and Adobe Experience Cloud