Getting to a Single Source of Truth | Starschema

Getting to a Single Source of Truth

Structured, SOX compliant, multi-layer data lake

Practice Area

  • Data Engineering

Business Impact

  • Dramatically reduced operational costs
  • Timely, accurate data for critical finance department functions

Challenges

  • Compliance
  • Auditing capability
  • Large number of source systems
  • Scalability

Technologies

HVR, Talend, Hadoop, Oracle

Summary

Large enterprises acquiring companies often face challenges integrating their financial systems. Over the last several decades, our client, a global enterprise, has acquired dozens of businesses, each with its own ERP system and data warehouse solution. By operating dozens of data warehouses, our client incurred license, equipment and personnel costs.

To improve performance and reduce costs, our client launched an ambitious project to consolidate these disparate data warehouses into a single data lake for its financial data. This presented many technical, operational and security challenges.

Our client reached out to Starschema to design and implement a highly performant, SOX compliant and secure data lake solution.

Challenge

Every day, our client's companies record tens of thousands of financial transactions. This poses a unique challenge for a data lake:

  • Highly granular data, usually transaction-level, needs to be ingested in near real-time (latency <1 hour)
  • Over 100 source systems belonging to more than thirty different types
  • The solution needs to comply with Sarbanes Oxley (SOX) requirements
  • Zero tolerance for data inconsistencies
  • 200TB of enterprise data comprising more than 25,000 data domains (tables) of over five hundred distinct types
  • Provide simultaneous data consumption and continuous ingestion

Solution

Starschema implemented a state-of-the-art architecture by deploying the Starschema Antares iDL™ design, in which raw data would first be mirrored by ingestion into a Massively Parallel Processing (MPP) relational database using HVR and Talend. Subsequently, data would be replicated to in-memory and Hadoop (file system) based consumption layers for later use, including aggregation, data stores, and data science applications.

Data consumption then takes place over a dynamic lambda architecture, providing streaming and batch processing layers. To facilitate the operation of this multi-layer data lake, a standard data definition structure (standard model) was devised for identical domain types of raw data, and a metadata knowledge base was used to store discovered constraints and relationships within the data. At this stage, a standard data model was devised for identical domain types of raw data.

In addition, the data lake automatically generated a continuously updated metadata knowledge base to store discovered constraints and relationships within the data, providing a comprehension of the data lake’s underlying structure itself. This in turn drives the Generic ETL Framework (GEF) and Data Lake Audit Framework (DAF) that constantly maintains and audits the data layers. Operations, change management, and development are supported by ITIL and SOX compliant DevOps CI/CD pipeline applications, which maintain compliance through a process-forcing design.

Results

Every day, the SOX certified the Finance Data Lake ingests approximately 200TB data through 6,000 parallel ETL processes from a diverse range of source systems – Oracle, SAP, PeopleSoft, Hyperion, enterprise-developed systems, etc. – into a single Oracle EBS type Standard Model.

Data is ingested in near real-time, allowing the enterprise to perform crucial finance functions, such as closing and reporting, account reconciliation and centralized tax calculation, based on accurate, consistent and up-to-date data.

Starschema Inverba NLG™

As humans, we think and communicate in narratives. Numbers and visualizations help, but taken on their own, they often don’t tell the full story. A good dashboard may be useful for showing trends and conveying metrics, but it’s words that resonate with decision-makers and provide them with important context and situational awareness

Dashboard Collaboration for Tableau

At the heart of data driven organizations lies the concept of data democratization: making data more accessible to everyone and making it easier to share, discuss, and act upon. Data visualization tools like Tableau help companies view, understand and leverage their data to make better decisions about their business.

Large Scale Data Replication Deployment

Our client, a global manufacturer in the power generation industry, faced challenges with an aging reporting environment based on multiple Oracle ODS systems and reporting straight from source databases.

Palette Insight

Palette Insight provides actionable intelligence about your Tableau deployment to help you maximize the benefits of Tableau Server.

SCROLL

This website uses cookies

To provide you with the best possible experience on our website, we may use cookies, as described here. By clicking accept, closing this banner, or continuing to browse our websites, you consent to the use of such cookies.

I agree