TES: Turning Complex Data into Actionable Insights

Connected Data. Better-Informed Decisions.

One Beyond helped TES build a data platform that brought information from multiple sources together, supporting audience analysis, business performance tracking and data science.


At a glance

Daily Scale

The platform ingested more than 20GB of user-interaction data each day.

Connected Sources

More than twenty microservices supported the collection and processing of data from multiple systems.

Consistent Data

Transformation processes standardised information for use in a central data warehouse and supporting databases.

Deeper Analysis

Hadoop-based processing combined and enriched datasets to support more detailed analysis.

Operational Visibility

Datadog dashboards monitored data and processes, with alerts highlighting abnormal activity.

About TES Global

TES Global is a digital education company whose teaching resources platform enables teachers to share lessons and curriculum-based content. The platform supports the exchange of educational materials, helping educators draw on resources contributed by their peers. As a digital business, TES generates data through audience interactions and its supporting systems, creating opportunities to better understand how people use its services and to track business performance.

The Challenge

TES recognised that it was not making full use of its data and wanted a stronger foundation for understanding its audience and monitoring business KPIs. Information needed to be collected from multiple sources, brought into consistent formats and made accessible for analysis. The challenge extended beyond storing data: TES needed to combine and process it into information that applications, dashboards and data scientists could use. This called for a dedicated platform capable of handling substantial daily volumes as part of its wider digital transformation.

Our Solution

One Beyond built an extract, transform and load (ETL) platform using more than twenty Node.js microservices hosted on AWS. We created a Hadoop-based transformation layer to normalise, aggregate and enrich data, with Amazon Redshift providing the central data warehouse. Supporting databases and an integration layer using RabbitMQ and Amazon Kinesis made information available to applications, APIs and data scientists. Overnight processing used Hive and Spark through Amazon EMR, while Datadog dashboards and alerts provided visibility into the platform’s data flows and operations.

A closer look at features and benefits

Data collection from multiple sources

Automated data normalisation and transformation

More than twenty microservices

Central Amazon Redshift data warehouse

Hadoop-based aggregation and enrichment

Overnight batch processing

Data access for applications and APIs

Datadog monitoring dashboards

Automated abnormal-activity alerts

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