Real-time data integration

Deliver trust and speed for data-driven business decisions and real-time analytics.

Today’s financial institutions need to modernize data management and accelerate reporting cycles in order to keep up with regulatory compliance, competition, and risk management. By integrating data from a wide range of sources into a cloud data lake, and applying data quality and tracking criteria, financial institutions can facilitate correlations between data sets, accelerating their ability to innovate, improving customer service, and enabling regulatory compliance.

Real-time data integration

Solutions

Talend

Talend’s end-to-end cloud data integration platform, Talend Cloud, offers over 70 connectors and components for AWS services, including self-service data preparation, data integration, data quality management, and native Apache Spark code generation capabilities for Amazon EMR. These can enable governance, trust, and speed for financial institutions who leverage AWS data lake or data warehouse environments.

Talend Cloud enables you to quickly integrate cloud and on-premises applications, data and analytics sources. The Talend Cloud Remote Engine for AWS is a deployment mechanism for Talend Cloud, which provides broad connectivity, and delivers a governed single source of truth for real-time cloud processing and advanced analytics on AWS. The below architecture diagram displays the end-to-end data workflow:

Talend Cloud
Financial services

Wealth management firm leverages Talend for real-time customer opportunities

A financial services company providing superannuation and investment products, insurance, financial advice, and banking products faced a common financial services challenge: needing to manage more data about its customers and ensure its reports were accurate and in accordance with regional regulatory guidance. They used Amazon Simple Storage Service (Amazon S3), Amazon Elastic MapReduce (Amazon EMR), Amazon Athena, and the Talend Platform to create data opportunities. These opportunities included the ability to ingest disparate data types, reconcile data, assess data quality, and to manage metadata. These solutions also allowed them to gain an understanding of data lineage and what was in their data lake.

Today, the financial services organization is able to process 10 billion rows of structured and unstructured data in under an hour and ensure that the data in its reports for regulators is validated and correct.

Head of Information Management's quote
Although this was built for this re-insurance use case, we actually built them in such a way that they can now be extended and used for pretty much any other use case related to those critical data sets.
- Head of Information Management, Technology and Operations, wealth management firm
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