A US Fintech Firm transforms and analyses data with Redshift and Kinesis

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A US-based fintech company provides real-time analytics to traders and investors. As part of its services, they have to process and analyze large volumes of news data from various sources in real time to help its customers make informed trading decisions. The previous data processing and analysis infrastructure relied on a combination of on-premises servers and cloud-based services, which was not scalable with the increasing volume and complexity of the data.


The firms existing data processing and analysis infrastructure faced several challenges, including:

  • Limited scalability and performance: The on-premises servers and cloud-based services could not handle the growing volume of data, resulting in slow query times and reduced accuracy of the news analytics.
  • High costs and complexity: The combination of on-premises and cloud-based infrastructure resulted in high costs and complexity, making it difficult to manage and maintain the system.
  • Poor reliability and availability: The on-premises servers were prone to downtime and outages, leading to disruptions in the delivery of news analytics to customers.


The firm partnered with Digital Alpha to provide a scalable and efficient solution for processing and analyzing large volumes of news data. Digital Alpha recommended using Amazon Redshift and Amazon Kinesis, which are highly scalable and cost-effective tools for data analysis.

  • Migrated clients’ data from the on-premises servers and cloud-based services to Redshift, using the S3 data loading feature to load the data quickly and efficiently.
  • Configured Redshift to support fast querying of the data using columnar storage and other performance-enhancing features.
  • Set up Kinesis to ingest real-time data streams from various sources and process the data in near-real-time using Kinesis Data Streams and Kinesis Data Analytics.
  • Integrated Kinesis with Redshift using Kinesis Data Firehose, allowing the firm to load the processed data into Redshift for further analysis.
  • Provided ongoing support and maintenance to ensure the smooth operation of the Redshift and Kinesis infrastructure.


The implementation of Redshift and Kinesis provided several benefits to the firm, including:

  • Improved performance and accuracy: Redshift and Kinesis allowed them to process and analyze large volumes of data quickly and accurately, enabling faster and more reliable customer insights.
  • Reduced costs and complexity: Redshift and Kinesis allowed it to eliminate the need for on-premises servers and simplify its data processing and analysis infrastructure, resulting in lower costs and improved efficiency.
  • Increased reliability and availability: The fully managed and scalable nature of Redshift and Kinesis ensured high availability and reliability, allowing the firm to deliver uninterrupted news analytics to its customers.

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