Systems | Development | Analytics | API | Testing

Discover Financial Services Automates Data Ingestion for Real-Time Decision-Making at Scale

Making operational decisions in a tight timeframe is critical to the success of an organization. Real-time data ingestion enables quicker data availability, in turn enabling timely decision-making. Real-time ingestion is foundational to our digital transformation at Discover Financial Services. As a senior manager leading the streaming and real-time data platforms at Discover, I don’t want to be in the data replication business manually.

What Is Database Schema? A Comprehensive Guide

A database schema, or DB schema, is an abstract design representing how your data is stored in a database. Database schemas can be visually represented using schema diagrams, such as the one below: A database schema diagram visually describes the following: Database schemas are at the heart of every scalable, high-performance database. They’re the blueprint that defines how a database stores and organizes data, its components’ relationships, and its response to queries.

ANSI X12 vs EDIFACT: Key Differences

Electronic Data Interchange (EDI) is a popular communication method that enterprises use to exchange information accurately and quickly with trading partners. EDI transmits data almost instantaneously — serving as a fast and efficient mode for exchanging business documents. ANSI X12 vs. EDIFACT are the two most common EDI standards used for EDI, but they have different structures, styles, and usage.

Event-Driven Microservices in Banking and Fraud Detection | Designing Event-Driven Microservices

How do we know whether Event-Driven Microservices are the right solution? This is the question that Tributary Bank faced when they looked at modernizing their old fraud-detection system. They were faced with many challenges, including scalability, reliability, and security. Some members of their team felt that switching to an event-driven microservice architecture would be the magic bullet that would solve all of their problems. But is there any such thing as a magic bullet? Let's take a look at the types of decisions Tributary Bank had to make as they started down this path.

Data Provenance vs. Data Lineage: Key Differences

Two related concepts often come up when data teams work on data governance: data provenance and data lineage. While they may seem similar at first glance, there are fundamental differences between the two concepts. Data provenance covers the origin and history of data, including its creation and modifications. On the other hand, data lineage tracks the data’s journey through various systems and processes, highlighting its flow and transformation across different data pipeline stages.

Countly's Product Analytics for The Privacy-Conscious Crypto Market

This article is part of a mini-series showcasing how Countly serves various industries. If you're interested in exploring product analytics from different market perspectives, these articles might be helpful: This isn't our first discussion about the crypto market in relation to product analytics and data privacy. While we’ve previously explored topics like NPS and crash analytics, in this article, we'll take a more comprehensive approach, addressing the following questions.