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3 Ways FinTechs Can Improve Cloud Observability at Scale

Financial technology (FinTech) companies today are shaping how consumers will save, spend, invest, and borrow in the economy of the future. But with that innovation comes a critical need for scalable cloud observability solutions that can support FinTech application performance, security, and compliance objectives through periods of exponential customer growth. In this blog, we explore why cloud observability is becoming increasingly vital for FinTech companies and three ways that FinTechs can improve cloud observability at scale.

How to Reduce Continuous Monitoring Costs

Continuous monitoring is a crucial practice in the fields of DevOps, cybersecurity, and compliance. It involves the proactive and ongoing process of observing, assessing, and collecting data from various systems, applications, and infrastructure components in real-time or near real-time. Continuous monitoring is closely related to observability, which goes beyond simple monitoring to provide a deep understanding of complex and dynamic systems.

5 AWS Logging Tips and Best Practices

If you’re an Amazon Web Services (AWS) user, you’re probably familiar with some of Amazon’s native services available for logging and monitoring, such as CloudWatch and CloudTrail. With that said, log management can get complicated quickly, especially if you’re dealing with a high volume of logs from AWS Lambda functions or a multi-cloud/hybrid cloud environment.

The Ultimate Guide to ELK Log Analysis

ELK has become one of the most popular log analytics solutions for software-driven businesses, with thousands of organizations relying on ELK for log analysis and management in 2021. In this ultimate guide to using ELK for log management and analytics, we’re providing insights and information that will help you know what to expect when deploying, configuring, and operating an ELK stack for your organization. Keep reading to discover answers to the following.

Data Lake vs Data Warehouse

Data warehouses and data lakes represent two of the leading solutions for enterprise data management in 2023. While data warehouses and data lakes may share some overlapping features and use cases, there are fundamental differences in the data management philosophies, design characteristics, and ideal use conditions for each of these technologies.
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Serverless Elasticsearch: Is ELK or OpenSearch Serverless Architecture Effective?

Here's the question of the hour. Can you use serverless Elasticsearch or OpenSearch effectively at scale, while keeping your budget in check? The biggest historical pain points around Elasticsearch and OpenSearch are their management complexity and costs. Despite announcements from both Elasticsearch and OpenSearch around serverless capabilities, these challenges remain. Both of these tools are not truly serverless, let alone stateless, hiding their underlying complexity and passing along higher management costs to the customer.

What is a Real-Time Data Lake?

A data lake is a centralized data repository where structured, semi-structured, and unstructured data from a variety of sources can be stored in their raw format. Data lakes help eliminate data silos by acting as a single landing zone for data from multiple sources. But what’s the difference between a traditional data lake and a real-time data lake?

How Gaming Analytics and Player Interactions Enhance Mobile App Development

The number of mobile game users is expected to increase to 2.3 billion users by 2027, with a CAGR of 7.08%. The resulting projected market volume is a staggering $376.7 billion by 2027. Competition is fierce, and differentiation is key to winning out in this rapidly growing market. To understand their users and build better games, gaming companies need to use data analytics to interpret how players interact with their games. Effective use of video game data can help companies.

Data Retention Policy Guide

Data retention policy will become a major focus for CIOs in 2021. Here’s why: First, enterprise organizations are producing larger volumes of data than ever before and utilizing enterprise data across a wider range of business processes and applications. To maximize its value, this data must be managed effectively throughout its entire life cycle - from collection and storage, through to usage, archiving, and eventually deletion.