Big data is no longer just a tool for record-keeping in the insurance industry as it has become a strategic force enabling fraud prevention, faster claims, and personalized pricing. Yet adopting these capabilities isn’t without challenges, whether it’s navigating compliance requirements or controlling costs. With the right cloud-native approach, however, insurers can overcome these hurdles. This article shares how insurers can partner with a company such as Romexsoft to transform data into measurable business outcomes.

The blog gives an overview of:

• Evolution of data usage in insurance
• Benefits of big data analytics for insurers
• Core challenges of adoption
• Practical use cases with AWS examples
• Strategic approaches to overcome adoption barriers.

Teams ranging from DevOps to IT operations now face the increasingly common challenges of backup audit failures, slow ransomware recovery, and rising cloud storage bills. CTOs and engineering leads often have to deal with complicated SLAs, multi-cloud sprawl, and automation blind spots, but they still lack the confidence to say that their systems will recover when required.

This article guides you through the mechanisms of designing a backup system for IT infrastructure and business data, using immutability, orchestration, and smart cost control to create a defense that stands up to both attackers and auditors.

The blog focuses on:

• Why cloud backups are essential
• Challenges in cloud data protection
• Practices for resilient and cost-effective backups
• Reference architectures for cloud and hybrid setups
• Implementation checklist for cloud backup strategies

This article explains the benefits of using big data analytics in banking, as well as the challenges associated with implementing these solutions, and provides real-life examples. We will also examine the impact that politics in various regions have on the adoption of analytic tools and cloud-based technologies in the fintech sector.

The blog gives an overview of:

• the importance of big data analytics
• challenges of implementing big data in banking
• key use cases of big data and analytics in fintech
• regional outlook for the use of big data in banking
• how Romexsoft supports banks with big data

This article explains how to ensure the security of a data lake and why it’s essential to incorporate it from the planning stage onward. As threats continue to evolve, your security must be able to cope with the rising challenges, which will require thorough planning, solid foundations, and effective monitoring, auditing, and alert processes.

The blog gives an overview of:

• what a data lake is and why its security matters
• core data lake security domains
• the technical and compliance challenges in securing a data lake
• comparison of SDL and SIEM
• architecture-level and operational best practices for securing data lakes on AWS

From this article, you’ll learn about the importance of a strategic approach to cloud transformation for keeping your system resilient and scalable as it grows with your business. We will cover the key differences between migration and transformation in the cloud, discuss the challenges you will face during these changes, provide real-life examples of how to resolve them, and share a five-phase transformation strategy.

The article covers:

• why having a transformation strategy matters
• migration vs transformation differences
• core modernization & transformation challenges
• real‑world success stories
• five‑phase transformation strategy
• transformation readiness self‑check questions

To ensure public cloud security, you must use a multi-layered approach that covers your system from every angle. This article explains how to do this step-by-step in the shared responsibility model context of public cloud. It also describes best security practices and lists AWS-native and third-party tools needed to strengthen defenses, increase visibility, and automate security in the public cloud. Finally, it explains threats you can face in the public cloud and how to evaluate if you need to upgrade your current security setup.

The blog gives an overview of:

• public cloud security mindset shift
• seven‑step security automation roadmap
• post‑migration threats
• public cloud security best practices
• key AWS security tools
• quick maturity self‑assessment

Which cloud platform reigns supreme? Azure, AWS, or Google Cloud? In this article, we explore identical cloud services from these leading providers, with their distinct offerings and shared traits.

Keep on reading to find out how these platforms measure up against each other at an organizational level and which one has the upper hand in the AWS vs Azure vs GCP comparison. Upon pursuing this article, you will be able to:

• Trace back the origin and strategic approaches of major cloud providers
• Compare GCP, AWS and Azure’s cloud computing offerings
• Investigate the merits and demerits of cloud platforms

Data integrity in automation testing is a cornerstone for achieving reliable and accurate test outcomes. The essence of maintaining this integrity lies in a comprehensive understanding of data validation, continuous monitoring, and proactive mitigation of risks associated with data handling in automated tests. This guide delves deep into the realms of data integrity, providing a structured pathway to ensure the quality and reliability of automation testing endeavors. Insights include:

• Definition and importance of data integrity in the landscape of automation testing.
• Exploration of potential risks and threats to data integrity during automation tests.
• Detailed insights into effective data validation strategies ensuring accuracy and consistency of test data.
• Integration of data integrity checks within test automation frameworks for continuous validation.
• Significance of regular data backups and efficient recovery strategies in maintaining data integrity.
• The role of continuous monitoring and auditing in assuring data integrity throughout the testing process.
• Insightful discussion on common challenges and effective solutions in maintaining data integrity in automation tests.