The strong rise of data products in today’s world has made companies introduce new best practices and stricter Service Level Agreements (SLAs) due to their critical functions. Whether these are internal or external-facing data products, experiencing downtime due to data replication issues is a major concern. In the ideal world, there would be no data replication issues, but in reality, they can occur for various reasons, which we’ve outlined below.

Agile development practices were designed to help businesses get to market faster.

Salesforce is the backbone of customer relationship management for countless businesses around the globe. As the world’s leading CRM platform, it enables organizations to streamline operations, automate workflows, and deliver exceptional customer experiences. According to the Salesforce report, the revenue for FY25 is $37.8 billion to $38 billion. For that reason, testing plays an important role in maintaining the functionality, performance, and reliability of Salesforce solutions.

ETL is evolving with trends like AI-driven automation, real-time processing, and cloud-native tools. Businesses leveraging modern ETL solutions, such as Snowflake or Apache Kafka, can process data faster from different sources and reduce errors significantly.

Ensuring quality has become critical in the rapidly changing environment of software development, where applications are constantly evolving, and customer expectations are high. Businesses are increasingly turning to automation testing services to meet this requirement by speeding up testing operations and improving software quality. Teams can now easily automate tests with Robot Framework, a potent tool for QA testing.

Exploring the parallels between piloting a small plane and building a software startup, emphasizing preparation, adaptability, teamwork, and data-driven decision-making for success.

As we head into 2025, Qlik is taking a significant step forward in the evolution of our go-to-market approach by placing an even greater emphasis on our partnerships. This move is aimed at capturing the growing market opportunity in data integration, data quality, analytics and AI.

AI is proving that it’s here to stay. While 2023 brought wonder, and 2024 saw widespread experimentation, 2025 will be the year that manufacturing enterprises get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

Large language models (LLMs) are at the heart of generative AI transformations, driving solutions across industries — from efficient customer support to simplified data analysis. Enterprises need performant, cost-effective and low-latency inference to scale their gen AI solutions. Yet, the complexity and computational demands of LLM inference present a challenge. Inference costs remain prohibitive for many workloads. That’s where SwiftKV and Snowflake Cortex AI come in.

Techniques that support agility have now assumed prominence in project management due to their ability to increase the probability of success. The current statistics reveal that the Agile project success rate is at 64% while the Waterfall project success rate is only at 49%. This is a prominent difference that shows how the Agile team performs successfully in fulfilling several project constraints. In terms of financial considerations, Agile makes a huge difference.