Systems | Development | Analytics | API | Testing

While Salesforce is a much celebrated CRM tool in the industry, it’s no perfect tool just like any others. It offers limited flexibility for on-the-fly data manipulation compared to Excel. Salesforce can be complex to set up and use effectively, particularly for users without technical expertise. It often requires training or support from Salesforce administrators to fully utilize its capabilities.

Ramkumar Nottath, the Senior Solutions Architect at AWS, beautifully puts it. And that’s where low-code or no-code ETL platforms can help—to make the data consumable and democratize it. In this blog, I explain low-code vs. no-code from my experience.

In 24.2, we introduced our enterprise copilot. Enterprise copilot allows you to upload business documents and collect them in knowledge sets. From there, you can ask questions about information in these documents and receive answers quickly. For instance, an organization with a heavy regulatory burden could upload legislative and operational documents. Then, these employees could get insights from Appian AI Copilot to ensure they adhere to compliance requirements.

In today's fast-paced world of media publishing, keeping up with technological advancements and changing consumer preferences is no easy task. Tight budgets, fierce competition and evolving audience behaviors add to the pressure, creating what's often termed the "content crash" — a saturation of content that makes it hard for publishers to stand out. But amidst these challenges, there's a beacon of hope: generative AI.

Today, we are excited to announce the public preview of Snowflake Cortex Analyst. Cortex Analyst, built using Meta’s Llama and Mistral models, is a fully managed service that provides a conversational interface to interact with structured data in Snowflake. It streamlines the development of intuitive, self-serve analytics applications for business users, while providing industry-leading accuracy.

How to leverage Fivetran and Starburst to supercharge your data lakehouse with end-to-end data pipelines.

Data is the driving force of the world’s modern economies, but data teams are struggling to meet demand to support generative AI (GenAI), including rapid data volume growth and the increasing complexity of data pipelines. More than 88% of software engineers, data scientists, and SQL analysts surveyed say they are turning to AI for more effective bug-fixing and troubleshooting. And 84% of engineers who use AI said it frees up their time to focus on high-value activities.

As compute gets increasingly powerful, the fact of the matter is: most AI workloads do not require the entire capacity of a single GPU. Computing power required across the model development lifecycle looks like a normal bell curve – with some compute required for data processing and ingestion, maximum firepower for model training and fine-tuning, and stepped-down requirements for ongoing inference.

Performance testing is a form of software testing that focuses on how a system running the system performs under a particular load. This type of test is not about finding software bugs or defects. Different performance testing types measures according to benchmarks and standards. Performance testing gives developers the diagnostic information they need to eliminate bottlenecks. In this article you will learn about.

With organizations prioritizing data-driven decision-making, the amount of collected and stored data is reaching historic highs. Meanwhile, organizations are democratizing access across all functions to convert this data into actionable insights. Since more users will work with sensitive data, ensuring secure access is more important than ever. Organizations must regulate and maintain the relationship between their data assets and users. Why?