Analytics

80% of organizations use APIs, highlighting the significant adoption rate and an upward trend for greater API usage. APIs can be important for businesses, but creating them from scratch is complex. With the advent of various API generation tools, the task of developing APIs has been simplified. API generation tools automate the creation process of an API based on existing code and specifications, such as pre-defined data sources, functionalities, and access control.

How to use BigQuery vector search to analyze logs and asset metadata stored in BigQuery tables.

Application Programming Interface (API) security is the practice of protecting APIs from attacks. Like databases, servers, and networks, APIs are susceptible to attacks from hackers looking to gain access to sensitive data. Since APIs are more commonly used — to the extent that they’ve become a fundamental part of modern applications — they’ve become a popular target for hackers.

Application Programming Interface (API) is a set of rules or protocols that allows one software application to interact with another. An API integration tool is a software that facilitates the ‘talk’ or communication and data sharing between different software systems and applications. While manual API integration is possible, utilizing API integration tools enhances the efficiency and reliability of this process.

In the age of AI, enterprises are increasingly looking to extract value from their data at scale but often find it difficult to establish a scalable data engineering foundation that can process the large amounts of data required to build or improve models. Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems.

The telecom industry is undergoing an unprecedented transformation. Fueled by tech advancements such as 5G, cloud computing, Internet of Things (IoT) and machine learning (ML), telecoms have the opportunity to reshape and streamline operations and make significant improvements in service delivery, customer experience and network optimization.

The magic of a data fabric architecture lies in its ability to unify data access and integration, enable real-time analytics, enhance governance and security, and boost operational efficiency. It’s not just a tool; it’s a game-changer. For IT leaders in this age of acceleration, understanding the top use cases of data fabric can mean the difference between winning and losing the race for customers and market share.

Digitization efforts across manufacturing have created new opportunities with digital twins, predictive maintenance and cloud systems.

If you’ve ever tried your hand at growth marketing, you know that it takes hard work and experimentation to get consistent results. Plus, you have to constantly adapt your strategy to account for changes in trends, technology, and your audience. This principle especially rings true for software-as-a-service (SaaS) companies, where the market changes quickly. As a SaaS marketer, you might be feeling the pressure of adapting to the latest market trends.

Snowpark in Snowflake is a service that transforms data processing by allowing developers to write code in familiar languages like Python directly within the Snowflake, Inc. platform. The 5 key takeaways from this Snowpark article are: Of all Snowflake features, Snowpark has stood out in the data analytics environment by streamlining data processing and analytics workflows. It enables developers to write code directly within the Snowflake environment with non-SQL languages like Python and Java.