Smartphones literally dominate the world. There are 6.92 billion smartphone users in the world today, which is 85.88% of the world population. That figure is only 49.40% in 2016. Such ubiquity of smartphones demands mobile app developers to deliver high-quality applications at all times to remain competitive and relevant through effective mobile application testing.

Organizations increasingly rely on streaming data sources not only to bring data into the enterprise but also to perform streaming analytics that accelerate the process of being able to get value from the data early in its lifecycle. As lakehouse architectures (including offerings from Cloudera and IBM) become the norm for data processing and building AI applications, a robust streaming service becomes a critical building block for modern data architectures.

In the first three parts of our Inside Flink blog series, we discussed the benefits of stream processing, explored why developers are choosing Apache Flink® for a variety of stream processing use cases, and took a deep dive into Flink's SQL API. In this post, we'll focus on how we’ve re-architected Flink as a cloud-native service on Confluent Cloud. However, before we get into the specifics, there is exciting news to share.

The Q3 Confluent Cloud Launch comes to you from Current 2023, where data streaming industry experts have come together to share insights into the future of data streaming and new areas of innovation. This year, we’re introducing Confluent Cloud’s fully managed service for Apache Flink®, improvements to Kora Engine, how AI and streaming work together, and much more.

REST endpoints that join table data typically allow you to retrieve and combine information from multiple tables or data sources in a single API call. These endpoints enable complex queries and data aggregations, often using JOIN operations, to return a unified dataset, simplifying data retrieval and reducing the need for multiple requests and manual data processing. They are commonly used in database systems and data analytics platforms to efficiently access and manipulate interconnected data.

Generative AI has recently emerged as a groundbreaking technology and businesses have been quick to respond. Recognizing its potential to drive innovation, deliver significant ROI and add economic value, business adoption is rapid and widespread. They are not wrong. A research report by Quantum Black, AI by McKinsey, titled "The Economic Potential of Generative AI”, estimates that generative AI could unlock up to $4.4 trillion in annual global productivity.

Unlocking the full potential of your SAP solution requires the complete trust of your data. Without trust, users will second guess any insights, stalling business progress. Research has pinpointed three key pain points that companies encounter with their SAP data: a prevailing sense of data distrust, a lack of maintenance and data cleansing, and a shortage of skilled users. These pain points not only impede progress but also pose significant roadblocks to migrating to S/4HANA, the future of SAP.

In the fast-paced world of banking and finance, acquisitions and mergers continuously shape industry dynamics. Amid these transformations, ensuring data integrity becomes paramount for financial institutions. The ever-changing regulatory environment, data migrations, and the need for data-driven risk management demand a robust solution to address these challenges.