In this video, we discuss some newer features of Snowflake called Snowpipe Streaming and Dynamic Tables.
➡️ Snowflake enables organizations to be data-driven by offering an expansive set of features for creating performant, scalable, and reliable data pipelines that feed dashboards, machine learning models, and applications.
➡️ But before data can be transformed and served or shared, it must be ingested from source systems. The volume of data generated in real time from application databases, sensors, and mobile devices continues to grow exponentially. While data generated in real time is valuable, it is more valuable when paired with historical data that provides context.
➡️ That proves to be a difficult task for data engineering teams that have to manage separate infrastructure for batch data and streaming data.
➡️ Streaming ingestion is not meant to replace file-based ingestion, but rather to augment it for data loading scenarios where it makes sense, such as:
►Low-latency telemetry analytics of user-application interactions for clickstream recommendations
►Identification of security issues in real-time streaming log analytics to isolate threats
►Stream processing of information from IoT devices to monitor critical assets
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Here, we're all about helping you extract the maximum amount of business value from your data. As a data engineer and solution architect, I understand the struggles of finding practical and high-value advice on Snowflake capabilities.
On this channel, I share my personal experiences, proven approaches, and best practices for designing and implementing Snowflake's capabilities. I also provide case studies and examples to show how it works for you.
➡️ My aim is to help you become a Snowflake expert and avoid costly pitfalls along the way. I'm all about providing you with robust, real-world advice based on my unique experiences in the field.
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