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With Great Power Comes Great Simplicity : Real-time Data with Snowflake
Snowflake’s leadership is dedicated to simplicity. As emphasized in the opening remarks of his recent summit keynote, Snowflake’s new CEO, Sridhar Ramaswamy reiterated the core principles of the platform: “Snowflake is one platform built on top of one engine, that just works.” This focus on simplicity is a cornerstone of Snowflake’s philosophy, making it a powerful tool for businesses. The Beauty of Simplicity Snowflake’s commitment to simplicity is more than just a design choice—it’s a strategic advantage. By offering a unified platform that avoids unnecessary complexity, Snowflake allows businesses to streamline their data operations. This simplicity translates to easier implementation, faster adoption, and fewer headaches for IT teams and data analysts alike. Massive Investments in Real-time Capabilities However, Snowflake hasn’t stopped at simplicity. The company has also made significant investments in real-time data capabilities. Features like Snowpipe for streaming data ingestion and dynamic tables for real-time transformations highlight Snowflake’s dedication to staying at the cutting edge of data technology. The Real-time Data Challenge Despite these advancements, utilizing Snowflake for real-time data in a heterogeneous operational landscape can be complex. Integrating data from diverse sources—ranging from social media and IoT sensors to legacy databases—requires sophisticated tools and expertise. This complexity can
Five Important Trends of the 2023 Data and Analytics Landscape
Our CEO shares his thoughts on the 2023 most defining trends of the data and analytics market.
Setting up a Business-driven Data Program at a Large Car Manufacturer
Discover how Snowpipe Streaming and Digazu create an end-to-end solution for real-time data integration to Snowflake.
Data Products: A Business Analyst Exploring Data in Self-service
In the previous piece, we uncovered the different characteristics of a well-designed data product. In this second blog, we will illustrate a real case scenario that tells the story of a data analyst, “Fred”, trying to navigate the complexities of his data landscape using our data product platform.
The 8 Characteristics of a Successful Data Product
The data mesh architecture is built around four fundamental concepts, the second of which is “data-as-a-product” often just called data products.
IT-powered innovations, the Internet of Things and new generation customer demands are fueling real-time data creation. In this new data landscape, businesses from all industries are quickly shifting from batch processing to real-time data streams to match the new imperatives. In this blog, our marketing analyst, Selima Triki, unpacks the concept of real time and real-time data, some of its benefits and applications.
No-code/low-code and Virtualisation in Data Engineering
In this blog, we uncover the multiple facets of data engineering, what it is and how complex it can be. Digazu’s product manager, Luc Berrewaerts walks us through the new technologies to navigate the complexities of data engineering.
Six Important Trends of the 2022 Data and Analytics Landscape
Our CEO shares his thoughts on the 2022 most defining trends of the data and analytics market.
Migrating a Thousand Data Pipelines Thanks to Code as Configuration
A leading investment bank approached us recently to review their data management to support new analytics use cases. Learn about how we helped them enhance their data processes through automation and scalability.
The Evolution of Data Architectures
Learn the various ways in which you can store your data and choose the one that best suits your needs. This article will underline the difference between data warehouse, data lake and data mesh while exposing the benefits and drawbacks that you could encounter. Find the best solution no matter how mature your data is