Incorporating Real-Time Data Pipelines Using Snowflake and dbt
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P108Keywords:
Real-time data pipelines, Snowflake, dbt, cloud-native architecture, data storage, data transformation, data ingestion, data processing, analytics workflows, data-driven decisions, data quality, real-time analysis, cloud technologies, data collaboration, scalable data workflows, agile reportingAbstract
Companies are increasingly trying to make these decisions right away by using actual time data pipelines in their operations in a data-driven world. Snowflake, a cloud-based data warehouse, and dbt (data build tool), a transformation tool, are key to this change because they provide scalable and too many efficient ways to handle and analyze huge amounts of information. This article speaks about how these data pipelines that work in the actual time are becoming more and more significant and what Snowflake and dbt do in the latest system. By employing Snowflake's flexible, cloud-native architecture and data transformation tools to manage huge volumes of information, businesses can make their data processing faster, more efficient and easier to access. The research looks at the pros and cons of employing these kinds of technology, such as how they may help save money, improve the growth of a business and make it easier to get to the information. It also talks about other possible problems, such data latency and the problems that come with putting these kinds of systems together. It looks at the best ways to set up actual time data pipelines, such as making sure the information is of high quality, making it easier to scale and speeding it up. The article shows businesses how to improve their data architecture and stresses how these technologies may help with their business intelligence and making these decisions. It also talks about possible future trends in the actual time data processing, such as advances in AI-driven analytics and automation. This in-depth study aims to provide more businesses the knowledge they need to add more successfully Snowflake and debt to their actual time data pipelines, allowing them to stay more competitive in a world that is becoming more data-driven
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