Snowflake json query2/19/2023 In this blog, we will understand the mechanism used to process JSON and how its data can be utilized for Data Analysis by executing analytical SQL Queries with the following Cloud Service/Frameworks,īefore going into processing mechanism, let’s shallow dive into JSON. JSON is one type of semi-structured data & it can be generated from many sources like smart devices or Rest API calls or in response to an event or request. Semi-structured data is data with nested data structures and the lack of a fixed schema & contains semantic tags or other types of mark-ups that identify individual and distinct entities within the data. Let’s understand what we mean when we use the term ‘ Semi-structured data’. Semi-structured ( such as JSON, Avro, ORC, Parquet and XML ).Structured (Data in Tabular format such as csv etc.Spark & Snowflake both, have capabilities to perform data analysis on different kinds of data like, On the other hand, Snowflake is a data warehouse that uses a new SQL database engine with a unique architecture designed for the cloud such as AWS and Microsoft Azure. In the Big Data world, Apache Spark is an open-source, scalable, massively parallel, in-memory execution, distributed cluster-computing framework which provides faster and easy-to-use analytics along with capabilities like Machine Learning, graph computation and stream processing using programming languages like Scala, R, Java and Python. Before we delve deeper into the differences between processing JSON in Spark vs Snowflake, let’s understand the basics of Cloud Service/Framework.
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