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Big Data Architectures

Data Cloud for Dummies Cloud Data Lake for Dummies

Big data architectures act as the design blueprint for big data infrastructure and solutions.

Big data architectures are created by data architects before a big data solution is built and deployed. They take into account the unique needs, structure, and data sources of the organization. 

The architecture of big data is concerned with these key functions: data ingestion (data streams, data ingestion patterns), data storage, data processing (batch, real-time), and lastly, data consumption/use by internal stakeholders (analytics, machine learning, data sharing, data exchange).

A well-designed big data architecture can save your company money and help you predict future trends so you can make good business decisions.

Benefits of Big Data Architectures

Data volume has been growing exponentially every year. In the IoT age, streaming data sources are multiplying at a rapid rate and bringing huge volumes of semi-structured and unstructured data into the mix. And then there is the question of how to best utilize all this data to glean business insights. These data trends are reinforcing the need for organizations to adhere to data architecture best practices. Given its inherent flexibility, scalability, and security, cloud data architecture has become the first choice for data-driven organizations.that want to reduce costs, speed decision making, and predict future trends. 

Snowflake’s architecture offers full relational database support for structured, semi-structured, and unstructured data, including JSON, Avro, Parquet, etc.; all within a single, logically integrated, highly scalable solution. The cloud data platform works in tandem AWS, Microsoft Azure, and Google Cloud data services to provide a complete, elastic platform for data management, storage, and analysis. 

With strong built-in security and compatibility with popular ETL and BI tools, Snowflake enables data leaders to support multiple data lake, data warehouse, data engineering, or data science workloads with virtually unlimited concurrency.