It's essential to understand data warehousing depending on your requirements and business. Hence, people are opting for the BigQuery/Snowflake course to understand data warehousing. Here, we are going to compare the two topmost data warehouses: BigQuery and Snowflake. Let's move right into knowing them. BigQuery is a serverless and fully managed data warehouse that can enable a scalable analysis of the data.
Streaming data is nice and all, especially with the growth of Internet of Things (IoT) technology, and all the data thrown off by the sensors in IoT devices. And, yes, a growing number of streaming data platforms can "land" their data into cloud storage repositories like Amazon Simple Storage Service (S3).
Cloud data warehouse player Snowflake Computing is today announcing availability of its platform on Microsoft's Azure cloud. Heretofore available only on Amazon Web Services (AWS), the company now offers the first major multi-cloud data warehouse. That Snowflake started off on AWS is somewhat ironic, given the company's Microsoft DNA. For example, Snowflake CEO Bob Muglia once led the Server and Tools Business (the precursor to today's Cloud and Enterprise division) at Microsoft. And Soma Somasegar, Managing Director at Madrona Venture Group, which participated in Snowflake's $100M Series D funding round last year, led Microsoft's developer division for many years.
According to Gartner, the Public Cloud market is poised to grow further in 2020 even as CIOs continue to place big bets on IaaS to push their digital transformation goals. Cloud spending will remain the number one priority -- an outcome of surging demand for remote Cloud management services and virtualization tools due to coronavirus. The year 2020 will be remembered for some really amazing innovations, tech partnerships and collaborations in the fields of AI Machine Learning, IoT, Cloud Computing and Virtualization. While IPOs always attract incredible responses from the technology investors, this year's biggest IPO launch will be remembered for the way it holds the potential to change Data Science industry forever. Yes, we are pointing at the recently announced Snowflake IPO.
Snowflake and Qubole have partnered to bring a new level of integrated product capabilities that make it easier and faster to build and deploy machine learning (ML) and artificial intelligence (AI) models in Apache Spark using data stored in Snowflake and big data sources. In this second blog of three we cover how to perform advanced data preparation with Apache Spark to create refined data sets and write the results to Snowflake, thereby enabling new analytic use cases. The blog series covers the use cases directly served by the Qubole–Snowflake integration. The first blog discussed how to get started with ML in Apache Spark using data stored in Snowflake. Blogs two and three cover how data engineers can use Qubole to read and write data in Snowflake, including advanced data preparation, such as data wrangling, data augmentation, and advanced ETL to refine existing Snowflake data sets.