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Summary Databases and analyticsarchitectures have gone through several generational shifts. Your first three Miro boards are free when you sign up today at [dataengineeringpodcast.com/miro]([link] Support Data Engineering Podcast Summary Databases and analyticsarchitectures have gone through several generational shifts.
Result: Hadoop & NoSQL frameworks emerged. Image by the author 2010 to 2020 - The Cloud Data Warehouse Enterprises now wanted quick data analytics without yesterday’s constraints of flexibility, processing power and scale. New data formats emerged — JSON, Avro, Parquet, XML etc.
One can use polybase: From Azure SQL Database or Azure Synapse Analytics, query data kept in Hadoop, Azure Blob Storage, or Azure Data Lake Store. Use a few straightforward T-SQL queries to import data from Hadoop, Azure Blob Storage, or Azure Data Lake Store without having to install a third-party ETL tool.
Some of the most common responsibilities of data engineers include Data collection Matching the architecture to the business his needs Discovering tasks that can be automated using data Using advanced analytics programs, machine learning, and statistical techniques Updating stakeholders based on analyticsArchitecture development, building, testing, (..)
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