Then you can create a pipeline to move data from your local server up to Azure Storage. Azure Data Factory (ADF) has a gateway that you can install on your local server.To perform loads on a regular basis with AzCopy, test the network speed to see if it is acceptable. This works if your data sizes are less than 10 TB. AzCopy utility moves data to Azure Storage over the public internet.The connections offer more reliability, faster speeds, lower latencies, and higher security than typical connections over the public internet. ExpressRoute connections do not route data through the public internet. ExpressRoute is a service that routes your data through a dedicated private connection to Azure. Azure ExpressRoute service enhances network throughput, performance, and predictability.Tools and services you can use to move data to Azure Storage: PolyBase and the COPY statement can load from either location. In either location, the data should be stored in text files. To land the data in Azure storage, you can move it to Azure Blob storage or Azure Data Lake Store Gen2. Land the data into Azure Blob storage or Azure Data Lake Store If you're exporting from SQL Server, you can use the bcp command-line tool to export the data into delimited text files. PolyBase and the COPY statement can also load data from Gzip and Snappy compressed files.Įxtended ASCII, fixed-width format, and nested formats such as WinZip or XML aren't supported. In addition to delimited text or CSV files, it loads from the Hadoop file formats such as ORC and Parquet. With PolyBase and the COPY statement, you can load data from UTF-8 and UTF-16 encoded delimited text or CSV files. The goal is to move the data into supported delimited text or CSV files. Getting data out of your source system depends on the storage location. Insert the data into production tables.įor a loading tutorial, see loading data from Azure blob storage.Load the data into staging tables with PolyBase or the COPY command.Land the data into Azure Blob storage or Azure Data Lake Store.Extract the source data into text files.The basic steps for implementing ELT are: What is ELT?Įxtract, Load, and Transform (ELT) is a process by which data is extracted from a source system, loaded into a dedicated SQL pool, and then transformed. For the most flexibility when loading, we recommend using the COPY statement. With PolyBase and the COPY statement, you can access external data stored in Azure Blob storage or Azure Data Lake Store via the T-SQL language. While dedicated SQL pools support many loading methods, including popular SQL Server options such as bcp and the SqlBulkCopy API, the fastest and most scalable way to load data is through PolyBase external tables and the COPY statement. Using an Extract, Load, and Transform (ELT) process leverages built-in distributed query processing capabilities and eliminates the resources needed for data transformation prior to loading. Synapse SQL, within Azure Synapse Analytics, uses distributed query processing architecture that takes advantage of the scalability and flexibility of compute and storage resources. Traditional SMP dedicated SQL pools use an Extract, Transform, and Load (ETL) process for loading data.
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