The dataset you choose should represent your typical data patterns along the following attributes:Īnd your dataset should be big enough to evaluate copy performance. Pick up a test dataset and establish a baseline.ĭuring development, test your pipeline by using the copy activity against a representative data sample. Take the following steps to tune the performance of your service with the copy activity: Scale out to multiple machines ( up to 4 nodes), and a single copy activity will partition its file set across all nodes.Ī single copy activity reads from and writes to the data store using multiple threads in parallel.When using self-hosted IR, you can take either of the following approaches:.When using Azure integration runtime (IR), you can specify up to 256 data integration units (DIUs) for each copy activity, in a serverless manner.You should use the numbers obtained in your performance tuning tests for production deployment planning, capacity planning, and billing projection.Ĭontrol flow can start multiple copy activities in parallel, for example using For Each loop.Ī single copy activity can take advantage of scalable compute resources. We recommend you to follow steps laid out in Performance tuning steps to optimize copy performance for your specific dataset and system configuration. The duration provided below are meant to represent achievable performance in an end-to-end data integration solution by using one or more performance optimization techniques described in Copy performance optimization features, including using ForEach to partition and spawn off multiple concurrent copy activities. The duration in each cell is calculated based on a given network and data store bandwidth and a given data payload size. The table below shows the calculation of data movement duration. Network bandwidth in between the source and destination data stores.
#RAPIDCOPY COPY IF IDFFERENT SIZE DAT FULL#
This full utilization means you can estimate the overall throughput by measuring the minimum throughput available with the following resources: Source or destination data store input/output operations per second (IOPS) and bandwidth.Network bandwidth between the source and destination data stores.These pipelines fully utilize the following resources: This architecture allows you to develop pipelines that maximize data movement throughput for your environment. Copy performance and scalability achievable using Azure Data Factory and Synapse pipelinesĪzure Data Factory and Synapse pipelines offer a serverless architecture that allows parallelism at different levels. If you aren't familiar with the copy activity in general, see the copy activity overview before you read this article. What other external factors to consider when optimizing copy performance?.What performance optimizations can I utilize for a single copy activity run?.What steps should I take to tune the performance of the copy activity?.What level of performance and scalability can I achieve using copy activity for data migration and data ingestion scenarios?.These advantages are an excellent fit for data engineers who want to build scalable data ingestion pipelines that are highly performant.Īfter reading this article, you will be able to answer the following questions: In each case, it is critical to achieve optimal performance and scalability.Īzure Data Factory and Azure Synapse Analytics pipelines provide a mechanism to ingest data, with the following advantages: Other times you want to ingest large amounts of data, from different sources into Azure, for big data analytics.
Sometimes you want to perform a large-scale data migration from data lake or enterprise data warehouse (EDW), to Azure.
0 kommentar(er)
