How to handle azure data factory lookup activity with more than 5000 records
Hello Experts, The DataFlow Activity successfully copies data from an Azure Blob Storage .csv file to Dataverse Table Storage. However, an error occurs when performing a Lookup on the Dataverse due to excessive data. This issue is in line with the documentation, which states that the Lookup activity has a limit of 5,000 rows and a maximum size of 4 MB. Also, there is a Workaround mentioned (Micrsofot Documentation): Design a two-level pipeline where the outer pipeline iterates over an inner pipeline, which retrieves data that doesn't exceed the maximum rows or size. How can I do this? Is there a way to define an offset (e.g. only read 1000 rows) Thanks, -SriAzureFunBytes Reminder - @Azure Data Factory Security with @narainabhishek - 5/20/2021
AzureFunBytes is a weekly opportunity to learn more about the fundamentals and foundations that make up Azure. It's a chance for me to understand more about what people across the Azure organization do and how they do it. Every week we get together at 11 AM Pacific on Microsoft LearnTV and learn more about Azure. This is part two of our series on Azure Data Factory. Last time Mark helped get us on the road to understanding how to best get our data into the cloud by using the linked services and tools with Azure Data Factory. Data Factory contains a series of interconnected systems that provide a complete end-to-end platform for data engineers. Utilizing our data requires some thoughtfulness when it comes to security. This week on AzureFunBytes, Senior Program Manager, Abishek Narain will help us learn more about security best practices for Data Engineers. When securing your data pipeline, there are some configurations and settings suggested by Azure you should follow. You'll want to follow this security baseline that applies guidance from the Azure Security Benchmark version1.0 to Azure Data Factory. You'll also want to ensure login details to your data endpoints are protected to ensure there's no unauthorized access due to credentials existing in the wild. There are also network security considerations that you will want to adhere to for various data stores that are accessed by Azure Data Factory, whether they are in the cloud or on-prem. When: May 20, 2021 11 AM Pacific / 2 PM Eastern Where: Microsoft LearnTV Our Agenda: Authentication Meta-data encryption (Meta-data at rest) Credential management Data in transit Data at rest Network Security Azure Policy integration From the Azure Documentation on security considerations: Azure Data Factory including Azure Integration Runtime and Self-hosted Integration Runtime does not store any temporary data, cache data or logs except for linked service credentials for cloud data stores, which are encrypted by using certificates. With Data Factory, you create data-driven workflows to orchestrate movement of data between supported data stores, and processing of data by using compute services in other regions or in an on-premises environment. You can also monitor and manage workflows by using SDKs and Azure Monitor. So let's connect and collect together and talk about big data security! We'll take questions from the audience and try to understand how all the pieces fit together to gain important insights that drive our businesses. Learn about Azure fundamentals with me! Live stream is available on Twitch, YouTube, and LearnTV at 11 AM PT / 2 PM ET Thursday. You can also find the recordings here as well: AzureFunBytes on Twitch AzureFunBytes on YouTube Azure DevOps YouTube Channel Follow AzureFunBytes on Twitter Useful Docs: AzureFunBytes Episode 43 - Intro to @Azure Data Factory with @KromerBigData Get $200 in free Azure Credit Microsoft Learn: Introduction to Azure fundamentals Azure security baseline for Azure Data Factory Security considerations for data movement in Azure Data Factory What is Data Factory? Data access strategies Azure Key Vault secrets in pipeline activities Azure Policy documentation Compute environments supported by Azure Data Factory
