Azure data factory

The availability of so much data is one of the greatest gifts of our day. But how does this impact a business when it’s transitioning to the cloud? Will your historic on-premise data be a hindrance if you’re looking to move to the cloud? What is Azure Data Factory (ADF) and how does it solve problems like this? Is it possible to enrich data generated in the cloud by using reference data from on-premise or other disparate data sources? Fortunately, What is Azure Data Factory? Azure Data Factory is a cloud-based data integration service that allows you to create data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. ADF does not store any data itself. It allows you to create data-driven workflows to orchestrate themovement of data between supported data stores and then process the data using compute services in other regions or in an on-premise environment. It also allows you to monitor and manage workflows using both programmatic and UI mechanisms. Azure Data Factory use cases ADF can be used for: • Supporting data migrations • Getting data from a client’s server or online data to an Azure Data Lake • Carrying out various data integration processes • Integrating data from different ERP systems and loading it into Azure Synapse for reporting How does Azure Data Factory work? The Data Factory service allows you to create data pipelines that move and transform data and then run the pipelines on a specified schedule (hourly, d...

In this article APPLIES TO: Azure Data Factory Azure Synapse Analytics In the world of big data, raw, unorganized data is often stored in relational, non-relational, and other storage systems. However, on its own, raw data doesn't have the proper context or meaning to provide meaningful insights to analysts, data scientists, or business decision makers. Big data requires a service that can orchestrate and operationalize processes to refine these enormous stores of raw data into actionable business insights. Azure Data Factory is a managed cloud service that's built for these complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects. Usage scenarios For example, imagine a gaming company that collects petabytes of game logs that are produced by games in the cloud. The company wants to analyze these logs to gain insights into customer preferences, demographics, and usage behavior. It also wants to identify up-sell and cross-sell opportunities, develop compelling new features, drive business growth, and provide a better experience to its customers. To analyze these logs, the company needs to use reference data such as customer information, game information, and marketing campaign information that is in an on-premises data store. The company wants to utilize this data from the on-premises data store, combining it with additional log data that it has in a cloud data store. To extract insights, it hopes to process the joined data by ...

This post was authored by Azure Data Factory (ADF), Synapse pipelines, and Azure Databricks make a rock-solid combo for building your Lakehouse on Azure Data Lake Storage Gen2 (ADLS Gen2). ADF provides the capability to natively ingest data to the Azure cloud from ADF has native integration with Azure Databricks via the Azure Databricks linked service and can execute notebooks, JARs, and Python code activities which enables organizations to build scalable data orchestration pipelines that ingest data from various data sources and curate that data in the lakehouse. The following list describes 5 key practices when using ADF and Azure Databricks. • Metadata Driven Ingestion Patterns ADF can be used to create a • ADF Ingestion to ADLS Landing Zones and Auto Loader or Directly to Delta Lake There are two common, best practice patterns when using ADF and Azure Databricks to ingest data to ADLS and then execute Azure Databricks notebooks to shape and curate data in the lakehouse. • Ingestion using Auto Loader ADF copy activities ingest data from various data sources and land data to landing zones in ADLS Gen2 using CSV, JSON, Avro, Parquet, or image file formats. ADF then executes notebook activities to run pipelines in Azure Databricks using Auto Loader. • Incrementally and efficiently processes new data files as they arrive in ADLS Gen2 • Automatically keeps track of files ingested/processed using • Capable of • Ingestion directly to Delta Lake ADF copy activities can ingest d...

Integrate all your data with Azure Data Factory, a fully managed, serverless data integration service. Visually integrate data sources with more than 90 built-in, maintenance-free connectors at no added cost. Easily construct ETL (extract, transform, and load) and ELT (extract, load, and transform) processes code-free in an intuitive environment or write your own code. Then deliver integrated data to Azure Synapse Analytics to unlock business insights. Data Factory provides a data integration and transformation layer that works across your digital transformation initiatives. • Enable citizen integrators and data engineers to drive business and IT-led Analytics/BI. • Prepare data, construct ETL and ELT processes, and orchestrate and monitor pipelines code-free. The managed Apache Spark™ service takes care of code generation and maintenance. • Transform faster with intelligent intent-driven mapping that automates copy activities. Azure Data Factory can help organizations looking to modernize SSIS. • Gain up to 88 percent cost savings with Azure Hybrid Benefit. • Enjoy the only fully compatible data integration service that makes it easy to move all your SSIS packages to the cloud. • Learn how easy migration is with the • Realize your vision for hybrid big data and data warehousing initiatives by using Data Factory cloud data pipelines. Ingesting data from diverse and multiple sources can be expensive and time consuming and may require multiple solutions. Azure Data Factory o...

Azure Data Factory documentation Azure Data Factory is Azure's cloud ETL service for scale-out serverless data integration and data transformation. It offers a code-free UI for intuitive authoring and single-pane-of-glass monitoring and management. You can also lift and shift existing SSIS packages to Azure and run them with full compatibility in ADF. SSIS Integration Runtime offers a fully managed service, so you don't have to worry about infrastructure management.

Search Search Series: Beginner's Guide to Azure Data Factory Welcome to this Beginner’s Guide to Azure Data Factory! In this series, I’m going to cover the fundamentals of Azure Data Factory in casual, bite-sized blog posts that you can read through at your own pace and reference later. You may not be new to ETL, data integration, Azure, or SQL, but we’re going to start completely from scratch when it comes to Azure Data Factory. How do you get started building data pipelines? What if you need to transform or re-shape data? How do you schedule and monitor your data pipelines? Can you make your solution dynamic and reusable? Join me in this Beginner’s Guide to Azure Data Factory to learn all of these things - and maybe more. 🤓 Let’s go! • • • • • • • • • • • • • • • • • • • • • • • • • • P.S. This series will always be a work-in-progress. Yes, always. Azure changes often, so I keep coming back to tweak, update, and improve content. I just might not be able to do it right away! Post 1 of 26 in Hi! I’m Cathrine 👋🏻 I really like Azure Data Factory. It’s one of my favorite topics, I can talk about it for hours. But talking about it can only help so many people - the ones who happen to attend an event where I’m presenting a session. So I’ve decided to try something new… I’m going to write an introduction to Azure Data Factory! And not just one blog post. A whole bunch of them. I’m going to take all the things I like to talk about and turn them into bite-sized blog posts that you...