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    which azure services can you use to create a pipeline for data ingestion and processing?

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    Overview of data ingestion in Azure Data Explorer – Azure Training Series

    Coming to the most critical part, for which we had been preparing until now, the Data Ingestion. There are different tools and ingestion methods used by Azure Data Explorer, each under its own categorized target scenario. We will uncover each of these categories one at a time. Data Ingestion Methods The three main categories under which…

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    Coming to the most critical part, for which we had been preparing until now, the Data Ingestion. There are different tools and ingestion methods used by Azure Data Explorer, each under its own categorized target scenario. We will uncover each of these categories one at a time.

    Data Ingestion Methods

    The three main categories under which the data ingestion method has been classified. They are –

    Ingestion using managed pipelines

    The first one in the series is the ingestion using managed pipelines. ADX offers pipelines using Event Grids, Event Hubs, and IoT Hubs to ingest data, which can be managed from Azure Portal. This is useful in cases where the organization wishes to have the external service perform management that includes data retrieval, monitoring, alerting, manage throttling, etc. In other words, it is used when you would like to have another service manage your data ingestion instead of developing and supporting it yourself.

    Before we actually make use of Event Hub, Event Grid, or IoT Hub for data ingestion into ADX, it is important for us to understand what they are and where they actually fit in the overall data analytics scenario

    Event Hubs

    Azure Event Hubs are big data pipelines. It facilitates the capture, retention, and replay of telemetry and event stream data. There can be multiple concurrent sources that allow the telemetry and event data to be made available to a variety of stream-processing infrastructures and analytics services.

    Event Hubs are capable of receiving and processing millions of events per second and make them available either as data streams or bundled event batches. Event Hubs provide a single solution that enables rapid retrieval of data for near real-time processing.

    Event Hubs have the following characteristics –

    Low latency

    Confirm to at least one delivery

    Capable of receiving and processing millions of events per second

    Event Grids

    Event Grid is event-driven and enables reactive programming. It is an event routing service that uses a publish-subscribe model (the pub-sub), where the publishers emit events, but have no expectation about which events are handled. Subscribers, on the other hand decide which events they want to handle. It simplifies the delivery of events between a publisher and the subscriber.

    They are not a data pipeline as Event Hubs and do not deliver the actual object or in other words it does not include the data transfer, but the notifications of the event that has occurred on the publisher. These notifications are then consumed by the publisher, an Event Hub, which then routes the events to the ADX cluster.

    The can be integrated with third-party services as well. It simplifies event consumption and lowers costs by eliminating the need for constant polling.

    In case there is a need to ingest blobs from your storage account into Azure Data Explorer, you can create an Event Grid data connection, which sets an Azure Data Grid subscription, which helps in routing the events from your storage account to ADX using Event Hubs. In this way you can also chain up the Event Grid and Event Hub.

    For more information on how to setup the ingestion pipeline to ingest blobs from storage account to ADX using Event Grid, routed via Event Hub, click here

    Event Grids have the following characteristics:

    Dynamically scalable

    Low cost Serverless

    Confirm to at least one delivery

    Supports dead-lettering

    You must be thinking of what the dead-lettering is?

    The purpose of the dead-letter queue is to hold messages that cannot be delivered to any receiver, or messages that could not be processed. Messages can then be removed from the DLQ and inspected separately. The dead-letter queue (abbreviated as DLQ) can neither be created, nor it can be deleted or managed separately from the main entity.

    IoT Hubs 

    IoT Hubs are used as pipelines for data transfer from the approved IoT devices to ADX. It is similar to Event Hubs in functionality, wherein, it acts as a central message hub for communications between the IoT devices sending telemetry data and the IoT applications. This data is captured and sent to ADX for data analysis in near real time (NRT).

    Ingestion using connectors and plugins

    Then comes ingestion using connectors and plugins. We have different types of connectors available today like – Power Automate (formerly known as MS-Flow), Kafka, Apache Spark and plugins like Logstash.

    Power Automate (MS Flow)

    This can be used to perform multiple actions when used with Azure Data Explorer. We can use the ADX commands (Kusto Commands) to perform tasks. Some of the tasks that can be performed are:

    Trigger ingestion of data from other databases to Azure and vice-versa

    Create and share ADX reports with tables and charts via emails

    स्रोत : azure-training.com

    Data ingestion with Azure Data Factory

    Learn the available options for building a data ingestion pipeline with Azure Data Factory and the benefits of each.

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    Data ingestion with Azure Data Factory

    Article 08/18/2022 5 minutes to read

    In this article, you learn about the available options for building a data ingestion pipeline with Azure Data Factory. This Azure Data Factory pipeline is used to ingest data for use with Azure Machine Learning. Data Factory allows you to easily extract, transform, and load (ETL) data. Once the data has been transformed and loaded into storage, it can be used to train your machine learning models in Azure Machine Learning.

    Simple data transformation can be handled with native Data Factory activities and instruments such as data flow. When it comes to more complicated scenarios, the data can be processed with some custom code. For example, Python or R code.

    Compare Azure Data Factory data ingestion pipelines

    There are several common techniques of using Data Factory to transform data during ingestion. Each technique has advantages and disadvantages that help determine if it's a good fit for a specific use case:

    Technique Advantages Disadvantages

    Data Factory + Azure Functions

    Low latency, serverless compute

    Stateful functions Reusable functions

    Only good for short running processing

    Data Factory + custom component

    Large-scale parallel computing

    Suited for heavy algorithms

    Requires wrapping code into an executable

    Complexity of handling dependencies and IO

    Data Factory + Azure Databricks notebook

    Apache Spark

    Native Python environment

    Can be expensive

    Creating clusters initially takes time and adds latency

    Azure Data Factory with Azure functions

    Azure Functions allows you to run small pieces of code (functions) without worrying about application infrastructure. In this option, the data is processed with custom Python code wrapped into an Azure Function.

    The function is invoked with the Azure Data Factory Azure Function activity. This approach is a good option for lightweight data transformations.

    Advantages:

    The data is processed on a serverless compute with a relatively low latency

    Data Factory pipeline can invoke a Durable Azure Function that may implement a sophisticated data transformation flow

    The details of the data transformation are abstracted away by the Azure Function that can be reused and invoked from other places

    Disadvantages:

    The Azure Functions must be created before use with ADF

    Azure Functions is good only for short running data processing

    Azure Data Factory with Custom Component activity

    In this option, the data is processed with custom Python code wrapped into an executable. It's invoked with an Azure Data Factory Custom Component activity. This approach is a better fit for large data than the previous technique.

    Advantages:

    The data is processed on Azure Batch pool, which provides large-scale parallel and high-performance computing

    Can be used to run heavy algorithms and process significant amounts of data

    Disadvantages:

    Azure Batch pool must be created before use with Data Factory

    Over engineering related to wrapping Python code into an executable. Complexity of handling dependencies and input/output parameters

    Azure Data Factory with Azure Databricks Python notebook

    Azure Databricks is an Apache Spark-based analytics platform in the Microsoft cloud.

    In this technique, the data transformation is performed by a Python notebook, running on an Azure Databricks cluster. This is probably, the most common approach that uses the full power of an Azure Databricks service. It's designed for distributed data processing at scale.

    Advantages:

    The data is transformed on the most powerful data processing Azure service, which is backed up by Apache Spark environment

    Native support of Python along with data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn

    There's no need to wrap the Python code into functions or executable modules. The code works as is.

    Disadvantages:

    Azure Databricks infrastructure must be created before use with Data Factory

    Can be expensive depending on Azure Databricks configuration

    Spinning up compute clusters from "cold" mode takes some time that brings high latency to the solution

    Consume data in Azure Machine Learning

    The Data Factory pipeline saves the prepared data to your cloud storage (such as Azure Blob or Azure Data Lake).

    स्रोत : learn.microsoft.com

    Data Ingestion Azure Data Factory Simplified 101

    This article talks about Data Ingestion Azure Data Factory in detail. It also explains Data Ingestion and Azure Data Factory briefly.

    Data Ingestion Azure Data Factory Simplified 101

    Manjiri Gaikwad on Azure Data Factory, Data Ingestion • May 12th, 2022 • WRITE FOR HEVO

    As data collection within organizations proliferates rapidly, developers are automating data movement through Data Ingestion techniques. However, implementing complex Data Ingestion techniques can be tedious and time-consuming for developers.

    As a result, to overcome such issues, Microsoft developed Azure Data Factory to help organizations build cost-effective Data Ingestion, ELT (Extract, Load, Transform), and ETL (Extract, Transform and Load) processes with a simple Graphical User Interface.

    You can also monitor your ingested data pipelines and schedule them using Azure Data Factory scheduling features. Azure Data Factory can perform Data Ingestion processes on cloud and on-premises services.

    This article talks about Data Ingestion Azure Data Factory in detail. It also explains Data Ingestion and Azure Data Factory briefly.

    Table Of Contents

    What is Data Ingestion?

    What is Azure Data Factory?

    Key Features of Azure Data Factory

    Understanding Data Ingestion Azure Data Factory

    Data Ingestion Azure Data Factory: Azure Data Factory with Azure functions

    Data Ingestion Azure Data Factory: Azure Data Factory with Custom Component Activity

    Data Ingestion Azure Data Factory: Azure Data Factory with Azure Databricks Notebook

    Data Ingestion Azure Data Factory: Consuming Data in Azure Machine Learning

    Conclusion

    Prerequisites

    Basics understanding of integration

    What is Data Ingestion?

    Data Ingestion moves data from one or more sources to a destination for further processing and analysis. Usually, Data Ingestion is leveraged to bring data from disparate sources like Saas applications into a Data Lake or Data Warehouse, or other storage for consolidating data.

    Data Ingestion has the following benefits:

    Data is Easily Available: Organizations use Data Ingestion processes to collect data from different sources and move it to a unified environment so that data can be easily accessed and further analyzed.Data is Simplified: Due to the advancement in Data Ingestion techniques like ETL (Extract, Transform, Load), data can be quickly transformed into various predefined formats and then sent to the centralized storage.Saves Time: Before Data Ingestion tools, developers or engineers manually performed the Data Ingestion process, which was time-consuming. However, now data engineers can perform Data Ingestion by using no-code solutions to expedite the process.

    Ingest Data in Minutes Using Hevo’s No-Code Data Pipeline

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    What is Azure Data Factory?

    Image Source

    Developed in 2015, Azure Data Factory is a managed cloud service built for extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects. Azure Data Factory allows you to visually integrate data sources with more than 90 built-in, maintenance-free connectors. It enables users to prepare data easily, construct ETL and ELT processes, and monitor pipelines with code-free services.

    Organizations often store unorganized or raw data in relational and non-relational forms. However, these data often do not provide the proper context for meaningful insights. Azure Data Factory provides a platform where all this data is transformed and stored in a centralized location, which organizations can further use for gaining meaningful insights.

    Key Features of Azure Data Factory

    Scalability: Azure Data Factory was developed to handle large amounts of data. It consists of in-built features like parallelism and time slicing that can transfer gigabytes of data in the cloud within a few hours.Built-in Connectors: Azure Data Factory consists of more than 90 built-in connectors to access data from different sources like Amazon Redshift, Google BigQuery, Oracle Exadata, Teradata, Salesforce, Marketo, ServiceNow, and more.Orchestrate, Monitor, and Manage Pipeline Performance: Managing data pipelines becomes difficult and time-consuming with the changing data landscape. With Azure Data Factory, you can monitor your data pipeline by setting up alerts. These alerts appear in the Azure alerts group, which notify users of data pipeline problems.

    What Makes Hevo’s Data Ingestion Process Unique

    स्रोत : hevodata.com

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