Azure Data Ingestion Overview and Key Features

Azure Data Ingestion is a cloud-based service that allows you to collect and process data from various sources, making it a crucial part of any data-driven strategy.

It supports a wide range of data sources, including IoT devices, social media, and on-premises systems, as mentioned in the "Data Sources and Integration" section.

With Azure Data Ingestion, you can collect data from various formats, such as JSON, CSV, and Avro, as described in the "Data Formats and Schema" section.

Azure Data Ingestion also provides a scalable and secure way to handle high volumes of data, as explained in the "Scalability and Security" section.

This makes it an ideal solution for organizations looking to harness the power of their data and gain valuable insights, as discussed in the "Benefits of Azure Data Ingestion" section.

Data ingestion is a crucial part of any data pipeline, and Azure offers a variety of methods to get data into your system. One of the main ingestion methods is using the Apache Spark connector, which supports every format supported by the Spark environment and has a maximum file size of unlimited.

The Spark connector is a great choice for existing pipelines, preprocessing on Spark before ingestion, and creating a safe (Spark) streaming pipeline from various sources. However, consider the cost of the Spark cluster, as well as comparing it with other methods like Azure Data Explorer data connection for Event Grid or Spark streaming.

Azure Data Factory (ADF) is another popular ingestion method, which supports formats that are unsupported, such as Excel and XML, and can copy large files from over 90 sources. ADF uploads all data to memory and then begins ingestion, making it a relatively slower method.

Event Grid, on the other hand, is a continuous ingestion method from Azure storage or external data in Azure storage, with a maximum file size of 1 GB uncompressed. Ingestion can be triggered by blob renaming or blob creation actions.

Here's a quick comparison of some of the main ingestion methods:

In addition to these methods, there are also other options like Get data experience, IoT Hub, Kafka connector, Kusto client libraries, LightIngest, Logic Apps, and LogStash, each with their own strengths and considerations.

Azure offers a variety of services for ingestion, including Azure Data Factory, which can be used to ingest data from various sources such as SQL Server, Oracle, and MongoDB.

Azure Data Factory supports over 80 data sources, making it a versatile option for ingestion.

Azure Event Hubs is another service that can be used for high-volume ingestion of streaming data from sources such as IoT devices, social media, and log files.

Functions and Logic Apps are powerful tools that can simplify complex data integration and orchestration challenges. With serverless Functions, developers can create custom event-driven serverless code that can solve these challenges.

You can call Functions from both Databricks notebooks and Data Factory pipelines, making it easy to integrate them into your existing workflows. Functions can be used to create custom event-driven serverless code that can solve complex data integration and orchestration challenges.

Logic Apps make it easy to create and run automated workflows that integrate your apps, data, services, and systems. With Logic Apps, you can quickly develop highly scalable integration solutions.

Azure's IoT Hub and Event Hub are cloud service offerings that can ingest large volumes of low-latency and high-reliability, real-time device data into the Lakehouse.

Azure IoT Hub connects IoT devices to Azure resources and supports bi-directional communication capabilities between devices. IoT Hub uses Event Hubs for its telemetry flow path.

Event Hub is designed for high-throughput data streaming of billions of requests per day. It's a great fit for Stream Analytics and Databricks Structured Streaming architectures.

The following table lists the differences in capabilities between Event Hubs and IoT Hubs.

Hevo is a no-code platform that simplifies data migration needs in just a few clicks. Its user-friendly interface makes it easier to use than Azure Data Factory's steeper learning curve.

Hevo offers seamless integration with 150+ data sources, providing a smoother experience than ADF's integration limitations. This means you can easily connect with various data sources without much hassle.

With Hevo, you get clear, flat pricing tiers that simplify cost management, unlike ADF's opaque and potentially costly model. This makes it easier for smaller businesses to manage their costs.

Hevo provides strong, accessible support across all tiers, ensuring that all businesses receive the needed assistance. This is in contrast to ADF's inadequate customer support.

Hevo offers greater flexibility and ease of use than ADF, which can be complex due to the requirement of Python expertise. This makes Hevo a more accessible option for businesses of all sizes.

Here are some key benefits of using Hevo:

To set up Kafka Connect for Azure Data ingestion, you'll need to create a custom Docker image with the Azure Data Explorer connector. This can be done by using the Strimzi Kafka Docker image as a base and adding the Azure Data Explorer connector JAR file to the plugin path.

You can download the connector JAR file and use a Dockerfile to build the custom image. Alternatively, you can use a pre-built image available on Docker Hub, such as abhirockzz/adx-connector-strimzi:1.0.1.

To install Kafka Connect, you'll need to define a KafkaConnect resource that includes the externalConfiguration attribute, which points to a secret that contains the connector configuration. This secret is loaded into the Kafka Connect Pod as a Volume, and the Kafka FileConfigProvider is used to access it.

The Strimzi Entity Operator can be used to create the topic and install the connector. To create the topic, you can use the following command: kubectl get kafkatopic. This will display the topic you just created, as well as any internal Kafka topics.

Here are the key attributes to configure when installing the connector:

* /opt/kafka/external-configuration is a fixed path inside the containeradx-auth-config is the name of the volume in the KafkaConnect definitionadx-auth.properties is the name of the file as defined in the SecretappID is the name of key

The flush.size.bytes and flush.interval.ms attributes work in tandem with each other to serve as a performance knob for batching. You can refer to the connector GitHub repo for details on these and other configuration parameters.

Security and authentication are crucial when working with Azure Data Explorer. To authenticate, you need to create an Azure Service Principal using the az ad sp create-for-rbac command.

You'll receive a JSON response with the appId, password, and tenant values, which you'll need to note down for subsequent steps. To assign an admin role to the Service Principal, you can use the Azure portal or a command in your Data Explorer cluster.

Here are the key properties you'll need to reference from the Service Principal:

These values will be used to seed the auth-related config as a Kubernetes Secret, which will be referenced later in the process.

Authentication is a crucial step in connecting to Azure Data Explorer. To authenticate, you'll need to create an Azure Service Principal using the az ad sp create-for-rbac command, which will provide you with a JSON response containing the appId, password, and tenant.

You'll need to note down these values as you'll be using them in subsequent steps. Next, you'll need to assign an admin role to the Service Principal, either through the Azure portal or by using a command in your Data Explorer cluster.

To seed the auth-related config as a Kubernetes Secret, you'll create a file called adx-auth.yaml with specific contents. You'll need to replace values for kustoURL, tenantID, appID, and password, which are obtained from the Service Principal.

To reference these properties in your configuration, you can use placeholders like {{ .Values.adx.kustoURL }}.

Here are the placeholders for the Service Principal properties:

To create a new table, you'll need at least Database User permissions. This is the minimum requirement to perform this action.

Database Ingestor permissions are required to ingest data into an existing table without changing its schema. This permission level ensures that data can be added to a table without compromising its integrity.

To change the schema of an existing table, you'll need either Table Admin or Database Admin permissions. These higher-level permissions grant the necessary authority to modify a table's structure.

Here's a breakdown of the required permissions for various ingestion scenarios:

For more information on Kusto role-based access control, be sure to check out the official documentation.

Configuring and Managing Azure Data Ingestion involves setting up the infrastructure to handle large volumes of data. Azure Data Explorer offers direct ingestion management commands for exploration and prototyping.

To configure ingestion, you can use the .ingest inline command, which contains the data to ingest within the command text itself. This method is intended for improvised testing purposes. Alternatively, you can use ingest from query, which indirectly specifies the data to ingest as the results of a query or a command.

Ingestion can be retried for up to 48 hours in the event of a failure, using the exponential backoff method for wait time between tries. This ensures that your data is ingested even if there are temporary issues with the process.

To manage ingestion, you can configure the export of Activity Logs and Diagnostic Logs to an Event Hub. This allows you to monitor and manage your data ingestion process. By following the steps outlined in the Azure Data Explorer documentation, you can set up a new database and configure the tables for log and monitor data.

To configure ingestion to Azure Data Explorer, you need to configure the export of Activity Logs and Diagnostic Logs to an Event Hub. This involves setting up the Event Hub activity_logs and diagnostic_logs.

The first step is to create a new database in your Azure Data Explorer cluster, which is called AzureMonitor with the default settings.

To prepare the tables for log and monitor data, you need to open the query editor and create the tables. The initial table for the Diagnostic Logs only contained one column called RawRecords to understand the structure of the Diagnostic Logs.

Here are the supported data formats for ingestion into Azure Data Explorer:

To ingest historical data, you should follow the guidance in the Ingest historical data document. If you're ingesting data from an unsupported format, you can integrate with Azure Data Factory or write your own custom code using the Kusto client libraries.

To integrate with Azure Data Factory, you can use the Copy data to Azure Data Explorer by using Azure Data Factory article. To write custom code, you can use the Kusto client libraries available for C#, Python, Java, JavaScript, TypeScript, and Go.

Direct Management is a key aspect of configuring and managing your Azure Data Explorer cluster.

You can ingest data directly into your cluster using management commands, which is ideal for exploration and prototyping purposes.

These commands, such as .ingest inline, .set, .append, .set-or-append, and .set-or-replace, allow you to ingest data from various sources, including queries and external storage.

The .ingest inline command is meant for improvised testing purposes and contains the data to ingest as part of the command text itself.

The .ingest into command gets data from external storage, such as Azure Blob Storage, that's accessible by your cluster.

In the event of a failure, ingestion is retried for up to 48 hours using the exponential backoff method for wait time between tries.

Here are the main ingestion management commands you can use:

The default ingestion method in Azure Data Explorer uses queued ingestion, which can result in an ingestion latency of up to 5 minutes.

This is because the ingestion batches are defined by three configuration parameters: time, item, and size, with default values of 5 minutes, 1000 items, and 1 GB, respectively.

To achieve near real-time ingestion, you can either customize the batch ingestion policy or enable the streaming ingestion policy.

Streaming ingestion must be enabled on the Azure Data Explorer cluster to work.

Enabling the streaming ingestion policy on the whole database instead of specific tables is a more efficient approach.

Querying data in Azure Data Explorer is a breeze. You can use the Azure Portal, Azure CLI, or client SDKs like Python to set up a cluster and database.

Once your data is ingested into the Storms table, you can verify that it's been successfully loaded by checking the row count. There should be no failures in the ingestion process.

To view all records in the Storms table, you can use a simple query. You can also use the where and project operators to filter specific data.

Azure Data Explorer uses the Kusto Query Language, which has a comprehensive documentation available for you to explore.

Azure Data Ingestion is a powerful tool for moving data from various sources to a centralized location. It's designed to handle large amounts of data and consists of built-in features like parallelism and time slicing.

Azure Data Factory has over 90 built-in connectors to access data from different sources, including Amazon Redshift, Google BigQuery, and Oracle Exadata. This makes it easy to collect data from disparate sources.

Data Ingestion has several benefits, including making data easily available, simplifying data transformation, and saving time. With no-code solutions like Hevo, data engineers can expedite the process and eliminate manual schema mapping.

Here are some key concepts to understand in Azure Data Ingestion:

Data Ingestion is a process that moves data from one or more sources to a destination for further processing and analysis. This can include bringing data from disparate sources like SaaS applications into a Data Lake or Data Warehouse.

Data Ingestion has several benefits, including making data easily available, simplifying data, and saving time. With no-code solutions, data engineers can perform Data Ingestion much faster than manually writing long commands.

Hevo's no-code platform streamlines data migration by allowing easy migration of different data types like CSV and JSON, with 150+ connectors including 60+ free sources. The auto-mapping feature eliminates the need for manual schema mapping.

In Azure Data Factory, Data Ingestion is composed of several key components, including connectors or linked services, pipelines, triggers, and activities. Connectors or linked services contain configuration settings for specific data sources.

Pipelines refer to logging groups of activities, and each pipeline can contain one or more activities. Triggers are used for scheduling configuration for pipelines, and activities are actions like data movement, transformations, or control flow actions.

Here's a breakdown of the components involved in Data Ingestion Azure Data Factory:

Azure Data Factory is a powerful tool that can handle large amounts of data, and it can transfer gigabytes of data in the cloud within a few hours.

It has more than 90 built-in connectors to access data from different sources, including Amazon Redshift, Google BigQuery, and Salesforce.

Managing data pipelines can be a challenge, but Azure Data Factory makes it easier by allowing you to monitor your data pipeline and set up alerts.

These alerts will notify you of any data pipeline problems and can be found in the Azure alerts group.

Azure Data Factory has built-in features like parallelism and time slicing that make it scalable and efficient.

Here are some of the key features of Azure Data Factory: